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Citations to Publications of
Dr. Carlos A. Coello Coello
that appear in the ISI Web of Science.
The total of citations (excluding self-citations and citations from his co-authors)
is 5327.
Tesis Doctoral
• Carlos A. Coello Coello. An Empirical Study of Evolutionary Techniques for Multiobjective Optimization
in Engineering Design, PhD thesis, Department of Computer Science, Tulane University, New Orleans,
Louisiana, April 1996.
1. Ayeley P. Tchangani, “Considering Bipolarity of Attributes With Regards to Objectives in Decisions Evaluation”,
Inzinerine Ekonomika–Engineering Economics, Vol. 21, No. 5, pp. 475–484, 2010.
2. K. Metaxiotis and K. Liagkouras, “Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive
literature review”, Expert Systems with Applications, Vol. 39, No. 14, pp. 11685–11698, October 15, 2012.
3. Musrrat. Ali, Patrick Siarry and Millie. Pant, “An efficient Differential Evolution based algorithm for solving multiobjective optimization problems”, European Journal of Operational Research, Vol. 217, No. 2, pp. 404–416, March 1,
2012.
4. Hamit Saruhan, “Pivoted-pad journal bearings lubrication design”, Industrial Lubrication and Tribology, Vol. 63, Nos.
2-3, pp. 119–126, 2011.
5. Mark A. Gammon, “Optimization of fishing vessels using a Multi-Objective Genetic Algorithm”, Ocean Engineering,
Vol. 38, No. 10, pp. 1054–1064, July 2011.
6. Cleber Zanchettin, Teresa B. Ludermir and Leandro Maciel Almeida, “Hybrid Training Method for MLP: Optimization
of Architecture and Training”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 41, No.
4, pp. 1097–1109, August 2011.
7. Min-Yuan Cheng and Ching-Shan Chen, “Optimal planning model for school buildings considering the tradeoff of seismic
resistance and cost effectiveness: a Taiwan case study”, Structural and Multidisciplinary Optimization, Vol. 43, No. 6,
pp. 863–879, June 2011.
8. Indika Meedeniya, Barbora Buhnova, Aldeida Aleti and Lars Grunske, “Reliability-driven deployment optimization for
embedded systems”, Journal of Systems and Software, Vol. 84, No. 5, pp. 835–846, May 2011.
9. S. Dhouib, A. Kharrat and H. Chabchoub, “Goal programming using multiple objective hybrid metaheuristic algorithm”,
Journal of the Operational Research Society, Vol. 62, No. 4, pp. 677–689, April 2011.
10. Souhail Dhouib, Aida Kharrat and Habib Chabchoub, “A multi-start threshold accepting algorithm for multiple objective
continuous optimization problems”, International Journal for Numerical Methods in Engineering, Vol. 83, No. 11, pp.
1498–1517, September 10, 2010.
11. C.A. Cortes, E. Mombello, R. Dib and G. Ratta, “A new class of flat-top windows for exposure assessment in magnetic
field measurements”, Signal Processing, Vol. 87, No. 9, pp. 2151–2164, September 2007.
12. Wahed Mohamed, Ibrahim Wesam and Effat Ahmed, “Finding an optimization of the plate element of Egyptian research
reactor using genetic algorithm”, Nuclear Science and Techniques, Vol. 19, No. 5, pp. 314–320, October 20, 2008.
13. Boguslaw Pytlak, “Multicriteria optimization of hard turning operation of the hardened 18HGT steel”, International
Journal of Advanced Manufacturing Technology, Vol. 49, Nos. 1–4, pp. 305–312, July 2010.
14. J. Dipama, A. Teyssedou, F. Aube and L. Lizon-A-Lugrin, “A grid based multi-objective evolutionary algorithm for the
optimization of power plants”, Applied Thermal Engineering, Vol. 30, Nos. 8-9, pp. 807–816, June 2010.
15. Zhi-Hua Hu, “A multiobjective immune algorithm based on a multiple-affinity model”, European Journal of Operational
Research, Vol. 202, No. 1, pp. 60–72, April 1, 2010.
16. Honglin Li, Hailei Zhang, Mingyue Zheng, Jie Luo, Ling Kang, Xiaofeng Liu, Xicheng Wang and Hualiang Jiang, “An
effective docking strategy for virtual screening based on multi-objective optimization algorithm”, BMC Bioinformatics,
Vol. 10, article number 58, February 11, 2009.
17. A. Albers, N. Leon-Rovira, H. Aguayo and T. Maier, “Development of an engine crankshaft in a framework of computeraided innovation”, Computers in Industry, Vol. 60, No. 8, pp. 604–612, October 2009.
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18. Mohamed El-Sayed Wahed, Wesam Zakaria Ibrahim and Ahmed Mostafa Effat, “Multiobjective Optimization of the
Plate Element of Egyptian Research Reactor Using Genetic Algorithm”, Nuclear Science and Engineering, Vol. 162, No.
3, pp. 275–281, July 2009.
19. Min-Rong Chen, Yong-zai Lu and Gen-ke Yang, “Multiobjective extremal optimization with applications to engineering
design”, Journal of Zhejiang University SCIENCE A, Vol. 8, No. 12, pp. 1905–1911, November 2007.
20. C. Elegbede, “Structural reliability assessment based on particles swarm optimization”, Structural Safety, Vol. 27, No.
2, pp. 171–186, 2005.
21. Adil Baykasoǧlu, “Preemptive goal programming using simulated annealing”, Engineering Optimization, Vol. 37, No. 1,
pp. 49–63, January 2005.
22. Guan-Chun Luh and Chung-Huei Chueh, “Multi-objective optimal design of truss structure with immune algorithm”,
Computers & Structures, Vol. 82, Nos. 11–12, pp. 829–844, May 2004.
23. J. Oh and C. Wu, “Genetic-algorithm-based real-time task scheduling with multiple goals”, Journal of Systems and
Software, Vol. 71, No. 3, pp. 245–258, May 2004.
24. C. Elegbede and K. Adjallah, “Availability allocation to repairable systems with genetic algorithms: a multi-objective
formulation”, Reliability Engineering & System Safety, Vol. 82, No. 3, pp. 319–330, December 2003.
25. Balram Suman, “Simulated Annealing-Based Multiobjective Algorithms and Their Application for System Reliability”,
Engineering Optimization, Vol. 35, No. 4, pp. 391–416, August 2003.
26. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical design with constraints and preferences: application
to a purge valve”, Computer Methods in Applied Mechanics and Engineering, Vol. 192, Nos. 39–40, pp. 4355–4378,
2003.
27. B. De Smedt and G.C.E. Gielen, “WATSON: Design space boundary exploration and model generation for analog and
RF IC design”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 22, No. 2, pp.
213–224, February 2003.
28. Johan Andersson and David Wallace, “Pareto optimization using the struggle genetic crowding algorithm”, Engineering
Optimization, Vol. 34, No. 6, pp. 623–643, December 2002.
29. Guan-Chun Luh, Chung-Huei Chueh and Wei-Wen Liu, “MOIA: Multi-Objective Immune Algorithm”, Engineering
Optimization, Volume 35, No. 2, pp. 143–164, April 2003.
30. K.C. Tan, E.F. Khor, T.H. Lee and Y.J. Yang, “A tabu-based exploratory evolutionary algorithm for multiobjective
optimization”, Artificial Intelligence Review, Vol. 19, No. 3, pp. 231–260, May 2003.
31. K.C. Tan, E.F. Khor, T.H. Lee and R. Sathikannan, “An evolutionary algorithm with advanced goal and priority
specification for multi-objective optimization”, Journal of Artificial Intelligence Research, Vol. 18, pp. 183–215, 2003.
32. K.C. Tan, T.H. Lee and E.F. Khor, “Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons”, Artificial Intelligence Review, Vol. 17, No. 4, pp. 253–290, June 2002.
33. K.C. Tan, T.H. Lee & E. F. Khor, “Evolutionary Algorithms with Dynamic Population Size and Local Exploration for
Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 5, No. 6, pp. 565-588, December
2001.
34. A. Baykasoglu, “Goal programming using multiple objective tabu search”, Journal of the Operational Research Society,
Vol. 52, No. 12, pp. 1359–1369, December 2001.
35. K.C. Tan, Tong H. Lee, D. Khoo & E.F. Khor, “A Multiobjective Evolutionary Algorithm Toolbox for Computer-Aided
Multiobjective Optimization”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 31,
No. 4, pp. 537–556, August 2001.
36. Johan Andersson and Peter Krus, “Multiobjective Optimization of Mixed Variable Design Problems”, en Eckart Zitzler,
Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First International Conference on
Evolutionary Multi-Criterion Optimization, Springer-Verlag, Zurich, Suiza, pp. 624–638, Marzo de 2001.
37. Matthias Ehrgott and Xavier Gandibleux, “A Survey and Annotated Bibliography of Multiobjective Combinatorial
Optimization”, OR Spektrum, Vol. 22, pp. 425–460, 2000.
38. B. Suman, “Study of self-stopping PDMOSA and performance measure in multiobjective optimization”, Computers &
Chemical Engineering, Vol. 29, No. 5, pp. 1131–1147, April 15, 2005.
39. J. Olvander, “Robustness considerations in multi-objective optimal design”, Journal of Engineering Design, Vol. 16, No.
5, pp. 511–523, October 2005.
40. M. Omran, A.P. Engelbrecht and A. Salman, “Particle swarm optimization method for image clustering”, International
Journal of Pattern Recognition and Artificial Intelligence, Vol. 19, No. 3, pp. 297–321, May 2005.
41. David Greiner, Gabriel Winter, José M. Emperador and Blas Galván, “Gray Coding in Evolutionary Multicriteria
Optimization: Application in Frame Structural Optimum Design”, in Carlos A. Coello Coello, Arturo Hernández Aguirre
and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005,
pp. 576–591, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
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42. Seyed Hamid Reza Pasandideh and Seyed Taghi Akhavan Niaki, “Multi-response simulation optimization using genetic
algorithm within desirability function framework”, Applied Mathematics and Computation, Vol. 175, No. 1, pp. 366–382,
April 1, 2006.
43. B. Suman and P. Kumar, “A survey of simulated annealing as a tool for single and multiobjective optimization”, Journal
of the Operational Research Society, Vol. 57, No. 10, pp. 1143–1160, October 2006.
Libros
• Carlos A. Coello Coello, David A. Van Veldhuizen and Gary B. Lamont, “Evolutionary Algorithms for Solving
Multi-Objective Problems”, Kluwer Academic Publishers, New York, USA, ISBN 0-3064-6762-3, May 2002.
o Carlos A. Coello Coello, Gary B. Lamont and David A. Van Veldhuizen, “Evolutionary Algorithms for
Solving Multi-Objective Problems”, Second Edition, Springer-Verlag, New York, USA, Septiembre 2007,
ISBN 978-0-387-33254-3.
1. Javier Rubio-Loyola, Gregorio Toscano-Pulido, Marinos Charalambides, Marisol Magana-Aguilar, Joan Serrat-Fernandez,
George Pavlou and Hiram Galeana-Zapien, “Business-driven policy optimization for service management”, International
Journal of Network Management, Vol. 25, No. 2, pp. 113–140, March-April 2015.
2. Alan R.R. de Freitas, Peter J. Fleming and Federico G. Guimaraes, “Aggregation Trees for visualization and dimension
reduction in many-objective optimization”, Information Sciences, Vol. 298, pp. 288–314, March 20, 2015.
3. Ana Belen Ruiz, Ruben Saborido and Mariano Luque, “A preference-based evolutionary algorithm for multiobjective
optimization: the weighting achievement scalarizing function genetic algorithm”, Journal of Global Optimization, Vol.
62, No. 1, pp. 101–129, May 2015.
4. Rui Wang, Robin C. Purshouse, Ioannis Giagkiozis and Peter J. Fleming, “The iPICEA-g: a new hybrid evolutionary
multi-criteria decision making approach using the brushing technique”, European Journal of Operational Research, Vol.
243, No. 2, pp. 442–453, June 1, 2015.
5. Xiaoguang He, Cai Dai and Zehua Chen, “Many-Objective Optimization Using Adaptive Differential Evolution with a
New Ranking Method”, Mathematical Problems in Engineering, Article Number: 259473, 2014.
6. Cai Dai, Yuping Wang and Miao Ye, “A new evolutionary algorithm based on contraction method for many-objective
optimization problems”, Applied Mathematics and Computation, Vol. 245, pp. 191–205, October 15, 2014.
7. A. Kaveh and K. Laknejadi, “A new multi-swarm multi-objective optimization method for structural design”, Advances
in Engineering Software, Vol. 58, pp. 54–69, April 2013.
8. Sanghamitra Bandyopadhyay, Rudrasis Chakraborty and Ujjwal Maulik, “Priority based epsilon dominance: A new
measure in multiobjective optimization”, Information Sciences, Vol. 305, pp. 97–109, June 1, 2015.
9. Ernestas Filatovas, Olga Kurasova and Karthik Sendhya, “Synchronous R-NSGA-II: An Extended Preference-Based
Evolutionary Algorithm for Multi-Objective Optimization”, Informatica, Vol. 26, No. 1, pp. 33–50, 2015.
10. Proteek Chandan Roy, Md. Monirul Islam, Kazuyuki Murase and Xin Yao, “Evolutionary Path Control Strategy for
Solving Many-Objective Optimization Problem”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 702–715, April
2015.
11. Bili Chen, Wenhua Zeng, Yangbin Lin and Defu Zhang, “A New Local Search-Based Multiobjective Optimization
Algorithm”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 50–73, February 2015.
12. Hossein Rajabalipour Cheshmehgaz, Mohammad Ishak Desa and Antoni Wibowo, “Effective local evolutionary searches
distributed on an island model solving bi-objective optimization problems”, Applied Intelligence, Vol. 38, No. 3, pp.
331–356, April 2013.
13. Bernabe Dorronsoro, Gregoire Danoy, Antonio J. Nebro and Pascal Bouvry, “Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution”, Computers & Operations Research,
Vol. 40, No. 6, pp. 1552–1563, June 2013.
14. Mohammad Mortazavi-Naeini, George Kuczera and Lijie Cui, “Efficient multi-objective optimization methods for computationally intensive urban water resources models”, Journal of Hydroinformatics, Vol. 17, No. 1, pp. 36–55, 2015.
15. Jürgen Branke, Salvatore Greco, Roman Slowinski and Piotr Zielniewicz, “Learning Value Functions in Interactive
Evolutionary Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp.
88–102, February 2015.
16. Yong Zhang, Dun-Wei Gong and Na Gong, “Multi-Objective Optimization Problems Using Cooperative Evolvement
Particle Swarm Optimizer”, Journal of Computational and Theoretical Nanoscience, Vol. 10, No. 3, pp. 655-663, March
2013.
17. Matthew P. Ferringer, Ronald S. Clifton and Timothy G. Thompson, “Efficient and accurate evolutionary multi-objective
optimization paradigms for satellite constellation design”, Journal of Spacecraft and Rockets, Vol. 44, No. 3, pp. 682–691,
May-June 2007.
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18. Han-Young Park, Akhil Datta-Gupta and Michael J. King, “Handling conflicting multiple objectives using Pareto-based
evolutionary algorithm during history matching of reservoir performance”, Journal of Petroleum Science and Engineering,
Vol. 125, pp. 48–66, January 2015.
19. Jianhua Xiao, Jin Xu, Xiutang Geng and Linqiang Pan, “Multi-objective carrier chaotic evolutionary algorithm for DNA
sequences design”, Progress in Natural Science, Vol. 17, No. 12, pp. 1515–1520, December 2007.
20. Juan Rada-Vilela, Manuel Chica, Oscar Cordon and Sergio Damas, “A comparative study of Multi-Objective Ant Colony
Optimization algorithms for the Time and Space Assembly Line Balancing Problem”, Applied Soft Computing, Vol. 13,
No. 11, pp. 4370–4382, November 2013.
21. Jinn-Tsong Tsai, Ching-I. Yang and Jyh-Horng Chou, “Hybrid sliding level Taguchi-based particle swarm optimization
for flowshop scheduling problems”, Applied Soft Computing, Vol. 15, pp. 177–192, February 2014.
22. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionary
searches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,
March 2015.
23. Jonathan E. Fieldsend and Richard M. Everson, “The Rolling Tide Evolutionary Algorithm: A Multiobjective Optimizer
for Noisy Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 103–117,
February 2015.
24. Gideon Avigad, Alex Goldvard and Shaul Salomon, “Time-response-based evolutionary optimization”, Engineering
Optimization, Vol. 47, No. 4, pp. 533–549, April 3, 2015.
25. Prakash Shelokar, Arnaud Quirin and Oscar Cordon, “Three-objective subgraph mining using multiobjective evolutionary
programming”, Journal of Computer and System Sciences, Vol. 80, No. 1, pp. 16–26, February 2014.
26. Miguel Porto, Otilia Correia and Pedro Beja, “Optimization of Landscape Services under Uncoordinated Management
by Multiple Landowners”, Plos One, Vol. 9, No. 1, Article Number: e86001, January 17, 2014.
27. Xue-Song Yang, Bing-Zhong Wang, Sai Ho Yeung, Quan Xue and Kim Fung Man, “Circularly Polarized Reconfigurable
Crossed-Vagi Patch Antenna”, IEEE Antennas and Propagation Magazine, Vol. 53, No. 5, pp. 65–80, October 2011.
28. Ming Zhai, Changyu Shen, Chuntai Liu and Jingbo Chen, “Optimization of runner sizes and process conditions considering both part quality and manufacturing cost in injecting molding”, Journal of Polymer Engineering, Vol. 31, Nos.
6-7, pp. 489–494, November 2011.
29. Wesley Klewerton Guez Assuncao, Thelma Elita Colanzi, Silvia Regina Vergilio and Aurora Pozo, “A multi-objective
optimization approach for the integration and test order problem”, Information Sciences, Vol. 267, pp. 119–139, May
20, 2014.
30. Krishnaswamy Hariharan, Nirupam Chakraborti, Frederic Barlat and Myoung-Gyu Lee, “A Novel Multi-objective Genetic Algorithms-Based Calculation of Hill’s Coefficients”, Metallurgical and Materials Transactions A–Physical Metallurgy and Materials Science, Vol. 45A, No. 6, pp. 2704–2707, June 2014.
31. Miqing Li, Shengxiang Yang, Jinhua Zheng and Xiaohui Liu, “ETEA: A Euclidean Minimum Spanning Tree-Based
Evolutionary Algorithm for Multi-Objective Optimization”, Evolutionary Computation, Vol. 22, No. 2, pp. 189–230,
Summer 2014.
32. Khairy Elsayed and Chris Lacor, “ Robust parameter design optimization using Kriging, RBF and RBFNN with gradientbased and evolutionary optimization techniques”, Applied Mathematics and Computation, Vol. 236, pp. 325–344, June
1, 2014.
33. Lianbo Ma, Kunyuan Hu, Yunlong Zhu and Hanning Chen, “Cooperative artificial bee colony algorithm for multiobjective RFID network planning”, Journal of Network and Computer Applications, Vol. 42, pp. 143–162, June 2014.
34. Ehsan Gholamalizadeh and Man-Hoe Kim, “Thermo-economic triple-objective optimization of a solar chimney power
plant using genetic algorithms”, Energy, Vol. 70, pp. 204–211, June 1, 2014.
35. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-Objective
Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.
36. Huseyin Onur Mete and Zelda B. Zabinsky, “Multiobjective Interacting Particle Algorithm for Global Optimization”,
INFORMS Journal on Computing, Vol. 26, No. 3, pp. 500–513, Summer 2014.
37. Kostas Florios and George Mavrotas, “Generation of the exact Pareto set in Multi-Objective Traveling Salesman and
Set Covering Problems”, Applied Mathematics and Computation, Vol. 237, pp. 1–19, June 15, 2014.
38. Enze Zhang, Yifei Wu and Qingwei Chen, “ A practical approach for solving multi-objective reliability redundancy
allocation problems using extended bare-bones particle swarm optimization”, Reliability Engineering & System Safety,
Vol. 127, pp. 65–76, July 2014.
39. S. Sinaie, A. Heidarpour and X.L. Zhao, “A multi-objective optimization approach to the parameter determination of
constitutive plasticity models for the simulation of multi-phase load histories”, Computers & Structures, Vol. 138, pp.
112–132, July 1, 2014.
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40. Jose D. Martinez-Morales, Elvia R. Palacios-Hernandez and Gerardo A. Velazquez-Carrillo, “Artificial neural network
based on genetic algorithm for emissions prediction of a SI gasoline engine”, Journal of Mechanical Science and Technology, Vol. 28, No. 6, pp. 2417–2427, June 2014.
41. Yangyang Li, Xia Xu, Peidao Li and Licheng Jiao, “Improved RM-MEDA with local learning”, Soft Computing, Vol.
18, No. 7, pp. 1383–1397, July 2014.
42. I. Montalvo, J. Izquierdo, R. Perez-Garcia and M. Herrera, “Water Distribution System Computer-Aided Design by
Agent Swarm Optimization”, Computer-Aided Civil and Infrastructure Engineering, Vol. 29, No. 6, pp. 433–448, July
2014.
43. You-Jin Park, Rong Pan, Connie M. Borror, Douglas C. Montgomery and Gyu-Bong Lee, “Simultaneous Improvement
of Energy Efficiency and Product Quality in PCB Lamination Process”, International Journal of Precision Engineering
and Manufacturing–Green Technology, Vol. 1, No. 3, pp. 247–256, July 2014.
44. Weijian Kong, Tianyou Chai, Jinliang Ding and Shengxiang Yang, “Multifurnace Optimization in Electric Smelting
Plants by Load Scheduling and Control”, IEEE Transactions on Automation Science and Engineering, Vol. 11, No. 3,
pp. 850–862, July 2014.
45. Kamal Boudjelaba, Frederic Ros and Djamel Chikouche, “Adaptive genetic algorithm-based approach to improve the
synthesis of two-dimensional finite impulse response filters”, IET Signal Processing, Vol. 8, No. 5, pp. 429–446, July
2014.
46. Michele Amoretti, “Evolutionary strategies for ultra-large-scale autonomic systems”, Information Sciences, Vol. 274,
pp. 1–16, August 1, 2014.
47. Julien Schleich, Gregoire Danoy, Bernabe Dorronsoro and Pascal Bouvry, “Optimising small-world properties in VANETs:
Centralised and distributed overlay approaches”, Applied Soft Computing, Vol. 21, pp. 637–646, August 2014.
48. Danial S. Mohammadzadeh, Jafar Bolouri Bazaz and Amir H. Alavi, “An evolutionary computational approach for
formulation of compression index of fine-grained soils”, Engineering Applications of Artificial Intelligence, Vol. 33, pp.
58–68, August 2014.
49. Masoud Sharafi and Tarek Y. ELMekkawy, “Multi-objective optimal design of hybrid renewable energy systems using
PSO-simulation based approach”, Renewable Energy, Vol. 68, pp. 67–79, August 2014.
50. Mehmet Unal and Gordon P. Warn, “Optimal cost-effective topology of column bearings for reducing vertical acceleration
demands in multistory base-isolated buildings”, Earthquake Engineering & Structural Dynamics, Vol. 43, No. 8, pp.
1107–1127, July 10, 2014.
51. Zulkifli Mohamed, Mitsuki Kitani, Shin-ichiro Kaneko and Genci Capi, “Humanoid robot arm performance optimization
using multi objective evolutionary algorithm”, International Journal of Control Automation and Systems, Vol. 12, No.
4, pp. 870–877, August 2014.
52. David Gonzalez, Mario Garcia-Lozano, Silvia Ruiz and Dong Seop Lee, “A metaheuristic-based downlink power allocation
for LTE/LTE-A cellular deployments”, Wireless Networks, Vol. 20, No. 6, pp. 1369–1386, August 2014.
53. Himanshu Jain and Kalyanmoy Deb, “An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point
Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach”, IEEE
Transactions on Evolutionary Computation, Vol. 18, No. 4, pp. 602–622, August 2014.
54. Sepehr Sanaye and Navid Khakpaay, “Simultaneous use of MRM (maximum rectangle method) and optimization methods
in determining nominal capacity of gas engines in CCHP (combined cooling, heating and power) systems”, Energy, Vol.
72, pp. 145–158, August 1, 2014.
55. Vijay Rathod, Om Prakash Yadav, Ajay Rathore and Rakesh Jain, “Optimizing reliability-based robust design model
using multi-objective genetic algorithm”, Computers & Industrial Engineering, Vol. 66, No. 2, pp. 301–310, October
2013.
56. Jie Tang, Daniel K.C. So, Emad Alsusa and Khairi Ashour Hamdi, “Resource Efficiency: A New Paradigm on Energy
Efficiency and Spectral Efficiency Tradeoff”, IEEE Transactions on Wireless Communications, Vol. 13, No. 8, pp.
4656–4669, August 2014.
57. Brian J. Ross, “The evolution of higher-level biochemical reaction models”, Genetic Programming and Evolvable Machines, Vol. 13, No. 1, pp. 3–31, March 2012.
58. S. Sharma, G.P. Rangaiah and K.S. Cheah, “Multi-objective optimization using MS Excel with an application to design
of a falling-film evaporator system”, Food and Bioproducts Processing, Vol. 90, No. C2, pp. 123–134, April 2012.
59. G. Ridolfi, E. Mooij, D. Cardile, S. Corpino and G. Ferrari, “A methodology for system-of-systems design in support of
the engineering team”, Acta Astronautica, Vol. 73, pp. 88–99, April-May 2012.
60. Chun-Hao Chen, Tzung-Pei Hong and Vincent S. Tseng, “Finding Pareto-front Membership Functions in Fuzzy Data
Mining”, International Journal of Computational Intelligence Systems, Vol. 5, No. 2, pp. 343–354, April 2012.
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61. Antonio A. Marquez, Francisco A. Marquez and Antonio Peregrin, “A Mechanism to Improve the Interpretability of
Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a Multi-objective Evolutionary Algorithm”,
International Journal of Computational Intelligence Systems, Vol. 5, No. 2, pp. 297–321, April 2012.
62. Laszlo Daroczy, Gabor Janiga and Dominique Thevenin, “Systematic analysis of the heat exchanger arrangement problem
using multi-objective genetic optimization”, Energy, Vol. 65, pp. 364–373, February 1, 2014.
63. Dongdong Yang, Licheng Jiao, Ruican Niu and Maoguo Gong, “Investigation of Combinational Clustering Indices in
Artificial Immune Multi-Objective Clustering”, Computational Intelligence, Vol. 30, No. 1, pp. 115–144, February 2014.
64. Manojkumar Ramteke and Santosh K. Gupta, “Biomimetic Adaptations of GA and SA for the Robust MO Optimization
of an Industrial Nylon-6 Reactor”, Materials and Manufacturing Processes, Vol. 24, No. 1, pp. 38–46, Article Number:
PII 906599196, 2009.
65. Mohammad H. Kurdi, Tony L. Schmitz, Raphael T. Haftka and Brian P. Mann, “Milling optimisation of removal rate
and accuracy with uncertainty: Part 1: parameter selection”, International Journal of Materials & Product Technology,
Vol. 35, Nos. 1-2, pp. 3–25, 2009.
66. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
67. Mei-Po Kwan, Ningchuan Xiao and Guoxiang Ding, “Assessing Activity Pattern Similarity with Multidimensional Sequence Alignment Based on a Multiobjective Optimization Evolutionary Algorithm”, Geographical Analysis, Vol. 46,
No. 3, pp. 297–320, July 2014.
68. Carlos R. Garcia-Alonso, Leonor M. Perez-Naranjo and Juan C. Fernandez-Caballero, “Multiobjective evolutionary
algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms”,
Annals of Operations Research, Vol. 219, No. 1, pp. 187–202, August 2014.
69. Francesco Folino and Clara Pizzuti, “An Evolutionary Multiobjective Approach for Community Discovery in Dynamic
Networks”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 8, pp. 1838–1852, August 2014.
70. Joseph R. Kasprzyk, Patrick M. Reed, Gregory W. Characklis and Brian R. Kirsch, “Many-objective de Novo water
supply portfolio planning under deep uncertainty”, Environmental Modelling & Software, Vol. 34, pp. 87–104, June
2012.
71. Joseph R. Kasprzyk, Shanti Nataraj, Patrick M. Reed and Robert J. Lempert, “Many objective robust decision making
for complex environmental systems undergoing change”, Environmental Modelling & Software, Vol. 42, pp. 55–71, April
2013.
72. Hideki Katagiri, Ichiro Nishizaki, Tomohiro Hayashida and Takanori Kadoma, “Multiobjective Evolutionary Optimization of Training and Topology of Recurrent Neural Networks for Time-Series Prediction”, Computer Journal, Vol. 55,
No. 3, pp. 325–336, March 2012.
73. Kamyab Tahernezhad, Bazargan Lari, Ali Hamzeh and Sattar Hashemi, “HC-MOEA: A hierarchical clustering approach
for increasing the solution’s diversity in multiobjective evolutionary algorithms”, Intelligent Data Analysis, Vol. 19, No.
1, pp. 187–208, 2015.
74. Xiao Liang, Lihua Yue, Yan Xiong, Wenjuan Cheng and Sichen Liu, “On the Analysis of Evolutionary Programming
with Self-adaptive Cauchy Operation”, Chinese Journal of Electronics, Vol. 21, No. 2, pp. 309–312, April 2012.
75. Mariano Frutos and Fernando Tohme, “Evolutionary Multi-Objective Scheduling Procedures in Non-Standardized Production Processes”, DYNA-Colombia, Vol. 79, No. 172, pp. 101–107, April 2012.
76. Masatoshi Sakawa, Hideki Katagiri and Takeshi Matsui, “Interactive fuzzy stochastic two-level integer programming
through fractile criterion optimization”, Operational Research, Vol. 12, No. 2, pp. 209–227, August 2012.
77. Saeb M. Besarati and D. Yogi Goswami, “A computationally efficient method for the design of the heliostat field for
solar power tower plant”, Renewable Energy, Vol. 69, pp. 226–232, September 2014.
78. Eduardo Lupiani, Jose M. Juarez and Jose Palma, “Evaluating Case-Base Maintenance algorithms”, Knowledge-Based
Systems, Vol. 67, pp. 180–194, September 2014.
79. Singiresu S. Rao, Hoe-Gil Lee and Yi Hu, “Optimal Design of Compound Parabolic Concentrator Solar Collector System”,
Journal of Mechanical Design, Vol. 136, No. 9, Article Number: 091402, September 2014.
80. Mojtaba Shivaie, Ahmad Salemnia and Mohammad T. Ameli, “A multi-objective approach to optimal placement and
sizing of multiple active power filters using a music-inspired algorithm”, Applied Soft Computing, Vol. 22, pp. 189–204,
September 2014.
81. I. Kaliszewski and J. Miroforidis, “Two-Sided Pareto Front Approximations”, Journal of Optimization Theory and
Applications, Vol. 162, No. 3, pp. 845–855, September 2014.
82. M.J. Gacto, M. Galende, R. Alcala and F. Herrera, “METSK-HDe: A multiobjective evolutionary algorithm to learn
accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems”, Information Sciences, Vol. 276,
pp. 63–79, August 20, 2014.
6
83. Claudio Comis Da Ronco, Rita Ponza and Ernesto Benini, “Aerodynamic Shape Optimization in Aeronautics: A Fast
and Effective Multi-Objective Approach”, Archives of Computational Methods in Engineering, Vol. 21, No. 3, pp.
189–271, September 2014.
84. V.E. Berezkin and A.V. Lotov, “Comparison of two Pareto frontier approximations”, Computational Mathematics and
Mathematical Physics, Vol. 54, No. 9, pp. 1402–1410, September 2014.
85. Felipe Baesler and Cristian Palma, “Multiobjective parallel machine scheduling in the sawmill industry using memetic
algorithms”, International Journal of Advanced Manufacturing Technology, Vol. 74, Nos. 5-8, pp. 757–768, September
2014.
86. Tian Liang, Wei Heng, Chao Meng and Guodong Zhang, “Cooperative Power Allocation Based on Multi-Objective
Intelligent Optimization for Multi-Source Multi-Relay Networks”, IEICE Transactions on Communications, Vol. E97B,
pp. 1938–1946, September 2014.
87. Amal Kant, Pranmohan K. Suman, Brijesh K. Giri, Mukesh K. Tiwari, Chandranath Chatterjee, Purna C. Nayak
and Sawan Kumar, “Comparison of multi-objective evolutionary neural network, adaptive neuro-fuzzy inference system
and bootstrap-based neural network for flood forecasting”, Neural Computing & Applications, Vol. 23, pp. S231-S246,
Supplement 1, December 2013.
88. Nirupam Chakraborti, “Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic
algorithms in materials research”, Materials Science and Technology, Vol. 30, No. 11, pp. 1259–1262, September 2014.
89. Giacomo F. Porzio, Valentina Colla, Nicola Matarese, Gianluca Nastasi, Teresa A. Branca, Alessandro Amato, Barbara
Fornai, Marco Vannucci and Massimo Bergamasco, “Process integration in energy and carbon intensive industries: An
example of exploitation of optimization techniques and decision support”, Applied thermal Engineering, Vol. 70, No. 2,
pp. 1148–1155, September 22, 2014.
90. Christopher Priester, Sebastian Schmitt and Tiago P. Peixoto, “Limits and Trade-Offs of Topological Network Robustness”, Plos One, Vol. 9, No. 9, Article Number: e108215, September 24, 2014.
91. Yashar Hashemi and Khalil Valipour, “FDM based multi-objective optimal sitting and design of TC-FLSFCL for study
of distribution system reliability”, International Journal of Electrical Power & Energy Systems, Vol. 61, pp. 463–473,
October 2014.
92. P. Sharafi, Lip H. Teh and Muhammad N.S. Hadi, “Shape optimization of thin-walled steel sections using graph theory
and ACO algorithm”, Journal of Constructional Steel Research, Vol. 101, pp. 331–341, October 2014.
93. M. Frutos, M. Mendez, F. Tohme and D. Broz, “Comparison of Multiobjective Evolutionary Algorithms for Operations
Scheduling under Machine Availability Constraints”, Scientific World Journal, Article Number: 418396, 2013.
94. Steve O’Hagan, Joshua Knowles and Douglas B. Kell, “Exploiting Genomic Knowledge in Optimising Molecular Breeding
Programmes: Algorithms from Evolutionary Computing”, Plos One, Vol. 7, No. 11, Article Number: e48862, November
21, 2012.
95. Y. Zhang, A.L. Collins and R.D. Gooday, “Application of the FARMSCOPER tool for assessing agricultural diffuse
pollution mitigation methods across the Hampshire Avon Demonstration Test Catchment, UK”, Environmental Science
& Policy, Vol. 24, pp. 120–131, December 2012.
96. Eduardo Fernandez and Gonzalo Besuievsky, “Inverse lighting design for interior buildings integrating natural and
artificial sources”, Computers & Graphics-UK, Vol. 36, No. 8, pp. 1096–1108, December 2012.
97. Hilary E. Brown, Siddharth Suryanarayanan, Sudarshan A. Natarajan and Sanjay Rajopadhye, “Improving Reliability
of Islanded Distribution Systems With Distributed Renewable Energy Resources”, IEEE Transactions on Smart Grid,
Vol. 3, No. 4, pp. 2028–2038, December 2012.
98. Nicolas Jozefowiez, Gilbert Laporte and Frederic Semet, “A Generic Branch-and-Cut Algorithm for Multiobjective
Optimization Problems: Application to the Multilabel Traveling Salesman Problem”, INFORMS Journal on Computing,
Vol. 24, No. 4, pp. 554–564, Fall 2012.
99. Andrea Cynthia Santos, Diego Rocha Lima and Dario Jose Aloise, “Modeling and solving the bi-objective minimum
diameter-cost spanning tree problem”, Journal of Global Optimization, Vol. 60, No. 2, pp. 195–216, October 2014.
100. Gustavo R. Zavala, Antonio J. Nebro, Juan J. Durillo and Francisco Luna, “Integrating a multi-objective optimization
framework into a structural design software”, Advances in Engineering Software, Vol. 76, pp. 161–170, October 2014.
101. Victor Berrocal-Plaza, Miguel A. Vega-Rodriguez and Juan M. Sanchez-Perez, “Solving the location areas management
problem with multi-objective evolutionary strategies”, Wireless Networks, Vol. 20, No. 7, pp. 1909–1924, October 2014.
102. Romain Perriot, Jeremy Pfeifer, Laurent d’Orazio, Bruno Bachelet, Sandro Bimonte and Jerom Darmont, “Cost Models
for Selecting Materialized Views in Public Clouds”, International Journal of Data Warehousing and Mining, Vol. 10,
No. 4, pp. 1–25, October-December 2014.
103. Francisco Manzano-Agugliaro, Francisco G. Montoya, Carlos San-Antonio-Gomez, Sergio Lopez-Marquez, Maria J.
Aguilera and Consolacion Gil, “The assessment of evolutionary algorithms for analyzing the positional accuracy and
uncertainty of maps”, Expert Systems with Applications, Vol. 41, No. 14, pp. 6346–6360, October 15, 2014.
7
104. Danilo Sipoli Sanches, Joao Bosco A. London, Jr., Alexandre Claudio B. Delbem, Ricardo S. Prado, Federico G.
Guimaraes, Oriane M. Neto and Telma W. de Lima, “Multiobjective evolutionary algorithm with a discrete differential mutation operator developed for service restoration in distribution systems”, International Journal of Electrical
Power & Energy Systems, Vol. 62, pp. 700–711, November 2014.
105. Francisco G. Montoya, Francisco Manzano-Agugliaro, Sergio Lopez-Marquez, Quetzalcoatl Hernandez-Escobedo and
Consolacion Gil, “Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms”, Expert
Systems with Applications, Vol. 41, No. 15, pp. 6585–6595, November 1, 2014.
106. Hanieh Borhanazad, Saad Mekhilef, Velappa Gounder Ganapathy, Mostafa Modiri-Delshad and Ali Mirtaheri, “Optimization of micro-grid system using MOPSO”, Renewable Energy, Vol. 71, pp. 295–306, November 2014.
107. Michela Fazzolari, Rafael Alcala and Francisco Herrera, “A multi-objective evolutionary method for learning granularities
based on fuzzy discretization to improve the accuracy-complexity trade-off of fuzzy rule-based classification systems: DMOFARC algorithm”, Applied Soft Computing, Vol. 24, pp. 470–481, November 2014.
108. Wassim Ayadi and Jin-Kao Hao, “A memetic algorithm for discovering negative correlation biclusters of DNA microarray
data”, Neurocomputing, Vol. 145, pp. 14–22, December 5, 2014.
109. Bilel Derbel, Jeremie Humeauc, Arnaud Liefooghe and Sebastien Verel, “Distributed localized bi-objective search”,
European Journal of Operational Research, Vol. 239, No. 3, pp. 731–743, December 16, 2014.
110. Ke Li and Sam Kwong, “A general framework for evolutionary multiobjective optimization via manifold learning”,
Neurocomputing, Vol. 146, pp. 65–74, December 25, 2014.
111. Belen Melian-Batista, Alondra De Santiago, Francisco AngelBello and Ada Alvarez, “A bi-objective vehicle routing
problem with time windows: A real case in Tenerife”, Applied Soft Computing, Vol. 17, pp. 140–152, April 2014.
112. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Sergio Garcia-Nieto, “Physical programming for preference
driven evolutionary multi-objective optimization”, Applied Soft Computing, Vol. 24, pp. 341–362, November 2014.
113. Kenneth Sorensen and Johan Springael, “Progressive Multi-Objective Optimization”, International Journal of Information Technology & Decision Making, Vol. 13, No. 5, pp. 917–936, September 2014.
114. Christian von Lücken, Benjamı́n Barán and Carlos Brizuela, “A survey on multi-objective evolutionary algorithms for
many-objective problems”, Computational Optimization and Applications, Vol. 58, No. 3, pp. 707–756, July 2004.
115. M.J. Mahmoodabadi, M. Taherkhorsandi and A. Bagheri, “Pareto Design of State Feedback Tracking Control of a
Biped Robot via Multiobjective PSO in Comparison with Sigma Method and Genetic Algorithms: Modified NSGAII
and MATLAB’s Toolbox”, Scientific World Journal, Article Number: 303101, 2014.
116. Christopher Smith and Yaochu Jin, “Evolutionary multi-objective generation of recurrent neural network ensembles for
time series prediction”, Neurocomputing, Vol. 143, pp. 302–311, November 2, 2014.
117. Bernardo Severino, Felipe Gana, Rodrigo Palma-Behnke, Pablo A. Estevez, Williams R. Calderon-Munoz, Marcos E.
Orchard, Jorge Reyes and Marcelo Cortes, “Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms”, Journal of Power Sources, Vol. 267, pp. 288–299, December 1, 2014.
118. Hossein Karshenas, Roberto Santana, Concha Bielza and Pedro Larrañaga, “Multiobjective Estimation of Distribution
Algorithm Based on Joint Modeling of Objectives and Variables”, IEEE Transactions on Evolutionary Computation,
Vol. 18, No. 4, pp. 519–542, August 2014.
119. Francisco Luna, David L. Gonzalez-Alvarez, Francisco Chicano and Miguel A. Vega-Rodriguez, “The software project
scheduling problem: A scalability analysis of multi-objective metaheuristics”, Applied Soft Computing, Vol. 15, pp.
136–148, February 2014.
120. L.F. Gonzalez, D.S. Lee, K. Srinivas and K.C. Wong, “Single and multi-objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm”, Aeronautical Journal, Vol. 110, No. 1112, pp. 659–672, October
2006.
121. Gift Dumedah and Paulin Coulibaly, “Integration of an evolutionary algorithm into the ensemble Kalman filter and the
particle filter for hydrologic data assimilation”, Journal of Hydroinformatics, Vol. 16, No. 1, pp. 79–94, 2014.
122. Emrah Demir, Tolga Bektas and Gilbert Laporte, “The bi-objective Pollution-Routing Problem”, European Journal of
Operational Research, Vol. 232, No. 3, pp. 464–478, February 1, 2014.
123. Krzysztof Trawinski, Oscar Cordon, Arnaud Quirin and Luciano Sanchez, “Multiobjective genetic classifier selection
for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity?”, Knowledge-Based
Systems, Vol. 54, pp. 3–21, December 2013.
124. Mohammad Reza Ghasemi and Mohammad Farshchin, “Pareto-based optimum seismic design of steel frames”, Proceedings of the Institution of Civil Engineers–Structures and Buildings, Vol. 167, No. 1, pp. 66–74, January 2014.
125. B. Zhou, K.W. Chan, T. Yu and C.Y. Chung, “Equilibrium-Inspired Multiple Group Search Optimization With Synergistic Learning for Multiobjective Electric Power Dispatch”, IEEE Transactions on Power Systems, Vol. 28, No. 4, pp.
3534–3545, November 2013.
8
126. Mashael Maashi, Ender Özcan and Graham Kendall, “A multi-objective hyper-heuristic based on choice function”,
Expert Systems with Applications, Vol. 41, No. 9, pp. 4475–4493, July 2014.
127. Bahriye Akay, “Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms”, Journal
of Global Optimization, Vol. 57, No. 2, pp. 415–445, October 2013.
128. Hanning Chen, Ma Lian Bo, Yunlong Zhu, “Multi-hive bee foraging algorithm for multi-objective optimal power flow
considering the cost, loss, and emission”, International Journal of Electrical Power & Energy Systems, Vol. 60, pp.
203–220, September 2014.
129. A.L. Marquez, C. Gil, R. Banos and J. Gomez, “Parallelism on multicore processors using Parallel.FX”, Advances in
Engineering Software, Vol. 42, No. 5, pp. 259–265, May 2011.
130. Anthony P. Hurford, Tuana Huskova and Julien J. Harou, “Using many-objective trade-off analysis to help dams promote
economic development, protect the poor and enhance ecological health”, Environmental Science & Policy, Vol. 38, pp.
72–86, April 2014.
131. Abdelkader Boukhobza, Abdennacer Bounoua, Abdelmalik Taleb-Ahmed and Nasreddine Taleb, “Design of Biorthogonal
Filter Banks Using a Multi-objective Genetic Algorithm for an Image Coding Scheme”, Circuits Systems and Signal
Processing, Vol. 32, No. 4, pp. 1725–1744, August 2013.
132. Masoud Esmaili, “Placement of minimum distributed generation units observing power losses and voltage stability with
network constraints”, IET Generation Transmission & Distribution, Vol. 7, No. 8, pp. 813–821, August 2013.
133. Suha Orcun Mert and Zehra Ozcelik, “Multi-objective optimization of a direct methanol fuel cell system using a geneticbased algorithm”, International Journal of Energy Research, Vol. 37, No. 10, pp. 1256–1264, August 2013.
134. Christian Grimme, Joachim Lepping and Uwe Schwiegelshohn, “Multi-criteria scheduling: an agent-based approach for
expert knowledge integration”, Journal of Scheduling, Vol. 16, No. 4, pp. 369–383, August 2013.
135. Somayeh Toghyani, Alibakhsh Kasaeian and Mohammad H. Ahmadi, “Multi-objective optimization of Stirling engine
using non-ideal adiabatic method”, Energy Conversion and Management, Vol. 80, pp. 54–62, April 2014.
136. Gift Dumedah, Aaron A. Berg and Mark Wineberg, “Evaluating Autoselection Methods Used for Choosing Solutions
from Pareto-Optimal Set: Does Nondominance Persist from Calibration to Validation Phase?”, Journal of Hydrologic
Engineering, Vol. 17, No. 1, pp. 150–159, January 2012.
137. Mohammad Hossein Zangooei, Jafar Habibi and Roohallah Alizadehsani, “Disease Diagnosis with a hybrid method SVR
using NSGA-II”, Neurocomputing, Vol. 136, pp. 14–29, July 20, 2014.
138. Piyush Bhardwaj, Bhaskar Dasgupta and Kalyanmoy Deb, “Modelling the Pareto-optimal set using B-spline basis
functions for continuous multi-objective optimization problems”, Engineering Optimization, Vol. 46, No. 7, pp. 912–
938, July 3, 2014.
139. Jose M. Chaves-Gonzalez, Miguel A. Vega-Rodriguez and Jose M. Granado-Criado, “A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design”, Engineering Applications of Artificial
Intelligence, Vol. 26, No. 9, pp. 2045–2057, October 2013.
140. Jose M. Chaves-Gonzalez and Miguel A. Vega-Rodriguez, “A multiobjective approach based on the behavior of fireflies
to generate reliable DNA sequences for molecular computing”, Applied Mathematics and Computation, Vol. 227, pp.
291–308, January 15, 2014.
141. Brijesh Kumar Giri, Jussi Hakanen, Kaisa Miettinen and Nirupam Chakraborti, “Genetic programming through biobjective genetic algorithms with a study of a simulated moving bed process involving multiple objectives”, Applied Soft
Computing, Vol. 13, No. 5, pp. 2613–2623, May 2013.
142. J.M. Herrero, G. Reynoso-Meza, M. Martinez, X. Blasco and J. Sanchis, “A Smart-Distributed Pareto Front Using the
ev-MO GA Evolutionary Algorithm”, International Journal on Artificial Intelligence Tools, Vol. 23, No. 2, Article
Number: 1450002, April 2014.
143. Yu-Jun Zheng, Hai-Feng Ling, Jin-Yun Xue and Sheng-Yong Chen, “Population Classification in Fire Evacuation: A
Multiobjective Particle Swarm Optimization Approach”, IEEE Transactions on Evolutionary Computation, Vol. 18, No.
1, pp. 70–81, February 2014.
144. Diana Martin, Alejandro Rosete, Jesus Alcala-Fdez and Francisco Herrera, “A New Multiobjective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules”, IEEE Transactions
on Evolutionary Computation, Vol. 18, No. 1, pp. 54–69, February 2014.
145. Alvaro Garcia-Piquer, Albert Fornells, Jaume Bacardit, Albert Orriols-Puig and Elisabet Golobardes, “Large-Scale
Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering”, IEEE Transactions on
Evolutionary Computation, Vol. 18, No. 1, pp. 36–53, February 2014.
146. Masoud Asadzadeh and Brya Tolson, “Pareto archived dynamically dimensioned search with hypervolume-based selection
for multi-objective optimization”, Engineering Optimization, Vol. 45, No. 12, pp. 1489–1509, December 1, 2013.
9
147. N. Al Moubayed, A. Petrovski and J. McCall, “D2 MOPSO: MOPSO Based on Decomposition and Dominance with
Archiving Using Crowding Distance in Objective and Solution Spaces”, Evolutionary Computation, Vol. 22, No. 1, pp.
47–77, Spring 2014.
148. Sajad Tabatabaei, “A new gravitational search optimization algorithm to solve single and multiobjective optimization
problems”, Journal of Intelligent & Fuzzy Systems, Vol. 26, No. 2, pp. 993–1006, 2014.
149. Ke Li, Alvaro Fialho, Sam Kwong and Qingfu Zhang, “Adaptive Operator Selection With Bandits for a Multiobjective
Evolutionary Algorithm Based on Decomposition”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 1,
pp. 114–130, February 2014.
150. Yeboon Yun and Hirotaka Nakayama, “Utilizing expected improvement and generalized data envelopment analysis in
multi-objective genetic algorithms”, Journal of Global Optimization, Vol. 57, No. 2, pp. 367–384, October 2013.
151. Andre Britto and Aurora Pozo, “Using reference points to update the archive of MOPSO algorithms in Many-Objective
Optimization”, Neurocomputing, Vol. 127, pp. 78–87, March 15, 2014.
152. Zoran Vujicic, Rogerio P. Dionisio, Ali Shahpari, Natasa B. Pavlovic and Antonio Teixeira, “Efficient Dynamic Modeling
of the Reflective Semiconductor Optical Amplifier”, IEEE Journal of Selected Topics in Quantum Electronics, Vol. 19,
No. 5, Article Number: 3000310, September-October 2013.
153. X. Hyacinth Suganthi, U. Natarajan, S. Sathiyamurthy and K. Chidambaram, “Prediction of quality responses in
micro-EDM process using an adaptive neuro-fuzzy inference system (ANFIS) model”, International Journal of Advanced
Manufacturing Technology, Vol. 68, Nos. 1-4, pp. 339–347, September 2013.
154. Frederic Pinel, Bernabe Dorronsoro, Johnatan E. Pecero, Pascal Bouvry and Samee U. Khan, “A two-phase heuristic
for the energy-efficient scheduling of independent tasks on computational grids”, Cluster Computing–The Journal of
Networks Software Tools and Applications, Vol. 16, No. 3, pp. 421–433, September 2013.
155. Luis Felipe Caetano and Paulo Fonseca Teixeira, “Availability Approach to Optimizing Railway Track Renewal Operations”, Journal of Transporation Engineering, Vol. 139, No. 9, pp. 941–948, September 1, 2013.
156. Andrea Maesani, Pradeep Ruben Fernando and Dario Floreano, “Artificial Evolution by Viability Rather than Competition”, Plos One, Vol. 9, No. 1, Article Number: e86831, January 29, 2014.
157. Jacek Widuch, “A Label Correcting Algorithm for the Bus Routing Problem”, Fundamenta Informaticae, Vol. 118, No.
3, pp. 305–326, 2012.
158. Mir Majid Etghani, Mohammad Hassan Shojaeefard, Abolfazl Khalkhali and Mostafa Akbari, “A hybrid method of
modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel”, Applied
Thermal Engineering, Vol. 59, No. 1-2, pp. 309–315, September 25, 2013.
159. N. Fallah and S. Honarparast, “NSGA-II based multi-objective optimization in design of Pall friction dampers”, Journal
of Constructional Steel Research, Vol. 89, pp- 75–85, October 2013.
160. Seyed Hamid Reza Pasandideh, Seyed Taghi Akhavan Niaki and Sharareh Sharafzadeh, “Optimizing a bi-objective
multi-product EPQ model with defective items, rework and limited orders: NSGA-II and MOPSO algorithms”, Journal
of Manufacturing Systems, Vol. 32, No. 4, pp. 764–770, October 2013.
161. Jiao Shi, Maoguo Gong, Wenping Ma and Licheng Jiao, “A Multipopulation Coevolutionary Strategy for Multiobjective
Immune Algorithm”, Scientific World Journal, Article Number: 539128, 2014.
162. Ilhern Boussaid, Julien Lepagnot and Patrick Siarry, “A survey on optimization metaheuristics”, Information Sciences,
Vol. 237, pp. 82–117, July 10, 2013.
163. Sandra M. Venske, Richard A. Goncalves and Myriam R. Delgado, “ADEMO/D: Multiobjective optimization by an
adaptive differential evolution algorithm”, Neurocomputing, Vol. 127, pp. 65–77, March 15, 2014.
164. Luis Marti, Jesus Garcia, Antonio Berlanga and Jose M. Molina, “Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm”, Annals of Mathematics and Artificial Intelligence, Vol. 68,
No. 4, pp. 247–273, August 2013.
165. Efrén Mezura-Montes, Edgar A. Portilla-Flores and Betania Hernández-Ocaña, “Optimum synthesis of a four-bar mechanism using the modified bacterial foraging algorithm”, International Journal of Systems Science, Vol. 45, No. 5, pp.
1080–1100, May 4, 2014.
166. Najwa Altwaijry and Mohamed El Bachir Menai, “Data Structures in Multi-Objective Evolutionary Algorithms”, Journal
of Computer Science and Technology, Vol. 27, No. 6, pp. 1197–1210, November 2012.
167. Jiuping Xu and Zhimiao Tao, “A class of multi-objective equilibrium chance maximization model with twofold random
phenomenon and its application to hydropower station operation”, Mathematics and Computers in Simulation, Vol. 85,
pp. 11–33, November 2012.
168. Dan Zhang, Zhen Gao and Irene Fassi, “Design optimization of a spatial hybrid mechanism for micromanipulation”,
International Journal of Mechanics and Materials in Design, Vol. 7, No. 1, pp. 55–70, March 2011.
10
169. R. Cela and M.H. Bollain, “New cluster mapping tools for the graphical assessment of non-dominated solutions in
multi-objective optimization”, Chemometrics and Intelligent Laboratory Systems, Vol. 114, pp. 72–86, May 15, 2012.
170. Fatimah Sham Ismail, Rubiyah Yusof and Marzuki Khalid, “Optimization of electronics component placement design
on PCB using self organizing genetic algorithm (SOGA)”, Journal of Intelligent Manufacturing, Vol. 23, No. 3, pp.
883–895, June 2012.
171. Kousik Deb and Anirban Dhar, “Parameter Estimation for a System of Beams Resting on Stone Column-Reinforced
Soft Soil”, International Journal of Geomechanics, Vol. 13, No. 3, pp. 222–233, June 2013.
172. Diego Jose Bodas-Sagi, Pablo Fernandez-Blanco, Jose Ignacio Hidalgo and Francisco Jose Soltero-Domingo, “A parallel
evolutionary algorithm for technical market indicators optimization”, Natural Computing, Vol. 12, No. 2, pp. 195–207,
June 2013.
173. Suat Ozdemir, Bara’a A. Attea and Onder A. Khalil, “Multi-objective clustered-based routing with coverage control in
wireless sensor networks”, Soft Computing, Vol. 17, No. 9, pp. 1573–1584, September 2013.
174. F. Afsari, M. Eftekhari, E. Eslami and P.-Y. Woo, “Interpretability-based fuzzy decision tree classifier a hybrid of the
subtractive clustering and the multi-objective evolutionary algorithm” Soft Computing, Vol. 17, No. 9, pp. 1673–1686,
September 2013.
175. Nicola Beume, Boris Naujoks and Michael Emmerich, “SMS-EMOA: Multiobjective selection based on dominated hypervolume”, European Journal of Operational Research, Vol. 181, No. 3, pp. 1653–1669, September 16, 2007.
176. T. Hanne and S. Nickel, “A multiobjective evolutionary algorithm for scheduling and inspection planning in software
development projects”, European Journal of Operational Research, Vol. 167, No. 3, pp. 663–678, December 16, 2005.
177. Liping Jia, Yuping Wang and Lei Fan, “Multiobjective bilevel optimization for production-distribution planning problems
using hybrid genetic algorithm”, Integrated Computer-Aided Engineering, Vol. 21, No. 1, pp. 77–90, 2014.
178. Nikos D. Lagaros, “An efficient dynamic load balancing algorithm”, Computational Mechanics, Vol. 53, No. 1, pp.
59–76, January 2014.
179. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “An effective model of multiple multiobjective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs”,
Applied Soft Computing, Vol. 13, No. 5, pp. 2863–2895, May 2013.
180. Khairy Elsayed and Chris Lacor, “CFD modeling and multi-objective optimization of cyclone geometry using desirability
function, artificial neural networks and genetic algorithms”, Applied Mathematical Modelling, Vol. 37, No. 8, pp. 5680–
5704, April 15, 2013.
181. Beatriz Pontes, Raul Giraldez and Jesus S. Aguilar-Ruiz, “Configurable pattern-based evolutionary biclustering of gene
expression data”, Algorithms for Molecular Biology, Vol. 8, Article Number: UNSP 4, February 23, 2013.
182. N.P. Garcia-Lopez, M. Sanchez-Silva, A.L. Medaglia and A. Chateauneuf, “An improved robust topology optimization
approach using multiobjective evolutionary algorithms”, Computers & Structures, Vol. 125, pp. 1–10, September 2013.
183. F. Jolai, H. Asefi, M. Rabiee and P. Ramezani, “Bi-objective simulated annealing approaches for no-wait two-stage
flexible flow shop scheduling problem”, Scientia Iranica, Vol. 20, No. 3, pp. 861–872, June 2013.
184. Datong Xie, Lixin Ding, Yurong Hu, Shenwen Wang, ChengWang Xie and Lei Jiang, “A Multi-Algorithm Balancing
Convergence and Diversity for Multi-Objective Optimization”, Journal of Information Science and Engineering, Vol.
29, No. 4, pp. 811–834, September 2013.
185. Victor M. Cervantes-Salido, Oswaldo Jaime, Carlos A. Brizuela and Israel M. Martinez-Perez, “Improving the design of
sequences for DNA computing: A multiobjective evolutionary approach”, Applied Soft Computing, Vol. 13, No. 12, pp.
4594–4607, December 2013.
186. Aniruddha Basak, Swagatam Das and Kay Chen Tan, “Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance-Based Selection”, IEEE Transactions on Evolutionary Computation,
Vol. 17, No. 5, pp. 666–685, October 2013.
187. Fulya Altiparmak, Mitsuo Gen, Lin Lin and Turan Paksoy, “A genetic algorithm approach for multi-objective optimization of supply chain networks”, Computers & Industrial Engineering, Vol. 51, pp. 196–215, September 2006.
188. Yu-Jun Zheng, Qing Song and Sheng-Yong Chen, “Multiobjective fireworks optimization for variable-rate fertilization
in oil crop production”, Applied Soft Computing, Vol. 13, No. 11, pp. 4253–4263, November 2013.
189. Karthik Sindhya, Kaisa Miettinen and Kalyanmoy Deb, “A Hybrid Framework for Evolutionary Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 4, pp. 495–511, August 2013.
190. Urvesh Bhowan, Mark Johnston, Mengjie Zhang and Xin Yao, “Evolving Diverse Ensembles Using Genetic Programming
for Classification With Unbalanced Data”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 3, pp. 368–
386, June 2013.
191. Elisabet Capon-Garcia, Aaron D. Bojarski, Antonio Espuna and Luis Puigjaner, “Multiobjective Evolutionary Optimization of Batch Process Scheduling Under Environmental and Economic Concerns”, AICHE Journal, Vol. 59, No. 2,
pp. 429–444, February 2013.
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192. Eduardo Fernandez and Rafael Olmedo, “An outranking-based general approach to solving group multi-objective optimization problems”, European Journal of Operational Research, Vol. 225, No. 3, pp. 497–506, March 16, 2013.
193. Prakash Shelokar, Arnaud Quirin and Oscar Cordon, “MOSubdue: a Pareto dominance-based multiobjective Subdue
algorithm for frequent subgraph mining”, Knowledge and Information Systems, Vol. 34, No. 1, pp. 75–108, January
2013.
194. Melissa Gardenghi, Margaret M. Wiecek and Wenshan Wang, “Biobjective optimization for analytical target cascading:
optimality vs. achievability”, Structural and Multidisciplinary Optimization, Vol. 47, No. 1, pp. 111–133, January 2013.
195. Yaohang Li, “MOMCMC: An efficient Monte Carlo method for multi-objective sampling over real parameter space”,
Computers & Mathematics with Applications, Vol. 64, No. 11, pp. 3542–3556, December 2012.
196. Yau-Zen Chang, Kao-Ting Hung, Hsin-Yi Shih and Zhi-Ren Tsai, “Surrogate Neural Network and Multi-Objective Direct
Algorithm for the Optimization of a Swiss-Roll Type Recuperator”, International Journal of Innovative Computing
Information and Control, Vol. 8, No. 12, pp. 8199–8214, December 2012.
197. Irwanda Laory, Nizar Bel Hadj Ali, Thanh N. Trinh and Ian F.C. Smith, “Measurement System Configuration for
Damage Identification of Continuously Monitored Structures”, Journal of Bridge Engineering, Vol. 17, No. 6, pp.
857–866, November-December 2012.
198. P. Shahnazari-Shahrezaei, R. Tavakkoli-Moghaddam, M. Azarkish and A. Sadeghnejad-Barkousaraie, “A Differential
Evolution Algorithm Developed for a Nurse Scheduling Problem”, South African Journal of Industrial Engineering, Vol.
23, No. 3, pp. 68–90, November 2012.
199. ZhiQiang He, Kai Niu, Tao Qiu, Tao Song, WenJun Xu, Li Guo and JiaRu Lin, “A bio-inspired approach for cognitive
radio networks”, Chinese Science Bulletin, Vol. 57, Nos. 28-29, pp. 3723–3730, October 2012.
200. Juan Gabriel Correa Medina, Loecelia Guadalupe Ruvalcaba Sanchez, Elias Olivares-Benitez and Vittorio Zanella Palacios, “Biobjective Model for Redesigning Sales Territories”, International Journal of Industrial Engineering–Theory,
Applications and Practice, Vol. 19, No. 9, pp. 350–358, 2012.
201. Thomas A. Wettergren and Russell Costa, “Optimal Multiobjective Placement of Distributed Sensors against Moving
Targets”, ACM Transactions on Sensor Networks, Vol. 8, No. 3, Article Number: 21, 2012.
202. Maik Ringkamp, Sina Ober-Blobaum, Michael Dellnitz and Oliver Schütze, “Handling high-dimensional problems with
multi-objective continuation methods via successive approximation of the tangent space”, Engineering Optimization,
Vol. 44, No. 9, pp. 1117–1146, 2012.
203. R. Venkata Rao and V.D. Kalyankar, “Parameter Optimization of Machining Processes Using a New Optimization
Algorithm”, Materials and Manufacturing Processes, Vol. 27, No. 9, pp. 978–985, 2012.
204. Seyed Reza Hosseini, Majid Amidpour and Seyed Ehsan Shakib, “Cost optimization of a combined power and water
desalination plant with exergetic, environment and reliability consideration”, Desalination, Vol. 285, pp. 123–130,
January 31, 2012.
205. Juan F. Fernandez-Bootello, Manuel Delgado-Restituto and Angel Rodriguez-Vazquez, “IC-constrained optimization of
continuous-time Gm-C filters”, International Journal of Circuit Theory and Applications, Vol. 40, No. 2, pp. 127–143,
February 2012.
206. Jose Luis Guerrero, Antonio Berlanga and Jose Manuel Molina, “A multi-objective approach for the segmentation issue”,
Engineering Optimization, Vol. 44, No. 3, pp. 267–287, 2012.
207. A.M. Mora, P. Garcia-Sanchez, J.J. Merelo and P.A. Castillo, “Pareto-based multi-colony multi-objective ant colony
optimization algorithms: an island model proposal”, Soft Computing, Vol. 17, No. 7, pp. 1175–1207, July 2013.
208. Cristina Teixeira, J.A. Covas, Thomas Stutzle and A. Gaspar-Cunha, “Multi-objective ant colony optimization for the
twin-screw configuration problem”, Engineering Optimization, Vol. 44, No. 3, pp. 351–371, 2012.
209. K. Sivakumar, C. Balamurugan and S. Ramabalan, “Evolutionary multi-objective concurrent maximisation of process
tolerances”, International Journal of Production Research, Vol. 50, No. 12, pp. 3172–3191, 2012.
210. X. Dong, S. Zeng and J. Chen, “A spatial multi-objective optimization model for sustainable urban wastewater system
layout planning”, Water Science and Technology, Vol. 66, No. 2, pp. 267–274, 2012.
211. Jaime Gagne and Marilyne Andersen, “A generative facade design method based on daylighting performance goals”,
Journal of Building Performance Simulation, Vol. 5, No. 3, pp. 141–154, 2012.
212. Amos H.C. Ng, Jacob Bernedixen and Anna Syberfeldt, “A comparative study of production control mechanisms using
simulation-based multi-objective optimisation”, International Journal of Production Research, Vol. 50, No. 2, pp.
359–377, 2012.
213. Susmita Bandyopadhyay and Ranjan Bhattacharya, “Applying modified NSGA-II for bi-objective supply chain problem”,
Journal of Intelligent Manufacturing, Vol. 24, No. 4, pp. 707–716, August 2013.
214. J.B. Kollat, P.M. Reed and T. Wagener, “When are multiobjective calibration trade-offs in hydrologic models meaningful?”, Water Resources Research, Vol. 48, Article Number: W03520, March 21, 2012.
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215. Miguel G. Villarreal-Cervantes, Carlos A. Cruz-Villar, Jaime Alvarez-Gallegos and Edgar A. Portilla-Flores, “Robust
Structure-Control Design Approach for Mechatronic Systems”, IEEE-ASME Transactions on Mechatronics, Vol. 18,
No. 5, pp. 1592–1601, October 2013.
216. Masoud Asadzadeh and Bryan Tolson, “Hybrid Pareto archived dynamically dimensioned search for multi-objective
combinatorial optimization: application to water distribution network design”, Journal of Hydroinformatics, Vol. 14,
No. 1, pp. 192–205, January 2012.
217. Jeroen Groot and Walter A.H. Rossing, “Model-aided learning for adaptive management of natural resources: an evolutionary design perspective”, Methods in Ecology and Evolution, Vol. 2, No. 6, pp. 643–650, December 2011.
218. Viktor Vegh and Quang M. Tieng, “Unconstrained Real Valued Optimization Based on Stochastic Differential Equations”, International Journal of Innovative Computing Information and Control, Vol. 7, No. 11, pp. 6235–6246,
November 2011.
219. Andre L.V. Coelho, Everlandio Fernandes and Katti Faceli, “Multi-objective design of hierarchical consensus functions
for clustering ensembles via genetic programming”, Decision Support Systems, Vol. 51, No. 4, pp. 794–809, November
2011.
220. J.C. Calvo, J. Ortega and M. Anguita, “Comparison of parallel multi-objective approaches to protein structure prediction”, Journal of Supercomputing, Vol. 58, No. 2, pp. 253–260, November 2011.
221. E. Zio and G. Viadana, “Optimization of the inspection intervals of a safety system in a nuclear power plant by MultiObjective Differential Evolution (MODE)”, Reliability Engineering & System Safety, Vol. 96, No. 11, pp. 1552–1563,
November 2011.
222. Yufeng Shang and Bo Yu, “A constraint shifting homotopy method for convex multi-objective programming”, Journal
of Computational and Applied Mathematics, Vol. 236, No. 5, pp. 640–646, October 1, 2011.
223. Satyabrata Sen, Gongguo Tang and Arye Nehorai, “Multiobjective Optimization of OFDM Radar Waveform for Target
Detection”, IEEE Transactions on Signal Processing, Vol. 59, No. 2, pp. 639–652, February 2011.
224. Y.-K. Juan, L. Wang, J. Wang, J.O. Leckie and K.-M. Li, “A decision-support system for smarter city planning and
management”, IBM Journal of Research and Development, Vol. 55, Nos. 1-2, Article Number: 3, January-March 2011.
225. Bara’a Ali Attea, Laylan Mohammad Rashid and Wafaa Khazzal Shames, “Evolutionary algorithm for example-based
painterly rendering”, International Journal of Bio-Inspired Computation, Vol. 2, No. 2, pp. 132–141, 2010.
226. Konstantinos B. Baltzis, “An Efficient Finger Allocation Method for the Maximum Likelihood RAKE Receiver”, Radioengineering, Vol. 17, No. 4, pp. 45–50, December 2008.
227. Claudio R.M. Silva and Sinara R. Martins, “An Adaptive Evolutionary Algorithm for UWB Microstrip Antennas Optimization using a Machine Learning Technique”, Microwave and Optical Technology Letters, Vol. 55, No. 8, pp.
1864–1868, August 2013.
228. Steve Bergen and Brian J. Ross, “Aesthetic 3D model evolution”, Genetic Programming and Evolvable Machines, Vol.
14, No. 3, pp. 339–367, September 2013.
229. Leandro D. Vignolo, Diego H. Milone and Jacob Scharcanski, “Feature selection for face recognition based on multiobjective evolutionary wrappers”, Expert Systems with Applications, Vol. 40, No. 13, pp. 5077–5084, October 1, 2013.
230. Weijian Kong, Tianyou Chai, Shengxiang Yang and Jinliang Ding, “A hybrid evolutionary multiobjective optimization
strategy for the dynamic power supply problem in magnesia grain manufacturing”, Applied Soft Computing, Vol. 13,
No. 5, pp. 2960–2969, May 2013.
231. Sultan Nomal Qasem, Siti Mariyam Shamsuddin, Siti Zaiton Mohd Hashim, Maslina Darus and Eiman Al-Shammari,
“Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems”,
Information Sciences, Vol. 239, pp. 165–190, August 1, 2013.
232. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm”, Journal of Intelligent
Manufacturing, Vol. 24, No. 2, pp. 277–293, April 2013.
233. M. Rabiee, M. Zandieh and P. Ramezani, “Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA,
MOGA and PAES approaches”, International Journal of Production Research, Vol. 50, No. 24, pp. 7327–7342, 2012.
234. Prakash Shelokar, Arnaud Quirin and Oscar Cordon, “A multiobjective evolutionary programming framework for graphbased data mining”, Information Sciences, Vol. 237, pp. 118–136, July 10, 2013.
235. Christiane Regina Soares Brasil, Alexandre Claudio Botazzo Delbem and Fernando Luis Barroso da Silva, “Multiobjective
evolutionary algorithm with many tables for purely ab initio protein structure prediction”, Journal of Computational
Chemistry, Vol. 34, No. 20, pp. 1719–1734, July 30, 2013.
236. Vui Ann Shim, Kay Chen Tan, Jun Yong Chia and Abdullah Al Mamun, “Multi-Objective Optimization with Estimation
of Distribution Algorithm in a Noisy Environment”, Evolutionary Computation, Vol. 21, No. 1, pp. 149–177, Spring
2013.
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237. Sinan Korkmaz, Nizar Bel Hadj Ali and Ian F.C. Smith, “Configuration of control system for damage tolerance of a
tensegrity bridge”, Advanced Engineering Informatics, Vol. 26, No. 1, pp. 145–155, January 2012.
238. Eunice Oliveira, Carlos Henggeler Antunes and Alvaro Gomes, “A comparative study of different approaches using an
outranking relation in a multi-objective evolutionary algorithm”, Computers & Operations Research, Vol. 40, No. 6, pp.
1602–1615, June 2013.
239. Alexandre C.B. Delbem, Telma W. de Lima and Guilherme P. Telles, “Efficient Forest Data Structure for Evolutionary
Algorithms Applied to Network Design”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 6, pp. 829–846,
December 2012.
240. Marcia P. Basgalupp, Andre C.P.L.F. de Carvalho, Rodrigo C. Barros, Duncan D. Ruiz and Alex A. Freitas, “Lexicographic multi-objective evolutionary induction of decision trees”, International Journal of Bio-Inspired Computation,
Vol. 1, Nos. 1-2, pp. 105–117, 2009.
241. M.V.C. da Silva, N. Nedjah and L.M. Mourelle, “Power-aware multi-objective evolutionary optimisation for application
mapping on network-on-chip platforms”, International Journal of Electronics, Vol. 97, No. 10, pp. 1163–1179, Article
Number: PII 927691877, 2010.
242. Maria del Jesus, Jose A. Gamez, Pedro Gonzalez and Jose M. Puerta, “On the discovery of association rules by means
of evolutionary algorithms”, Wiley Interdisciplinary Reviews–Data Mining and Knowlegde Discovery, Vol. 1, No. 5, pp.
397–415, September-October 2011.
243. Xinye Cai, Ou Wei and Zhiqiu Huang, “Evolutionary Approaches for Multi-Objective Next Release Problem”, Computing
and Informatics, Vol. 31, No. 4, pp. 847–875, 2012.
244. Marcela Zuluaga, Andreas Krause, Peter Milder and Markus Püschel, ““Smart” Design Space Sampling to Predict
Pareto-Optimal Solutions”, ACM SIGPLAN Notices, Vol. 47, No. 5, pp. 119–128, May 2012.
245. Matthieu Basseur, Rong-Qiang Zeng and Jin-Kao Hao, “Hypervolume-based multi-objective local search”, Neural Computing & Applications, Vol. 21, No. 8, pp. 1917–1929, November 2012.
246. Diana Manjarres, Javier Del Ser, Sergio Gil-Lopez, Massimo Vecchio, Itziar Landa-Torres, Sancho Salcedo-Sanz and
Roberto Lopez-Valcarce, “On the design of a novel two-objective harmony search approach for distance- and connectivitybased localization in wireless sensor networks”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 2, pp.
669–676, February 2013.
247. Nuria Macia, Ester Bernado-Mansilla, Albert Orriols-Puig and Tin Kam Ho, “Learner excellence biased by data set
selection: A case for data characterisation and artificial data sets”, Pattern Recognition, Vol. 46, No. 3, pp. 1054–1066,
March 2013.
248. Thelma Elita Colanzi, Silvia Regina Vergilio, Wesley Klewerton Guez Assuncao and Aurora Pozo, “Search Based Software
Engineering: Review and analysis of the field in Brazil”, Journal of Systems and Software, Vol. 86, No. 4, pp. 970–984,
April 2013.
249. Francisco J. Rodriguez, Carlos Garcia-Martinez and Manuel Lozano, “Hybrid Metaheuristics Based on Evolutionary
Algorithms and Simulated Annealing: Taxonomy, Comparison, and Synergy Test”, IEEE Transactions on Evolutionary
Computation, Vol. 16, No. 6, pp. 787–800, December 2012.
250. David Hadka and Patrick Reed, “Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework”,
Evolutionary Computation, Vol. 21, No. 2, pp. 231–259, Summer 2013.
251. Ronay Ak, Yanfu Li, Valeria Vitelli, Enrico Zio, Enrique Lopez Droguett and Carlos Magno Couto Jacinto, “NSGA-IItrained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment”,
Expert Systems with Applications, Vol. 40, No. 4, pp. 1205–1212, March 2013.
252. Sergio Nesmachnow, “Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing
and grid systems”, Computational Optimization and Applications, Vol. 55, No. 2, pp. 515–544, June 2013.
253. Vili Podgorelec, Matej Sprogar and Sandi Pohorec, “Evolutionary design of decision trees”, Wiley Interdisciplinary
Reviews-Data Mining and Knowledge Discovery, Vol. 3, No. 2, pp. 63–82, March-April 2013.
254. Wanxing Sheng, Ke-yan Liu, Yongmei Liu, Xiaoli Meng and Xiaohui Song, “A New DG Multiobjective Optimization
Method Based on an Improved Evolutionary Algorithm”, Journal of Applied Mathematics, Article Number: 643791,
2013.
255. Raul Banos, Julio Ortega, Consolacion Gil, Antonio L. Marquez and Francisco de Toro, “A hybrid meta-heuristic for
multi-objective vehicle routing problems with time windows”, Computers & Industrial Engineering, Vol. 65, No. 2, pp.
286–296, June 2013.
256. David Lahoz, Beatriz Lacruz and Pedro M. Mateo, “A multi-objective micro genetic ELM algorithm”, Neurocomputing,
Vol. 111, pp. 90–103, July 2, 2013.
257. Giuseppe Aiello, Giada La Scalia and Mario Enea, “A non dominated ranking Multi Objective Genetic Algorithm and
electre method for unequal area facility layout problems”, Expert Systems with Applications, Vol. 40, No. 12, pp.
4812–4819, September 15, 2013.
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258. Raffaele Grasso, Marco Cococcioni, Baptiste Mourre, John Osler and Jacopo Chiggiato, “A decision support system for
optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization”,
Expert Systems with Applications, Vol. 40, No. 10, pp. 3886–3899, August 2013.
259. Bo Wang and Junzo Watada, “Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem”,
IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, No. 2, pp. 146–154, March 2013.
260. Andre Schardong, Slobodan P. Simonovic and A. Vasan, “Multiobjective Evolutionary Approach to Optimal Reservoir
Operation”, Journal of Computing in Civil Engineering, Vol. 27, No. 2, pp. 139–147, March 2013.
261. Maoguo Gong, Xiaowei Chen, Lijia Ma, Qingfu Zhang and Licheng Jiao, “Identification of multi-resolution network
structures with multi-objective immune algorithm”, Applied Soft Computing, Vol. 13, No. 4, pp. 1705–1717, April 2013.
262. A. Jamali, M. Ghamati, B. Ahmadi and N. Nariman-zadeh, “Probability of failure for uncertain control systems using neural networks and multi-objective uniform-diversity genetic algorithms (MUGA)”, Engineering Applications of
Artificial Intelligence, Vol. 26, No. 2, pp. 714–723, February 2013.
263. Fabien Tricoire, “Multi-directional local search”, Computers & Operations Research, Vol. 39, No. 12, pp. 3089–3101,
December 2012.
264. Gilberto Reynoso-Meza, Sergio Garcia-Nieto, Javier Sanchis and F. Xavier Blasco, “Controller Tuning by Means of MultiObjective Optimization Algorithms: A Global Tuning Framework”, IEEE Transactions on Control Systems Technology,
Vol. 21, No. 2, pp. 445–458, March 2013.
265. A. Kaveh and K. Laknejadi, “A hybrid evolutionary graph-based multi-objective algorithm for layout optimization of
truss structures”, Acta Mechanica, Vol. 224, No. 2, pp. 343–364, February 2013.
266. Rajan Filomeno Coelho, “Co-Evolutionary Optimization for Multi-Objective Design Under Uncertainty”, Journal of
Mechanical Design, Vol. 135, No. 2, Article Number: 021006, February 2013.
267. Igor Vatolkin, Mike Preuss, Günter Rudolph, Markus Eichhoff and Claus Weihs, “Multi-objective evolutionary feature
selection for instrument recognition in polyphonic audio mixtures”, Soft Computing, Vol. 16, No. 12, pp. 2027–2047,
December 2012.
268. Anirban Mukhopadhyay, Sumanta Ray and Moumita De, “Detecting protein complexes in a PPI network: a gene
ontology based multi-objective evolutionary approach”, Molecular Biosystems, Vol. 8, No. 11, pp. 3036–3048, 2012.
269. A. Alcayde, R. Baños, C. Gil, F.G. Montoya, J. Moreno-Garcia and J. Gomez, “Annealing-tabu PAES: a multi-objective
hybrid meta-heuristic”, Optimization, Vol. 60, No. 12, pp. 1473–1491, 2011.
270. Raul Baños, Julio Ortega, Consolacion Gil, Antonio Fernandez and Francisco de Toro, “A Simulated Annealing-based
parallel multi-objective approach to vehicle routing problems with time windows”, Expert Systems with Applications,
Vol. 40, No. 5, pp. 1696–1707, April 2013.
271. Gilberto Reynoso-Meza, Xavier Blasco, Javier Sanchis and Juan M. Herrero, “Comparison of design concepts in multicriteria decision-making using level diagrams”, Information Sciences, Vol. 221, pp. 124–141, February 1, 2013.
272. Michela Fazzolari, Rafael Alcala, Yusuke Nojima, Hisao Ishibuchi and Francisco Herrera, “A Review of the Application
of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions”, IEEE Transactions on Fuzzy
Systems, Vol. 21, No. 1, pp. 45–65, February 2013.
273. Maria Jose Gacto, Rafael Alcala and Francisco Herrera, “A multi-objective evolutionary algorithm for an effective tuning
of fuzzy logic controllers in heating, ventilating and air conditioning systems”, Applied Intelligence, Vol. 36, No. 2, pp.
330–347, March 2012.
274. Ke Li, Sam Kwong, Ran Wang, Kit-Sang Tang and Kim-Fung Man, “Learning paradigm based on jumping genes: A
general framework for enhancing exploration in evolutionary multiobjective optimization”, Information Sciences, Vol.
226, pp. 1–22, March 20, 2013.
275. P.M. Reed, D. Hadka, J.D. Herman, J.R. Kasprzyk and J.B. Kollat, “Evolutionary multiobjective optimization in water
resources: The past, present, and future”, Advances in Water Resources, Vol. 51, pp. 438–456, January 2013.
276. Lixin Tang and Xianpen Wang, “A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization
Problems”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 20–45, February 2013.
277. Juan Arturo Herrera Ortiz, Katya Rodriguez-Vazquez, Miguel A. Castaneda and Fernando Arambula Cosio, “Autonomous robot navigation based on the evolutionary multi-objective optimization of potential fields”, Engineering
Optimization, Vol. 45, No. 1, pp. 19–43, 2013.
278. Sultan Noman Qasem, Siti Mariyam Shamsuddin and Azlan Mohd Zain, “Multi-objective hybrid evolutionary algorithms
for radial basis function neural network design”, Knowledge-based Systems, Vol. 27, pp. 475–497, March 2012.
279. M.J. Mahmoodabadi, S. Arabani Mostaghim, A. Bagheri and N. Nariman-zadeh, “Pareto optimal design of the decoupled sliding mode controller for an inverted pendulum system and its stability simulation via Java programming”,
Mathematical and Computer Modelling, Vol. 57, Nos. 5-6, pp. 1070–1082, March 2013.
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280. M.J. Mahmoodabadi, A. Bagheri, N. Nariman-Zadeh, A. Jamali and R. Abedzadeh Maafi, “Pareto Design of Decoupled
Sliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm”, Journal of Applied
Mathematics, Article Number: 639014, 2012.
281. A. Shokuhi-Rad, A. Jamali, M. Naghashzadegan, N. Nariman-zadeh and A. Hajiloo, “Optimum Pareto design of nonlinear predictive control with multi-design variables for PEM fuel cell”, International Journal of Hydrogen Energy, Vol.
37, No. 15, pp. 11244–11254, August 2012.
282. M. Mohammad Rezapour Tabari and Jaber Soltani, “Multi-Objective Optimal Model for Conjunctive Use Management
Using SGAs and NSGA-II Models”, Water Resources Management, Vol. 27, No. 1, pp. 37–53, January 2013.
283. Itza T.Q. Curiel, Sonia B. Di Giannatale, Juan A. Herrera and Katya Rodriguez, “Pareto Frontier of a Dynamic
Principal-Agent Model with Discrete Actions: An Evolutionary Multi-Objective Approach”, Computational Economics,
Vol. 40, No. 4, pp. 415–443, December 2012.
284. Fernando Alonso Zotes and Matilde Santos Penas, “Particle swarm optimisation of interplanetary trajectories from Earth
to Jupiter and Saturn”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 1, pp. 189–199, February 2012.
285. Virginia Yannibelli and Analia Amandi, “Project scheduling: A multi-objective evolutionary algorithm that optimizes
the effectiveness of human resources and the project makespan”, Engineering Optimization, Vol. 45, No. 1, pp. 45–65,
2013.
286. Carolina P. Almeida, Richard A. Goncalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, “An
experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem”, Annals of Operations
Research, Vol. 199, No. 1, pp. 305–341, October 2012.
287. Arnaud Liefooghe, Matthieu Basseur, Jeremie Humeau, Laetitia Jourdan and El-Ghazali Talbi, “On optimizing a biobjective flowshop scheduling problem in an uncertain environment”, Computers & Mathematics with Applications, Vol.
64, No. 12, pp. 3747–3762, December 2012.
288. Christian Grimme, Joachim Lepping and Alexander Papaspyrou, “Parallel predator-prey interaction for evolutionary
multi-objective optimization”, Natural Computing, Vol. 11, No. 3, pp. 519–533, September 2012.
289. Arnaud Liefooghe, Jeremie Humeau, Salma Mesmoudi, Laetitia Jourdan and El-Ghazali Talbi, “On dominance-based
multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman
problems”, Journal of Heuristics, Vol. 18, No. 2, pp. 317–352, April 2012.
290. David Greiner and Prabhat Hajela, “Truss topology optimization for mass and reliability considerations-co-evolutionary
multiobjective formulations”, Structural and Multidisciplinary Optimization, Vol. 45, No. 4, pp. 589–613, April 2012.
291. Thomas Weise, Raymond Chiong and Ke Tang, “Evolutionary Optimization: Pitfalls and Booby Traps”, Journal of
Computer Science and Technology, Vol. 27, No. 5, pp. 907–936, September 2012.
292. Jose A. Salinas-Perez, Carlos R. Garcia-Alonso, Cristina Molina-Parrilla, Esther Jorda-Sampietro and Luis SalvadorCarulla, “Identification and location of hot and cold spots of treated prevalence of depression in Catalonia (Spain)”,
International Journal of Health Geographics, Vol. 11, Article Number: 36, August 24, 2012.
293. Elisenda Roca, Manuel Velasco-Jimenez, Rafael Castro-Lopez and Francisco V. Fernandez, “Context-dependent transformation of Pareto-optimal performance fronts of operational amplifiers”, Analog Integrated Circuits and Signal Processing,
Vol. 73, No. 1, pp. 65–76, October 2012.
294. Neda Manavizadeh, Masoud Rabbani, Davoud Moshtaghi and Fariborz Jolai, “Mixed-model assembly line balancing
in the make-to-order and stochastic environment using multi-objective evolutionary algorithms”, Expert Systems with
Applications, Vol. 39, No. 15, pp. 12026–12031, November 1, 2012.
295. Enrique Alba, Gabriel Luque and Sergio Nesmachnow, “Parallel metaheuristics: recent advances and new trends”,
International Transactions in Operational Research, Vol. 20, No. 1, pp. 1–48, January 2013.
296. Soumyadip Sengupta, Swagatam Das, Md Nasir, Athanasios V. Vasilakos and Witold Pedrycz, “An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks”, IEEE Transactions on
Systems, Man and Cybernetics Part C–Applications and Reviews, Vol. 42, No. 6, pp. 1093–1102, November 2012.
297. Isabelle Grechi, Mohamed-Mahmoud Ould-Sidi, Nadine Hilgert, Rachid Senoussi, Benoit Sauphanor and Francoise
Lescourret, “Designing integrated management scenarios using simulation-based and multi-objective optimization: Application to the peach tree-Myzus persicae aphid system”, Ecological Modelling, Vol. 246, pp. 47–59, November 10,
2012.
298. Patricia Ruiz, Bernabe Dorronsoro, Giorgio Valentini, Frederic Pinel and Pascal Bouvry, “Optimisation of the enhanced
distance based broadcasting protocol for MANETs”, Journal of Supercomputing, Vol. 62, No. 3, pp. 1213–1240,
December 2012.
299. I. Arnaldo, J.L. Risco-Martin, J.L. Ayala and J.I. Hidalgo, “Power profiling-guided floorplanner for 3D multi-processor
systems-on-chip”, IET Circuits Devices & Systems, Vol. 6, No. 5, pp. 322–329, September 2012.
300. Gift Dumedah, “Formulation of the Evolutionary-Based Data Assimilation, and its Implementation in Hydrological
Forecasting”, Water Resources Management, Vol. 26, No. 13, pp. 3853–3870, October 2012.
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301. Clare Levene, Elon Correa, Ewan W. Blanch and Royston Goodacre, “Enhancing Surface Enhanced Raman Scattering
(SERS) Detection of Propranolol with Multiobjective Evolutionary Optimization”, Analytical Chemistry, Vol. 84, No.
18, pp. 7899–7905, September 18, 2012.
302. Kalyanmoy Deb, Francisco Ruiz, Mariano Luque, Rahul Tewari, Jose M. Cabello and Jose M. Cejudo, “On the sizing of
a solar thermal electricity plant for multiple objectives using evolutionary optimization”, Applied Soft Computing, Vol.
12, No. 10, pp. 3300–3311, October 2012.
303. Gift Dumedah, Aaron A. Berg and Mark Wineberg, “Pareto-optimality and a search for robustness: choosing solutions
with desired properties in objective space and parameter space”, Journal of Hydroinformatics, Vol. 14, No. 2, pp.
270–285, 2012.
304. James N. Richardson, Sigrid Adriaenssens, Philippe Bouillard and Rajan Filomeno Coelho, “Multiobjective topology
optimization of truss structures with kinematic stability repair”, Structural and Multidisciplinary Optimization, Vol. 46,
No. 4, pp. 513–532, October 2012.
305. Chun-Hao Chen, Tzung-Pei Hong, Vincent S. Tseng and Lien-Chin Chen, “Multi-objective Genetic-Fuzzy Data Mining”,
International Journal of Innovative Computing Information and Control, Vol. 8, No. 10A, pp. 6551–6568, October 2012.
306. P.M. Mateo and I. Alberto, “A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms”,
Journal of Heuristics, Vol. 18, No. 1, pp. 53–89, February 2012.
307. M.R. Dashtbayazi, “Artificial Neural Network-Based Multiobjective Optimization of Mechanical Alloying Process for
Synthesizing of Metal Matrix Nanocomposite Powder”, Materials and Manufacturing Processes, Vol. 27, No. 1, pp.
33–42, 2012.
308. H. Nasiraghdam and S. Jadid, “Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multiobjective artificial bee colony (MOABC) algorithm”, Solar Energy, Vol. 86, No. 10, pp. 3057–3071, October 2012.
309. Aryeh Warmflash, Paul Francois and Eric D. Siggia, “Pareto evolution of gene networks: an algorithm to optimize
multiple fitness objectives”, Physical Biology, Vol. 9, No. 5, Article Number: 056001, October 2012.
310. Manuel Chica, Oscar Cordon, Sergio Damas and Joaquin Bautista, “Multiobjective memetic algorithms for time and
space assembly line balancing”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 2, pp. 254–273, March
2012.
311. E. Zio, L.R. Golea and C.M. Rocco, “Identifying groups of critical edges in a realistic electrical network by multi-objective
genetic algorithms”, Reliability Engineering & System Safety, Vol. 99, pp. 172–177, March 2012.
312. G. Chiandussi, M. Codegone, S. Ferrero and F.E. Varesio, “Comparison of multi-objective optimization methodologies
for engineering applications”, Computers & Mathematics with Applications, Vol. 63, No. 5, pp. 912–942, March 2012.
313. Nuno F. Lages, Carlos Cordeiro, Marta Sousa Silva, Ana Ponces Freire and Antonio E.N. Ferreira, “Optimization of
Time-Course Experiments for Kinetic Model Discrimination”, Plos One, Vol. 7, No. 3, Article Number: e32749, March
5, 2012.
314. Samane Noori-Darvish, Iraj Mahdavi and Nezam Mahdavi-Amiri, “A bi-objective possibilistic programming model for
open shop scheduling problems with sequence-dependent setup times, fuzzy processing times, and fuzzy due dates”,
Applied Soft Computing, Vol. 12, No. 4, pp. 1399–1416, April 2012.
315. Sahar Ashayer, Mansur Askari and Hossein Afarideh, “Optimal per cent by weight of elements in diagnostic quality
radiation shielding materials”, Radiation Protection Dosimetry, Vol. 149, No. 3, pp. 268–288, April 2012.
316. Manojkumar Ramteke and Rajagopalan Srinivasan, “Large-Scale Refinery Crude Oil Scheduling by Integrating Graph
Representation and Genetic Algorithm”, Industrial & Engineering Chemistry Research, Vol. 51, No. 14, pp. 5256–5272,
April 11, 2012.
317. Izaskun Ibarbia, Alexander Mendiburu, Maria Santos and Jose A. Lozano, “An interactive optimization approach to a
real-world oceanographic campaign planning problem”, Applied Intelligence, Vol. 36, No. 3, pp. 721–734, April 2012.
318. Weiqin Ying, Xing Xu, Yuxiang Feng and Yu Wu, “An Efficient Conical Area Evolutionary Algorithm for Bi-objective Optimization”, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, Vol. E95A,
No. 8, pp. 1420–1425, August 2012.
319. Roberto Santana, Concha Bielza and Pedro Larrañaga, “Regularized logistic regression and multiobjective variable
selection for classifying MEG data”, Biological Cybernetics, Vol. 106, Nos. 6–7, pp. 389–405, September 2012.
320. Carolina P. Almeida, Richard A. Goncalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, “An
experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem”, Annals of Operations
Research, Vol. 199, No. 1, pp. 305–341, October 2012.
321. Jun-fang Li, Bu-han Zhang, Yi-fang Liu, Kui Wang and Xiao-shan Wu, “Spatial evolution character of multi-objective
evolutionary algorithm based on self-organized criticality theory”, Physica A–Statistical Mechanics and its Applications,
Vol. 391, No. 22, pp. 5490–5499, November 15, 2012.
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322. Ofer M. Shir, Jonathan Roslund, Zaki Leghtas and Herschel Rabitz, “Quantum control experiments as a testbed for
evolutionary multi-objective algorithms”, Genetic Programming and Evolvable Machines, Vol. 13, No. 4, pp. 445–491,
December 2012.
323. Domenico A. Bau and Jonghyun Lee, “Multi-Objective Optimization for the Design of Groundwater Supply Systems
under Uncertain Parameter Distribution”, Pacific Journal of Optimization, Vol. 7, No. 3, pp. 407–424, September 2011.
324. Hugo-Tiago C. Pedro and Marcelo H. Kobayashi, “On a cellular division method for topology optimization”, International
Journal for Numerical Methods in Engineering, Vol. 88, No. 11, pp. 1175–1197, December 16, 2011.
325. Domenico A. Bau, “Planning of Groundwater Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced
Models and Multi-Objective Evolutionary Optimization”, Water Resources Management, Vol. 26, No. 9, pp. 2513–2536,
July 2012.
326. Anthony Gerard Scanlan and Mark Keith Halton, “Hierarchical synthesis system with hybrid DLO-MOGA optimization”,
COMPEL–The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol.
30, No. 2, pp. 741–761, 2011.
327. Gustavo Olague and Leonardo Trujillo, “Interest point detection through multiobjective genetic programming”, Applied
Soft Computing, Vol. 12, No. 8, pp. 2566–2582, August 2012.
328. A. Clarke and J.C. Miles, “Strategic Fire and Rescue Service decision making using evolutionary algorithms”, Advances
in Engineering Software, Vol. 50, pp. 29–36, August 2012.
329. Vicent Romero-Garcia, Juan Sanchez-Perez and Luis Miguel Garcia-Raffi, “Molding the Acoustic Attenuation in QuasiOrdered Structures: Experimental Realization”, Applied Physics Express, Vol. 5, No. 8, Article Number: 087301,
August 2012.
330. Pankaj Rajak, Sudipto Ghosh, Baidurya Bhattacharya and Nirupam Chakraborti, “Pareto-optimal analysis of Zn-coated
Fe in the presence of dislocations using genetic algorithms”, Computational Materials Science, Vol. 62, pp. 266–271,
September 2012.
331. Benedicte Quilot-Turion, Mohamed-Mahmoud Ould-Sidi, Abdeslam Kadrani, Nadine Hilgert, Michel Genard and Francoise Lescourret, “Optimization of parameters of the ‘Virtual Fruit’ model to design peach genotype for sustainable
production systems”, European Journal of Agronomy, Vol. 42, pp. 34–48, October 2012.
332. David Hadka and Patrick Reed, “Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective
Evolutionary Optimization”, Evolutionary Computation, Vol. 20, No. 3, pp. 423–452, Fall 2012.
333. Anne Auger, Johannes Bader, Dimo Brockhoff and Eckart Zitzler, “Hypervolume-based multiobjective optimization:
Theoretical foundations and practical implications”, Theoretical Computer Science, Vol. 425, pp. 75–103, March 30,
2012.
334. Wali Khan Mashwani and Abdellah Salhi, “A decomposition-based hybrid multiobjective evolutionary algorithm with
dynamic resource allocation”, Applied Soft Computing, Vol. 12, No. 9, pp. 2765–2780, September 2012.
335. F.R.B. Cruz, G. Kendall, L. While, A.R. Duarte and N.L.C. Brito, “Throughput Maximization of Queueing Networks
with Simultaneous Minimization of Service Rates and Buffers”, Mathematical Problems in Engineering, Article Number:
692593, 2012.
336. Rodrigo Coelho Barros, Marcio Porto Basgalupp, Andre C.P.L.F. de Carvalho and Alex A. Freitas, “A Survey of
Evolutionary Algorithms for Decision-Tree Induction”, IEEE Transactions on Systems, Man and Cybernetics Part C–
Applications and Reviews, Vol. 42, No. 3, pp. 291–312, May 2012.
337. Reinhard Koenig and Sven Schneider, “Hierarchical structuring of layout problems in an interactive evolutionary layout
system”, AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing, Vol. 26, No. 2, pp.
129–142, May 2012.
338. Clara Pizzuti, “A Multiobjective Genetic Algorithm to Find Communities in Complex Networks”, IEEE Transactions
on Evolutionary Computation, Vol. 16, No. 3, pp. 418–430, June 2012.
339. Amelia Zafra and Sebastian Ventura, “Multi-objective approach based on grammar-guided genetic programming for
solving multiple instance problems”, Soft Computing, Vol. 16, No. 6, pp. 955–977, June 2012.
340. Kaveh Khalili-Damghani abnd Maghsoud Amiri, “Solving binary-state multi-objective reliability redundancy allocation
series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA”,
Reliability Engineering & System Safety, Vol. 103, pp. 35–44, July 2012.
341. Davide Bianchi, Simone Genovesi and Agostino Monorchio, “Constrained Pareto Optimization of Wide Band and Steerable Concentric Ring Arrays”, IEEE Transactions on Antennas and Propagation, Vol. 60, No. 7, pp. 3195–3204, July
2012.
342. Renan S. Maciel, Mauro Rosa, Vladimiro Miranda and Antonio Padilha-Feltrin, “Multi-objective evolutionary particle
swarm optimization in the assessment of the impact of distributed generation”, Electric Power Systems Research, Vol.
89, pp. 100–108, August 2012.
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343. Satoshi Kitayama and Koetsu Yamazaki, “Compromise point incorporating trade-off ratio in multi-objective optimization”, Applied Soft Computing, Vol. 12, No. 8, pp. 1959–1964, August 2012.
344. Manuel Cruz-Ramirez, Cesar Hervas-Martinez, Juan Carlos Fernandez, Javier Briceno and Manuel de la Mata, “Multiobjective evolutionary algorithm for donor-recipient decision system in liver transplants”, European Journal of Operational Research, Vol. 222, No. 2, pp. 317–327, October 16, 2012.
345. C. Voglis, K.E. Parsopoulos, D.G. Papageorgiou, I.E. Lagaris and M.N. Vrahatis, “MEMPSODE: A global optimization
software based on hybridization of population-based algorithms and local searches”, Computer Physics Communications,
Vol. 183, No. 5, pp. 1139–1154, May 2012.
346. C. Fernandes, A.J. Pontes, J.C. Viana and A. Gaspar-Cunha, “Using Multi-objective Evolutionary Algorithms for
Optimization of the Cooling System in Polymer Injection Molding”, International Polymer Processing, Vol. 27, No. 2,
pp. 213–223, May 2012.
347. Gheorghe Serban, Laurentiu Ionescu and Alin Mazare, “The Possibility of Optimisation for Power Supply Consumption
using Evolvable Power Regulator”, Revue Roumaine des Sciences Techniques–Serie Electrotechnique et Energetique, Vol.
57, No. 2, pp. 222–231, April-June 2012.
348. Fangqing Gu, Hai-lin Liu and Kay Chen Tan, “A Multiobjective Evolutionary Algorithm using Dynamic Weight Design
Method”, International Journal of Innovative Computing Information and Control, Vol. 8, No. 5B, pp. 3677–3688, May
2012.
349. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Juan M. Herrero, “Multiobjective evolutionary algorithms
for multivariable PI controller design”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7895–7907, July 2012.
350. Youcef Bouchebaba, Ali-Erdem Ozcan, Pierre Paulin and Gabriela Nicolescu, “MpAssign: a framework for solving the
many-core platform mapping problem”, Software–Practice & Experience, Vol. 42, No. 7, pp. 891–915, July 2012.
351. Yakoub Bazi, Naif Alajlan and Farid Melgani, “Improved Estimation of Water Chlorophyll Concentration With Semisupervised Gaussian Process Regression”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 7, pp.
2733–2743, Part 2, July 2012.
352. Gustavo Olague and Leonardo Trujillo, “Interest point detection through multiobjective genetic programming”, Applied
Soft Computing, Vol. 12, No. 8, pp. 2566–2582, August 2012.
353. Ying Liu, Melody Kiang and Michael Brusco, “A unified framework for market segmentation and its applications”,
Expert Systems with Applications, Vol. 39, No. 11, pp. 10292–10302, September 1, 2012.
354. K. Metaxiotis and K. Liagkouras, “Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive
literature review”, Expert Systems with Applications, Vol. 39, No. 14, pp. 11685–11698, October 15, 2012.
355. Federico Divina, Beatriz Pontes, Raul Giraldez and Jesus S. Aguilar-Ruiz, “An effective measure for assessing the quality
of biclusters”, Computers in Biology and Medicine, Vol. 42, No. 2, pp. 245–256, February 2012.
356. Massimo Vecchio, Roberto Lopez-Valcarce and Francesco Marcelloni, “A two-objective evolutionary approach based on
topological constraints for node localization in wireless sensor networks”, Applied Soft Computing, Vol. 12, No. 7, pp.
1891–1901, July 2012.
357. Krzysztof Trawinski, Oscar Cordon and Arnaud Quirin, “A Study on the Use of Multiobjective Genetic Algorithms
for Classifier Selection in FURIA-based Fuzzy Multiclassifiers”, International Journal of Computational Intelligence
Systems, Vol. 5, No. 2, pp. 231–253, April 2012.
358. El-Ghazali Talbi, Matthieu Basseur, Antonio J. Nebro and Enrique Alba, “Multi-objective optimization using metaheuristics: non-standard algorithms”, International Transactions in Operational Research, Vol. 19, Nos. 1-2, pp. 283–
305, January-March 2012.
359. Helon Vicente Hultmann Ayala and Leandro dos Santos Coelho, “Tuning of PID controller based on a multiobjective
genetic algorithm applied to a robotic manipulator”, Expert Systems with Applications, Vol. 39, No. 10, pp. 8968–8974,
August 2012.
360. Dedi Liu, Shenglian Guo, Xiaohong Chen, Quanxi Shao, Qihua Ran, Xingyuan Song and Zhaoli Wang, “A macroevolutionary multi-objective immune algorithm with application to optimal allocation of water resources in Dongjiang
River basins, South China”, Stochastic Environmental Research and Risk Assessment, Vol. 26, No. 4, pp. 497–507, May
2012.
361. Yong Zhang, Dun-Wei Gong and Zhonghai Ding, “A bare-bones multi-objective particle swarm optimization algorithm
for environmental/economic dispatch”, Information Sciences, Vol. 192, pp. 213–227, June 1, 2012.
362. A. Weber, S. Fasoulas and K. Wolf, “Conceptual interplanetary space mission design using multi-objective evolutionary
optimization and design grammars”, Proceedings of the Institution of Mechanical Engineers Part G–Journal of Aerospace
Engineering, Vol. 225, No. G11, pp. 1253–1261, November 2011.
363. I.G.P. Asto Buditjahjanto and Hajime Miyauchi, “An Intelligent Decision Support Based on a Subtractive Clustering and
Fuzzy Inference System for Multiobjective Optimization Problem in Serious Game”, International Journal of Information
Technology & Decision Making, Vol. 10, No. 5, pp. 793–810, September 2011.
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364. A. Kaveh and K. Laknejadi, “A Hybrid Multi-Objective Optimization and Decision Making Procedure for Optimal
Design of Truss Structures”, Iranian Journal of Science and Technology–Transactions of Civil Engineering, Vol. 35, No.
C2, pp. 137–154, August 2011.
365. Juan J. Durillo and Antonio J. Nebro, “jMetal: A Java framework for multi-objective optimization”, Advances in
Engineering Software, Vol. 42, No. 10, pp. 760–771, October 2011.
366. Reza Akbari and Koorush Ziarati, “Multi-objective Bee Swarm Optimization”, International Journal of Innovative
Computing Information and Control, Vol. 8, No. 1B, pp. 715–726, January 2012.
367. Ali Kaveh, Karim Laknejadi and Babak Alinejad, “Performance-based multi-objective optimization of large steel structures”, Acta Mechanica, Vol. 223, No. 2, pp. 355–369, February 2012.
368. Khaled Badran and Peter Rockett, “Multi-class pattern classification using single, multi-dimensional feature-space feature
extraction evolved by multi-objective genetic programming and its application to network intrusion detection”, Genetic
Programming and Evolvable Machines, Vol. 13, No. 1, pp. 33–63, March 2012.
369. B. Naujoks, H. Trautmann, S. Wessing and C. Weihs, “Advanced concepts for multi-objective evolutionary optimization
in aircraft industry”, Proceedings of the Institution of Mechanical Engineers Part G–Journal of Aerospace Engineering,
Vol. 225, No. G10, pp. 1081–1096, October 2011.
370. Chiu-Hung Chen, Tung-Kuan Liu, I-Ming Huang and Jyh-Horng Chou, “Multiobjective Synthesis of Six-bar Mechanisms
Under Manufacturing and Collision-free Constraints”, IEEE Computational Intelligence Magazine, Vol. 7, No. 1, pp.
36–48, February 2012.
371. Arnaud Zinflou, Caroline Gagne and Marc Gravel, “GISMOO: A new hybrid genetic/immune strategy for multipleobjective optimization”, Computers & Operations Research, Vol. 39, No. 9, pp. 1951–1968, September 2012.
372. Khairy Elsayed and Chris Lacor, “Modeling and Pareto optimization of gas cyclone separator performance using RBF
type artificial neural networks and genetic algorithms”, Poweder Technology, Vol. 217, pp. 84–99, February 2012.
373. Yavuz Cengiz and Eray Konar, “Pareto-optimal synthesis of microwave amplifier to design the noise-constrained gain
value”, Microwave and Optical Technology Letters, Vol. 54, No. 4, pp. 1079–1084, April 2012.
374. T. Gomez, M. Hernandez, J. Molina, M.A. Leon, E. Aldana and R. Caballero, “A multiobjective model for forest
planning with adjacency constraints”, Annals of Operations Research, Vol. 190, No. 1, pp. 75–92, October 2011.
375. C.A. Garcia Montoya and S. Mendoza Toro, “Implementation of an evolutionary algorithm in planning investment in a
power distribution system”, Revista Ingenierı́a e Investigación, Vol. 31, Supplement: 2, pp. 118–124, 2011.
376. Sunith Bandaru and Kalyanmoy Deb, “Towards automating the discovery of certain innovative design principles through
a clustering-based optimization technique”, Engineering Optimization, Vol. 43, No. 9, pp. 911–941, 2011.
377. Shuo Xu, Ze Ji, Duc Truong Pham and Fan Yu, “Binary Bees Algorithm - bioinspiration from the foraging mechanism
of honeybees to optimize a multiobjective multidimensional assignment problem”, Engineering Optimization, Vol. 43,
No. 11, pp. 1141–1159, 2011.
378. Ben G. Small, Barry W. McColl, Richard Allmendinger, Jürgen Pahle, Gloria Lopez-Castejon, Nancy J. Rothwell, Joshua
Knowles, Pedro Mendes, David Brough and Doublas B. Kell, “Efficient discovery of anti-inflammatory small-molecule
combinations using evolutionary computing”, Nature Chemical Biology, Vol. 7, No. 12, pp. 902–908, December 2011.
379. Manojkumar Ramteke and Rajagopalan Srinivasan, “Novel genetic algorithm for short-term scheduling of sequence
dependent changeovers in multiproduct polymer plants”, Computers & Chemical Engineering, Vol. 35, No. 12, pp.
2945–2959, December 14, 2011.
380. Yun-Geun Lee, Bob Mckay, Kang-Il Kim, Dong-Kyun Kim and Nguyen Xuan Hoai, “Investigating vesicular selection A
selection operator in in vitro evolution”, Applied Soft Computing, Vol. 11, No. 8, pp. 5528–5550, December 2011.
381. Guillermo Molina, Francisco Luna, Antonio J. Nebro and Enrique Alba, “An efficient local improvement operator for
the multi-objective wireless sensor network deployment problem”, Engineering Optimization, Vol. 43, No. 10, pp.
1115–1139, 2011.
382. W.L. Wang, X.J. Yang, G.X. Xu and Y. Huang, “Multi-objective design optimization of the complete valve system
in an adjustable linear hydraulic damper”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of
Mechanical Engineering Science, Vol. 225, No. C3, pp. 679–699, 2011.
383. Jose L. Bernal-Agustin and Rodolfo Dufo-Lopez, “Simulation and optimization of stand-alone hybrid renewable energy
systems”, Renewable & Sustainable Energy Reviews, Vol. 13, No. 8, pp. 2111–2118, October 2009.
384. Kwang Mong Sim and Bo An, “Evolving Best-Response Strategies for Market-Driven Agents Using Aggregative Fitness
GA”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 39, No. 3, pp.
284–298, May 2009.
385. Md. Rafiul Hassan, Baikunth Nath, Michael Kirley and Joarde Kamruzzaman, “A hybrid of multiobjective Evolutionary
Algorithm and HMM-Fuzzy model for time series prediction”, Neurocomputing, Vol. 81, pp. 1–11, April 1, 2012.
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386. Kent McClymont and Ed Keedwell, “Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting”,
Evolutionary Computation, Vol. 20, No. 1, pp. 1–26, Spring 2012.
387. Teodor Marcu, Birgit Köppen-Seliger and Reinhard Stücher, “Design of fault detection for a hydraulic looper using
dynamic neural networks”, Control Engineering Practice, Vol. 16, No. 2, pp. 192–213, February 2008.
388. Christian Gagne and Marc Parizeau, “Coevolution of nearest neighbor classifiers”, International Journal of Pattern
Recognition and Artificial Intelligence, Vol. 21, No. 5, pp. 921–946, August 2007.
389. B.Y. Qu and P.N. Suganthan, “Constrained multi-objective optimization algorithm with an ensemble of constraint
handling methods”, Engineering Optimization, Vol. 43, No. 4, pp. 403–416, 2011.
390. Manojkumar Ramteke and Santosh K. Gupta, “Kinetic Modeling and Reactor Simulation and Optimization of Industrially Important Polymerization Processes: a Perspective”, International Journal of Chemical Reactor Engineering, Vol.
9, Article Number: R1, 2011.
391. Arnaud Liefooghe, Laetitia Jourdan and El-Ghazali Talbi, “A software framework based on a conceptual unified model
for evolutionary multiobjective optimization: ParadisEO-MOEO”, European Journal of Operational Research, Vol. 209,
No. 2, pp. 104–112, March 1, 2011.
392. Massimiliano Manfren, Paola Caputo and Gaia Costa, “Paradigm shift in urban energy systems through distributed
generation: Methods and models”, Applied Energy, Vol. 88, No. 4, pp. 1032–1048, April 2011.
393. P.M. Reed and J.B. Kollat, “Save now, pay later? Multi-period many-objective groundwater monitoring design given
systematic model errors and uncertainty”, Advances in Water Resources, Vol. 35, pp. 55–68, January 2012.
394. C.W. Bong and M. Rajeswari, “Multiobjective clustering with metaheuristic: current trends and methods in image
segmentation”, IET Image Processing, Vol. 6, No. 1, pp. 1–10, February 2012.
395. Douglas A.G. Vieira, Ricardo H.C. Takahashi and Rodney R. Saldanha, “Multicriteria optimization with a multiobjective
golden section line search”, Mathematical Programming, Vol. 131, Nos. 1-2, pp. 131–161, February 2012.
396. H. Kordabadi and A. Jahanmiri, “A pseudo-dynamic optimization of a dual-stage methanol synthesis reactor in the face
of catalyst deactivation”, Chemical Engineering and Processing, Vol. 46, No. 12, pp. 1299–1309, December 2007.
397. David Daum and Nicolas Morel, “Assessing the saving potential of blind controller via multi-objective optimization”,
Building Simulation, Vol. 2, No. 3, pp. 175–185, September 2009.
398. Wei-Mei Chen, Hsien-Kuei Hwang and Tsung-Hsi Tsai, “Maxima-finding algorithms for multidimensional samples: A
two-phase approach”, Computational Geometry–Theory and Applications, Vol. 45, Nos. 1-2, pp. 33–53, JanuaryFebruary 2012.
399. Lina Perelman, Avi Ostfeld and Elad Salomons, “Cross Entropy multiobjective optimization for water distribution
systems design”, Water Resources Research, Vol. 44, No. 9, Article Number: W09413, September 10, 2008.
400. Michael A. Trick and Hakan Yildiz, “Locally Optimized Crossover for the Traveling Umpire Problem”, European Journal
of Operational Research, Vol. 216, No. 2, pp. 286–292, January 16, 2012.
401. Diego P. Pinto-Roa, Benjamin Baran and Carlos A. Brizuela, “Routing and wavelength converter allocation in WDM
networks: a multi-objective evolutionary optimization approach”, Photonic Network Communications, Vol. 22, No. 1,
pp. 23–45, August 2011.
402. Marc Holze and Norbert Ritter, “System models for goal-driven self-management in autonomic databases”, Data &
Knowledge Engineering, Vol. 70, No. 8, pp. 685–701, August 2011.
403. Karsten Hentsch and Peter Köchel, “Job scheduling with forbidden setups and two objectives using genetic algorithms
and penalties”, Central European Journal of Operations Research, Vol. 19, No. 3, pp. 285–298, September 2011.
404. Renata Furtuna, Silvia Curteanu and Carmen Racles, “NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants”, Central European Journal of Chemistry, Vol. 9, No. 6, pp.
1080–1095, December 2011.
405. Ronghua Shang, Licheng Jiao, Fang Liu and Wenping Ma, “A Novel Immune Clonal Algorithm for MO Problems”,
IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 35–50, February 2012.
406. K.C. Tan, Q. Yu and J.H. Ang, “A dual-objective evolutionary algorithm for rules extraction in data mining”, Computational Optimization and Applications, Vol. 34, No. 2, pp. 273–294, June 2006.
407. J.B. Kollat, P.M. Reed and J.R. Kasprzyk, “A new epsilon-dominance hierarchical Bayesian optimization algorithm for
large multiobjective monitoring network design problems”, Advances in Water Resources, Vol. 31, No. 5, pp. 828–845,
May 2008.
408. Mohammed Shalaby and Kazuhiro Saitou, “Design for Disassembly With High-Stiffness Heat-Reversible Locator-Snap
Systems”, Journal of Mechanical Design, Vol. 130, No. 12, Article Number: 121701, December 2008.
409. B. Descamps, R. Filomeno Coelho, L. Ney and Ph. Bouillard, “Multicriteria optimization of lightweight bridge structures
with a constrained force density method”. Computers & Structures, Vol. 89, Nos. 3-4, pp. 277–284, February 2011.
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410. I-Tung Yang and Jui-Sheng Chou, “Multiobjective optimization for manpower assignment in consulting engineering
firms”, Applied Soft Computing, Vol. 11, No. 1, pp. 1183–1190, January 2011.
411. Hesham Kamel, Ramin Sedaghati and Mamoun Medraj, “Crashworthiness improvement of a pickup truck’s chassis frame
using the Pareto-Front and genetic algorithm”, International Journal of Heavy Vehicle Systems, Vol. 18, No. 1, pp.
83–103, 2011.
412. Wahabou Abdou, Adrien Henriet, Christelle Bloch, Dominique Dhoutaut, Damien Charlet and Francois Spies, “Using
an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks”, Journal of Network and
Computer Applications, Vol. 34, No. 6, pp. 1794–1804, November 2011.
413. Yongtai Huang and Lei Liu, “Multiobjective Water Quality Model Calibration Using a Hybrid Genetic Algorithm and
Neural Network-Based Approach”, Journal of Environmental Engineering–ASCE, Vol. 136, pp. 1020–1031, October
2010.
414. Bruno Urli and Francois Terrien, “Project portfolio selection model, a realistic approach”, International Transactions in
Operational Research, Vol. 17, No. 6, pp. 809–826, November 2010.
415. Roger M. Jarvis, William Rowe, Nicola R. Yaffe, Richard O’Connor, Joshua D. Knowles, Ewan W. Blanch and Royston
Goodacre, “Multiobjective evolutionary optimisation for surface-enhanced Raman scattering”, Analytical and Bioanalytical Chemistry, Vol. 397, No. 5, pp. 1893–1901, July 2010.
416. David Daum and Nicolas Morel, “Assessing the total energy impact of manual and optimized blind control in combination
with different lighting schedules in a building simulation environment”, Journal of Building Performance Simulation,
Vol. 3, No. 1, pp. 1–16, 2010.
417. Minqiang Li, Dan Lin and Shouyang Wang, “Solving a type of biobjective bilevel programming problem using NSGAII”,k Computers & Mathematics with Applications, Vol. 59, No. 2, pp. 706–715, January 2010.
418. Zhe Xu and Susan Lu, “Multi-objective optimization of sensor array using genetic algorithm”, Sensors and Actuators
B-Chemical, Vol. 160, No. 1, pp. 278–286, December 15, 2011.
419. Karim Hamza and Kazuhiro Saitou, “A Co-Evolutionary Approach for Design Optimization via Ensembles of Surrogates
With Application to Vehicle Crashworthiness”, Journal of Mechanical Design, Vol. 134, No. 1, Article Number: 011001,
January 2012.
420. K. Rodriguez-Vazquez, M.L. Arganis-Juarez, C. Cruickshank-Villanueva and R. Dominguez-Mora, “Rainfall-runoff modelling using genetic programming”, Journal of Hydroinformatics, Vol. 14, No. 1, pp. 108–121, January 2012.
421. Monica Carvalho, Miguel A. Lozano and Luis M. Serra, “Multicriteria synthesis of trigeneration systems considering
economic and environmental aspects”, Applied Energy, Vol. 91, No. 1, pp. 245–254, March 2012.
422. Peter A. N. Bosman, “On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization”,
IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 51–69, February 2012.
423. Rocio L. Cecchini, Ignacio Ponzoni and Jessica A. Carballido, “Multi-objective evolutionary approaches for intelligent
design of sensor networks in the petrochemical industry”, Expert Systems with Applications, Vol. 39, No. 3, pp. 2643–
2649, February 15, 2012.
424. Wenping Zou, Yunlong Zhu, Hanning Chen and Beiwei Zhang, “Solving Multiobjective Optimization Problems Using
Artificial Bee Colony Algorithm”, Discrete Dynamics in Nature and Society, Article Number: 569784, 2011.
425. Eduardo Fernandez Gonzalez, Edy Lopez Cervantes, Jorge Navarro Castillo and Ines Vega Lopez, “Application of MultiObjective Metaheuristics to Public Portfolio Selection Through Multidimensional Modelling of Social Return”, Gestion
y Politica Publica, Vol. 20, No. 2, pp. 381–432, 2011.
426. Shuang Wei and Henry Leung, “A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary
Multiobjective Optimization”, Mathematical Problems in Engineering, Article Number: 695087, 2011.
427. Joaquin Izquierdo, Idel Montalvo, Rafael Perez-Garcia and Agustin Matias, “On the Complexities of the Design of Water
Distribution Networks”, Mathematical Problems in Engineering, Vol. Article Number: 947961, 2012.
428. Karthik Sindhya, Kalyanmoy Deb and Kaisa Miettinen, “Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm”, Natural Computing, Vol. 10, No. 4, pp. 1407–1430,
December 2011.
429. Dan Zhang and Zhen Gao, “Hybrid head mechanism of the groundhog-like mine rescue robot”, Robotics and ComputerIntegrated Manufacturing, Vol. 27, No. 2, pp. 460–470, April 2011.
430. Weihong Li, Lijuan Liu and Weiguo Gong, “Multi-objective uniform design as a SVM model selection tool for face
recognition”, Expert Systems with Applications, Vol. 38, No. 6, pp. 6689–6695, June 2011.
431. Gustavo Olague and Leonardo Trujillo, “Evolutionary-computer-assisted design of image operators that detect interest
points using genetic programming”, Image and Vision Computing, Vol. 29, No. 7, pp. 484–498, June 2011.
432. Kejing Li and Xiaobing Zhang, “Multi-Objective Optimization of Interior Ballistic Performance Using NSGA-II”, Propellants Explosives Pyrotechnics, Vol. 36, No. 3, pp. 282–290, June 2011.
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433. Everardo Gutierrez and Carlos Brizuela, “An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem”,
International Journal of Computational Intelligence Systems, Vol. 4, No. 4, pp. 530–549, June-August 2011.
434. Shih-Pin Chen and Ming-Jiun Tsai, “Time-cost trade-off analysis of project networks in fuzzy environments”, European
Journal of Operational Research, Vol. 212, No. 2, pp. 386–397, July 16, 2011.
435. Kishalay Mitra and Sushanta Majumder, “Successive approximate model based multi-objective optimization for an
industrial straight grate iron ore induration process using evolutionary algorithm”, Chemical Engineering Science, Vol.
66, No. 15, pp. 3471–3481, August 1, 2011.
436. E. David Ford and Maureen C. Kennedy, “Assessment of uncertainty in functional-structural plant models”, Annals of
Botany, Vol. 108, No. 6, pp. 1043–1053, October 2011.
437. Antonio L. Marquez, Raul Banos, Consolacion Gil, Maria G. Montoya, Francisco Manzano-Agugliaro and Francisco G.
Montoya, “Multi-objective crop planning using pareto-based evolutionary algorithms”, Agricultural Economics, Vol. 42,
No. 6, pp. 649–656, November 2011.
438. Oscar Daniel Chuk and Benjamin R. Kuchen, “Supervisory control of flotation columns using multi-objective optimization”, Minerals Engineering, Vol. 24, No. 14, pp. 1545–1555, November 2011.
439. H. Komoto, T. Tomiyama, S. Silvester, and H. Brezet, “Analyzing supply chain robustness for OEMs from a life cycle
perspective using life cycle simulation”, International Journal of Production Economics, Vol. 134, No. 2, pp. 447–457,
December 2011.
440. Rajan Filomeno Coelho and Philippe Bouillard, “Multi-Objective Reliability-Based Optimization with Stochastic Metamodels”, Evolutionary Computation, Vol. 19, No. 4, pp. 525–560, Winter 2011.
441. Rafael Alcala, Yusuke Nojima, Francisco Herrera and Hisao Ishibuchi, “Multiobjective genetic fuzzy rule selection of
single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions”,
Soft Computing, Vol. 15, No. 12, pp. 2303–2318, December 2011.
442. Leonardo Trujillo, Gustavo Olague, Evelyne Lutton, Francisco Fernandez de Vega, Leon Dozal and Eddie Clemente,
“Speciation in Behavioral Space for Evolutionary Robotics”, Journal of Intelligent & Robotic Systems, Vol. 64, Nos.
3-4, pp. 323–351, December 2011.
443. Rajeev Kumar and Nilanjan Banerjee, “Multiobjective network topology design”, Applied Soft Computing, Vol. 11, No.
8, pp. 5120–5128, December 2011.
444. Ignacy Kaliszewski, J. Miroforidis and Dmitry Podkopaev, “Interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy”, European Journal of Operational
Research, Vol. 216, No. 1, pp. 188–199, January 1, 2012.
445. S. Afshin Mansouri, David Gallear and Mohammad H. Askariazad, “Decision support for build-to-order supply chain
management through multiobjective optimization”, International Journal of Production Economics, Vol. 135, No. 1,
pp. 24–36, January 2012.
446. Sultan Noman Qasem and Siti Mariyam Shamsuddin, “Memetic Elitist Pareto Differential Evolution algorithm based
Radial Basis Function Networks for classification problems”, Applied Soft Computing, Vol. 11, No. 8, pp. 5565–5581,
December 2011.
447. Hisao Ishibuchi, Yusuke Nakashima and Yusuke Nojima, “Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning”, Soft Computing, Vol. 15, No. 12, pp.
2415–2434, December 2011.
448. Ke Li, Sam Kwong, Jingjing Cao, Miqing Li, Jinhua Zheng and Ruimin Shen, “Achieving balance between proximity
and diversity in multi-objective evolutionary algorithm”, Information Sciences, Vol. 182, No. 1, pp. 220–242, January
1, 2012.
449. Andre B. de Carvalho and Aurora Pozo, “Measuring the convergence and diversity of CDAS Multi-Objective Particle
Swarm Optimization Algorithms: A study of many-objective problems”, Neurocomputing, Vol. 75, No. 1, pp. 43–51,
January 1, 2012.
450. H. Li and D. Landa-Silva, “An Adaptive Evolutionary Multi-Objective Approach Based on Simulated Annealing”,
Evolutionary Computation, Vol. 19, No. 4, pp. 561–595, Winter 2011.
451. Thomas Tometzki and Sebastian Engell, “Risk conscious solution of planning problems under uncertainty by hybrid
multi-objective evolutionary algorithms”, Computers & Chemical Engineering, Vol. 35, No. 11, pp. 2521–2539, November 15, 2011.
452. Hans-Friedrich Köhn, “A review of multiobjective programming and its application in quantitative psychology”, Journal
of Mathematical Psychology, Vol. 55, No. 5, pp. 386–396, October 2011.
453. Sai Ho Yeung and Kim Fung, “Multiobjective Optimization”, IEEE Microwave Magazine, Vol. 12, No. 6, pp. 120–133,
October 2011.
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454. A. Kaveh and K. Laknejadi, “A novel hybrid charge system search and particle swarm optimization method for multiobjective optimization”, Expert Systems with Applications, Vol. 38, No. 12, pp. 15475–15488, November-December
2011.
455. K.P. Anagnostopoulos and G. Mamanis, “The mean-variance cardinality constrained portfolio optimization problem:
An experimental evaluation of five multiobjective evolutionary algorithms”, Expert Systems with Applications, Vol. 38,
No. 11, pp. 14208–14217, October 2011.
456. Karthik Sindhya, Sauli Ruuska, Tomi Haanpää and Kaisa Miettinen, “A new hybrid mutation operator for multiobjective
optimization with differential evolution”, Soft Computing, Vol. 15, No. 10, pp. 2041–2055, October 2011.
457. Yi Chen, Yong Ma, Zheng Lu, Lixia Qiu and Jin He, “Terahertz spectroscopic uncertainty analysis for explosive mixture
components determination using multi-objective micro-genetic algorithm”, Advances in Engineering Software, Vol. 42,
No. 9, pp. 649–659, September 2011.
458. Emiliano Carreño Jara, “Long memory time series forecasting by using genetic programming”, Genetic Programming
and Evolvable Machines, Vol. 12, No. 4, pp. 429–456, December 2011.
459. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm Part
III Analysis of the results”, Polish Maritime Research, Vol. 18, No. 4, pp. 3–13, 2011.
460. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm Part
II Computational simulations”, Polish Maritime Research, Vol. 18, No. 3, pp. 3–30, 2011.
461. Zbigniew Sekulski, “Multi-objective topology and size optimization of high-speed vehicle-passenger catamaran structure
by genetic algorithm”, Marine Structures, Vol. 23, No. 4, pp. 405–433, October 2010.
462. Yong Zhang, Dun-wei Gong and Zhong-hai Ding, “Handling multi-objective optimization problems with a multi-swarm
cooperative particle swarm optimizer”, Expert Systems with Applications, Vol. 38, No. 11, pp. 13933–13941, October
2011.
463. Hisao Ishibuchi, Yuji Sakane, Noritaka Tsukamoto and Yusuke Nojima, “Implementation of cellular genetic algorithms
with two neighborhood structures for single-objective and multi-objective optimization”, Soft Computing, Vol. 15, No.
9, pp. 1749–1767, September 2011.
464. Rodolfo Dufo-Lopez, Jose L. Bernal-Agustin, Jose M. Yusta-Loyo, Jose A. Dominguez-Navarro, Ignacio J. RamirezRosado, Juan Lujano and Ismael Aso, “Multi-objective optimization minimizing cost and life cycle emissions of standalone PV-wind-diesel systems with batteries storage”, Applied Energy, Vol. 88, No. 11, pp. 4033–4041, November
2011.
465. I. Alberto and P.M. Mateo, “A crossover operator that uses Pareto optimality in its definition”, TOP, Vol. 19, No. 1,
pp. 67–92, July 2011.
466. Manuel Chica, Oscar Cordon and Sergio Damas, “An advanced multiobjective genetic algorithm design for the time and
space assembly line balancing problem”, Computers & Industrial Engineering, Vol. 61, No. 1, pp. 103–117, August
2011.
467. David R. White, Andrea Arcuri and John A. Clark, “Evolutionary Improvement of Programs”, IEEE Transactions on
Evolutionary Computation, Vol. 15, No. 4, pp. 515–538, August 2011.
468. Slim Bechikh, Lamjed Ben Said and Khaled Ghédira, “Searching for knee regions of the Pareto front using mobile
reference points”, Soft Computing, Vol. 15, No. 9, pp. 1807–1823, 2011.
469. Alvaro Luis Bustamante, José M. Molina López and Miguel A. Patricio, “MIJ2K Optimization using evolutionary
multiobjective optimization algorithms”, Expert Systems with Applications, Vol. 38, No. 9, pp. 10999–11010, September
2011.
470. Renata Furtuna, Silvia Curteanu and Florin Leon, “An elitist non-dominated sorting genetic algorithm enhanced with a
neural network applied to the multi-objective optimization of a polysiloxane synthesis process”, Engineering Applications
of Artificial Intelligence, Vol. 24, No. 5, pp. 772–785, August 2011.
471. Debanga Nandan Mondal, Kadambini Sarangi, Frank Pettersson, Prodip Kumar Sen, Henrik Saxen and Nirupam
Chakraborti, “Cu-Zn separation by supported liquid membrane analyzed through Multi-objective Genetic Algorithms”,
Hydrometallurgy, Vol. 107, Nos. 3-4, pp. 112–123, May 2011.
472. Oscar Cordon, “A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems”, International Journal of Approximate Reasoning, Vol. 52, No. 6, pp.
894–913, September 2011.
473. H. Moradi, M. Zandieh and Iraj Mahdavi, “Non-dominated ranked genetic algorithm for a multi-objective mixed-model
assembly line sequencing problem”, International Journal of Production Research, Vol. 49, No. 12, pp. 3479–3499, 2011.
474. Wei-Chang Yeh and Mei-Chi Chuang, “Using multi-objective genetic algorithm for partner selection in green supply
chain problems”, Expert Systems with Applications, Vol. 38, No. 4, pp. 4244–4253, April 2011.
475. Reza Ghaemi, Nasir bin Sulaiman, Hamidah Ibrahim and Norwati Mustapha, “A review: accuracy optimization in
clustering ensembles using genetic algorithms”, Artificial Intelligence Review, Vol. 35, No. 4, pp. 287–318, April 2011.
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476. Shafaq B. Chaudhry, Victor C. Hung, Ratan K. Guha and Kenneth O. Stanley, “Pareto-based evolutionary computational
approach for wireless sensor placement”, Engineering Applications of Artificial Intelligence, Vol. 24, No. 3, pp. 409–425,
April 2011.
477. H. Safikhani, M.A. Akhavan-Behabadi, N. Nariman-Zadeh and M.J. Mahmood Abadi, “Modeling and multi-objective
optimization of square cyclones using CFD and neural networks”, Chemical Engineering Research & Design, Vol. 89,
No. 3A, pp. 301–309, March 2011.
478. M.Sh. Levin and M.V. Petukhov, “Connection of Users with a Telecommunications Network: Multicriteria Assignment
Problem”, Journal of Communications Technology and Electronics, Vol. 55, No. 12, pp. 1532–1541, December 2010.
479. P. Ghobadi, M. Yahyaei and S. Banisi, “Optimization of the performance of flotation circuits using a genetic algorithm
oriented by process-based rules”, International Journal of Mineral Processing, Vol. 98, Nos. 3-4, pp. 174–181, March 9,
2011.
480. Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov and Evan Dekker, “Empirical evaluation methods for
multiobjective reinforcement learning algorithms”, Machine Learning, Vol. 84, Nos. 1-2, pp. 51–80, July 2011.
481. Luis A. Moncayo-Martinez and David Z. Zhang, “Multi-objective ant colony optimisation: A meta-heuristic approach
to supply chain design”, International Journal of Production Economics, Vol. 131, No. 1, pp. 407–420, May 2011.
482. M.P. Cuellar, S. Capel-Cuevas, M.C. Pegalajar, I. de Orbe-Paya and L.F. Capitan-Vallvey, “Minimization of sensing
elements for full-range optical pH device formulation”, New Journal of Chemistry, Vol. 35, No. 5, pp. 1042–1053, 2011.
483. B. Sankararao and Chang Kyoo Yoo, “Development of a Robust Multiobjective Simulated Annealing Algorithm for
Solving Multiobjective Optimization Problems”, Industrial & Engineering Chemistry Research, Vol. 50, No. 11, pp.
6728–6742, June 1, 2011.
484. Pankaj Rajak, Ujjal Tewary, Sumitesh Das, Baidurya Bhattacharya and Nirupam Chakraborti, “Phases in Zn-coated
Fe analyzed through an evolutionary meta-model and multi-objective Genetic Algorithms”, Computational Materials
Science, Vol. 50, No. 8, pp. 2502–2516, June 2011.
485. Itishree Mohanty, Debashish Bhattacharjee and Shubhabrata Datta, “Designing cold rolled IF steel sheets with optimized
tensile properties using ANN and GA”, Computational Materials Science, Vol. 50, No. 8, pp. 2331–2337, June 2011.
486. Marianne Boix, Ludovic Montastruc, Luc Pibouleau, Catherine Azzaro-Pantel and Serge Domenech, “A multiobjective
optimization framework for multicontaminant industrial water network design”, Journal of Environmental Management,
Vol. 92, No. 7, pp. 1802–1808, July 2011.
487. Zhanpeng Jin and Allen C. Cheng, “SubsetTrio: An Evolutionary, Geometric, and Statistical Benchmark Subsetting
Framework”, ACM Transactions on Modeling and Computer Simulation, Vol. 21, No. 3, Article Number: 21, March
2011.
488. Chien-Ho Ko and Shu-Fan Wang, “Precast production scheduling using multi-objective genetic algorithms”, Expert
Systems with Applications, Vol. 38, No. 7, pp. 8293–8302, July 2011.
489. Darrell F. Lochtefeld and Frank W. Ciarallo, “Helper-objective optimization strategies for the Job-Shop Scheduling
Problem”, Applied Soft Computing, Vol. 11, No. 6, pp. 4161–4174, September 2011.
490. Markus Hartikainen, Kaisa Miettinen and Margaret M. Wiecek, “Constructing a Pareto front approximation for decision
making”, Mathematical Methods of Operations Research, Vol. 73, No. 2, pp. 209–234, April 2011.
491. Ata Allah Taleizadeh, Farnaz Barzinpour and Hui-Ming Wee, “Meta-heuristic algorithms for solving a fuzzy single-period
problem”, Mathematical and Computer Modelling, Vol. 54, Nos. 5-6, pp. 1273–1285, September 2011.
492. Jiaquan Gao and Jun Wang, “A hybrid quantum-inspired immune algorithm for multiobjective optimization”, Applied
Mathematics and Computation, Vol. 217, No. 9, pp. 4754–4770, January 1, 2011.
493. Y.P. Ju and C.H. Zhang, “Multi-point and multi-objective optimization design method for industrial axial compressor
cascades”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical Engineering Science,
Vol. 225, No. C6, pp. 1481–1493, 2011.
494. A. Kundu and P.K. Dan, “The Scope of Genetic Algorithms in Dealing with Facility Layout Problems”, South African
Journal of Industrial Engineering, Vol. 21, No. 2, pp. 39–49, November 2010.
495. Ernesto Benini, Rita Ponza and Andrea Massaro, “High-Lift Multi-Element Airfoil Shape and Setting Optimization
Using Multi-Objective Evolutionary Algorithms”, Journal of Aircraft, Vol. 48, No. 2, pp. 683–696, March-April 2011.
496. Kishalay Mitra, “Handling Uncertainty in Kinetic Parameters in Optimal Operation of a Polymerization Reactor”,
Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 446–454, 2011.
497. Yu Wang, Bin Li and Yunbi Chen, “Digital IIR filter design using multi-objective optimization evolutionary algorithm”,
Applied Soft Computing, Vol. 11, No. 2, pp. 1851–1857, March 2011.
498. Yi Sun, Chaoyong Zhang, Liang Gao and Xiaojuan Wang, “Multi-objective optimization algorithms for flow shop
scheduling problem: a review and prospects”, International Journal of Advanced Manufacturing Technology, Vol. 55,
Nos. 5-8, pp. 723–739, July 2011.
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499. Ali R. Yildiz and Kazuhiro Saitou, “Topology Synthesis of Multicomponent Structural Assemblies in Continuum Domains”, Journal of Mechanical Design, Vol. 133, No. 1, Article Number: 011008, January 2011.
500. Yu Liang, XiaoQuan Cheng, ZhengNeng Li and JinWu Xiang, “Multi-objective robust airfoil optimization based on
non-uniform rational B-spline (NURBS) representation”, Science China-Technological Sciences, Vol. 53, No. 10, pp.
2708–2717, October, 2010.
501. Satoshi Kitayama, Masao Arakawa and Koetsu Yamazaki, “Differential evolution as the global optimization technique
and its application to structural optimization”, Applied Soft Computing, Vol. 11, No. 4, pp. 3792–3803, June 2011.
502. R.P. Dionisio, G. Parca, C. Reis and A.L. Teixeira, “Operational parameter optimisation of MZI-SOA using multiobjective genetic algorithms”, Electronics Letters, Vol. 47, No. 9, pp. 561–562, April 28, 2011.
503. Gustavo C.M. Ferreira, S.P.N. Cani, M.J. Pontes and M.E.V. Segatto, “Optimization of Distributed Raman Amplifiers
Using a Hybrid Genetic Algorithm With Geometric Compensation Technique”, IEEE Photonics Journal, Vol. 3, No. 3,
pp. 390–399, June 2011.
504. P. Rocca, G. Oliveri and A. Massa, “Differential Evolution as Applied to Electromagnetics”, IEEE Antennas and
Propagation Magazine, Vol. 53, No. 1, pp. 38–49, February 2011.
505. Yu Liang, Xiao-quan Cheng, Zheng-neng Li and Jin-wu Xiang, “Robust Multi-Objective Wing Design Optimization via
CFD Approximation Model”, Engineering Applications of Computational Fluid Mechanics, Vol. 5, No. 2, pp. 286–300,
June 2011.
506. Yann Cooren, Maurice Clerc and Patrick Siarry, “MO-TRIBES, an adaptive multiobjective particle swarm optimization
algorithm”, Computational Optimization and Applications, Vol. 49, No. 2, pp. 379–400, June 2011.
507. Miltiadis Kotinis, “Implementing co-evolution and parallelization in a multi-objective particle swarm optimizer”, Engineering Optimization, Vol. 43, No. 6, pp. 635–656, June 2011.
508. Karthik Sindhya and Kaisa Miettinen, “New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary
Multiobjective Algorithm”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 481–492, 2011.
509. Rajan Filomeno Coelho, Jeremy Lebon and Philippe Bouillard, “Hierarchical stochastic metamodels based on moving
least squares and polynomial chaos expansion”, Structural and Multidisciplinary Optimization, Vol. 43, No. 5, pp.
707–729, May 2011.
510. Debarati Kundu, Kaushik Suresh, Sayan Ghosh, Swagatam Das, B.K. Panigrahi and Sanjoy Das, “Multi-objective
optimization with artificial weed colonies”, Information Sciences, Vol. 181, No. 12, pp. 2441–2454, June 15, 2011.
511. Chin-Wei Bong and Mandava Rajeswari, “Multi-objective nature-inspired clustering and classification techniques for
image segmentation”, Applied Soft Computing, Vol. 11, No. 4, pp. 3271–3282, June 2011.
512. A. Castelletti, A.V. Lotov and R. Soncini-Sessa, “Visualization-based multi-objective improvement of environmental
decision-making using linearization of response surfaces”, Environmental Modelling & Software, Vol. 25, No. 12, pp.
1552–1564, December 2010.
513. Ruchit Shah and Patrick Reed, “Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems”, European Journal of Operational Research, Vol.
211, No. 3, pp. 466–479, June 16, 2011.
514. Salem F. Adra and Peter J. Fleming, “Diversity Management in Evolutionary Many-Objective Optimization”, IEEE
Transactions on Evolutionary Computation, Vol. 15, No. 2, pp. 183–195, April 2011.
515. Burcin Cakir, Fulya Altiparmak and Berna Dengiz, “Multi-objective optimization of a stochastic assembly line balancing:
A hybrid simulated annealing algorithm”, Computers & Industrial Engineering, Vol. 60, No. 3, pp. 376–384, April 2011.
516. Renan Cabrera, Ofer M. Shir, Rebing Wu and Herschel Rabitz, “Fidelity between unitary operators and the generation
of robust gates against off-resonance perturbations”, Journal of Physics A–Mathematical and Theoretical, Vol. 44, No.
9, Article Number 095302, March 4, 2011.
517. Nadia Nedjah, Marcus Vinicius Carvalho da Silva and Luiza de Macedo Mourelle, “Customized computer-aided application mapping on NoC infrastructure using multi-objective optimization”, Journal of Systems Architecture, Vol. 57, No.
1, pp. 79–94, January 2011.
518. James Bekker and Chris Aldrich, “The cross-entropy method in multi-objective optimisation: An assessment”, European
Journal of Operational Research, Vol. 211, No. 1, pp. 112–121, May 16, 2011.
519. Fatimah Sham Ismail, Rubiyah Yusof and Marzuki Khalid, “Self Organizing Multi-Objective Optimization Problem”,
International Journal of Innovative Computing Information and Control, Vol. 7, No. 1, pp. 301–314, January 2011.
520. Zhong-Zhong Jiang, W.H. Ip, H.C.W. Lau and Zhi-Ping Fan, “Multi-objective optimization matching for one-shot
multi-attribute exchanges with quantity discounts in E-brokerage”, Expert Systems with Applications, Vol. 38, No. 4,
pp. 4169–4180, April 2011.
521. K. Sivakumar, C. Balamurugan and S. Ramabalan, “Simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection”, Computer-Aided Design, Vol. 43, No. 2, pp. 207–218, February
2011.
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522. Prithwish Chakraborty, Swagatam Das, Gourab Ghosh Roy and Ajith Abraham, “On convergence of the multi-objective
particle swarm optimizers”, Information Sciences, Vol. 181, No. 8, pp. 1411–1425, April 15, 2011.
523. K. Sivakumar, C. Balamurugan and S. Ramabalan, “Concurrent multi-objective tolerance allocation of mechanical
assemblies considering alternative manufacturing process selection”, International Journal of Advanced Manufacturing
Technology, Vol. 53, Nos. 5–8, pp. 711–732, March 2011.
524. Magdalene Marinaki, Yannis Marinakis and Georgios E. Stavroulakis, “Fuzzy control optimized by a Multi-Objective
Particle Swarm Optimization algorithm for vibration suppression of smart structures”, Structural and Multidisciplinary
Optimization, Vol. 43, No. 1, pp. 29–42, January 2011.
525. Juan C. Vidal, Manuel Mucientes, Alberto Bugarı́n and Manuel Lama, “Machine scheduling in custom furniture industry
through neuro-evolutionary hybridization”, Applied Soft Computing, Vol. 11, No. 2, pp. 1600–1613, March 2011.
526. Mohammad Hamdan, “A dynamic polynomial mutation for evolutionary multi-objective optimization algorithms”, International Journal on Artificial Intelligence Tools, Vol. 20, No. 1, pp. 209–219, February 2011.
527. Kousik Deb and Anirban Dhar, “Optimum design of stone column-improved soft soil using multiobjective optimization
technique”, Computers and Geotechnics, Vol. 38, No. 1, pp. 50–57, January 2011.
528. Carlos R. Garcia-Alonso, Luis Salvador-Carulla, Miguel A. Negrin-Hernandez and Berta Moreno-Kustner, “Development
of a new spatial analysis tool in mental health: Identification of highly autocorrelated areas (hot-spots) of schizophrenia
using a Multiobjective Evolutionary Algorithm model (MOEA/HS)”, Epidemiologia E Psichiatria Sociale–An International Journal for Epidemiology and Psychiatric Sciences, Vol. 19, No. 4, pp. 302–313, October-December 2010.
529. Tsung-Che Chiang, Hsueh-Chien Cheng and Li-Chen Fu, “NNMA: An effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5986–5999,
May 2011.
530. J.B. Kollat, P.M. Reed and R.M. Maxwell, “Many-objective groundwater monitoring network design using bias-aware
ensemble Kalman filtering, evolutionary optimization, and visual analytics”, Water Resources Research, Vol. 47, Article
Number: W02529, February 18, 2011.
531. Lei Gao and Atakelty Hailu, “Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems”, International Journal of Computational Intelligence Systems, Vol. 3, No. 6, pp. 832–842, December
2010.
532. Minqiang Li, Liu Liu and Dan Lin, “A fast steady-state epsilon-dominance multi-objective evolutionary algorithm”,
Computational Optimization and Applications, Vol. 48, No. 1, pp. 109–138, January 2011.
533. Nguyen Binh Ta Duong, Suiping Zhou, Wentong Cai, Xueyan Tang and Rassul Ayani, “Multi-objective zone mapping
in large-scale distributed virtual environments”, Journal of Network and Computer Applications, Vol. 34, No. 2, pp.
551–561, March 2011.
534. F. Noori, M. Gorji, A. Kazemi and H. Nemati, “Thermodynamic optimization of ideal turbojet with afterburner engines
using non-dominated sorting genetic algorithm II”, Proceedings of the Institution of Mechanical Engineers Part G–Journal
of Aerospace Engineering, Vol. 224, No. G12, pp. 1285–1296, December 2010.
535. S.-Z. Zhao and P.N. Suganthan, “Two-lbests based multi-objective particle swarm optimizer”, Engineering Optimization,
Vol. 43, No. 1, pp. 1–17, January 2011.
536. H. Yapicioglu, H. Liu, A.E. Smith and G. Dozier, “Hybrid approach for Pareto front expansion in heuristics”, Journal
of the Operational Research Society, Vol. 62, No. 2, pp. 348–359, February 2011.
537. Clay Holdsworth, Minsun Kim, Jay Liao Mark H. Phillips, “A hierarchical evolutionary algorithm for multiobjective
optimization in IMRT”, Medical Physics, Vol. 37, No. 9, pp. 4986–4997, September 2010.
538. S.H. Yang, U. Natarajan, M. Sekar and S. Palani, “Prediction of surface roughness in turning operations by computer vision using neural network trained by differential evolution algorithm”, International Journal of Advanced Manufacturing
Technology, Vol. 51, Nos. 9–12, pp. 965–971, December 2010.
539. Isolina Alberto, Asuncion Beamonte, Pilar Gargallo, Pedro M. Mateo and Manuel Salvador, “Variable Selection in STAR
Models with Neighbourhood Effects Using Genetic Algorithms”, Journal of Forecasting, Vol. 29, No. 8, pp. 728–750,
December 2010.
540. Kyoung Seok Shin, Jong-Oh Park and Yeo Keun Kim, “Multi-objective FMS process planning with various flexibilities
using a symbiotic evolutionary algorithm”, Computers & Operations Research, Vol. 38, No. 3, pp. 702–712, March 2011.
541. Kalyanmoy Deb, Kaisa Miettinen and Shamik Chaudhuri, “Toward an Estimation of Nadir Objective Vector Using a
Hybrid of Evolutionary and Local Search Approaches”, IEEE Transactions on Evolutionary Computation, Vol. 14, No.
6, pp. 821–841, December 2010.
542. Christopher L. Simons, Ian C. Parmee and Rhys Gwynllyw, “Interactive, Evolutionary Search in Upstream ObjectOriented Class Design”, IEEE Transactions on Software Engineering, Vol. 36, No. 6, pp. 798–816, November-December
2010.
27
543. Aris Kornelakis, “Multiobjective Particle Swarm Optimization for the optimal design of photovoltaic grid-connected
systems”, Solar Energy, Vol. 84, No. 12, pp. 2022–2033, December 2010.
544. Ying Liu, Sudha Ram, Robert F. Lusch and Michael Brusco, “Multicriterion Market Segmentation: A New Model,
Implementation, and Evaluation”, Marketing Science, Vol. 29, No. 5, pp. 880–894, September-October 2010.
545. Dongdong Yang, Licheng Jiao, Maoguo Gong and Jie Feng, “Adaptive Ranks Clone and k-Nearest Neighbor List-Based
Immune Multi-Objective Optimization”, Computational Intelligence, Vol. 26, No. 4, pp. 359–385, November 2010.
546. J. Branke, S. Greco, R. Slowinski and P. Zielniewicz, “Interactive evolutionary multiobjective optimization driven by
robust ordinal regression”, Bulletin of the Polish Academy of Sciences–Technical Series, Vol. 58, No. 3, pp. 347–358,
September 2010.
547. Mariano Frutos, Ana Carolina Olivera and Fernando Tohme, “A memetic algorithm based on a NSGAII scheme for the
flexible job-shop scheduling problem”, Annals of Operations Research, Vol. 181, No. 1, pp. 745–765, December 2010.
548. Isis Didier Lins and Enrique Lopez Droguett, “Redundancy allocation problems considering systems with imperfect repairs using multi-objective genetic algorithms and discrete event simulation”, Simulation Modelling Practice and Theory,
Vol. 19, No. 1, pp. 362–381, January 2011.
549. Gift Dumedah, Aaron A. Berg, Mark Wineberg and Robert Collier, “Selecting Model Parameter Sets from a Trade-off
Surface Generated from the Non-Dominated Sorting Genetic Algorithm-II”, Water Resources Management, Vol. 24, No.
15, pp. 4469–4489, December 2010.
550. Massimiliano Kaucic, “Investment using evolutionary learning methods and technical rules”, European Journal of Operational Research, Vol. 207, No. 3, pp. 1717–1727, December 16, 2010.
551. Javier Sanchis, Miguel A. Martinez, Xavier Blasco and Gilberto Reynoso-Meza, “Modelling preferences in multi-objective
engineering design”, Engineering Applications of Artificial Intelligence, Vol. 23, No. 8, pp. 1255–1264, December 2010.
552. Mohammad Hamdan, “On the Disruption-Level of Polynomial Mutation for Evolutionary Multi-Objective Optimisation
Algorithms”, Computing and Informatics, Vol. 29, No. 5, pp. 783–800, 2010.
553. N. Bel Hadj Ali and I.F.C. Smith, “Dynamic behavior and vibration control of a tensegrity structure”, International
Journal of Solids and Structures, Vol. 47, No. 9, pp. 1285–1296, May 1, 2010.
554. Lionel Gueguen and Berna Sayrac, “Efficient Spectrum Sensing With Dyadic Tree Partitioning”, IEEE Transactions on
Vehicular Technology, Vol. 59, No. 4, pp. 1745–1759, May 2010.
555. Konstantinos B. Baltzis and John N. Sahalos, “Suboptimal Rake Finger Allocation: Performance and Complexity
Tradeoffs”, Journal of Electrical Engineering-Elektrotechnicky CASOPIS, Vol. 61, No. 2, pp. 107–113, March-April
2010.
556. F. Cosmi and B. Reggiani, “The optimization of parts within complex assemblies”, Proceedings of the Institution of
Mechanical Engineers Part C–Journal of Mechanical Engineering Science, Vol. 224, No. C4, pp. 969–979, 2010.
557. Maurizio Galetto and Barbara Pralio, “Optimal sensor positioning for large scale metrology applications”, Precision
Engineering—Journal of the International Societies for Precision Engineering and Nanotechnology, Vol. 34, No. 3, pp.
563–577, July 2010.
558. Nenzi Wang and Kuo-Chiang Cha, “Multi-objective optimization of air bearings using hypercube-dividing method”,
Tribology International, Vol. 43, No. 9, pp. 1631–1638, September 2010.
559. Anselmo Ramalho Pitombeira Neto and Eduardo Vila Goncalves Filho, “A simulation-based evolutionary multiobjective
approach to manufacturing cell formation”, Computers & Industrial Engineering, Vol. 59, No. 1, pp. 64–74, August
2010.
560. Konstantinos Delibasis, Pantelis A. Asvestas and George K. Matsopoulos, “Multimodal genetic algorithms-based algorithm for automatic point correspondence”, Pattern Recognition, Vol. 43, No. 12, pp. 4011–4027, December 2010.
561. A. Deihimi and H. Javaheri, “A Fuzzy Multi-Objective Multi-Case Genetic-Based Optimization for Allocation of FACTS
Devices to Improve System Static Security, Power Loss and Transmission Line Voltage Profiles”, International Review
of Electrical Engineering–IREE, Vol. 5, No. 4, pp. 1616–1626, Part B, July-August 2010.
562. Manuel Chica, Oscar Cordon, Sergio Damas and Joaquin Bautista, “Including different kinds of preferences in a multiobjective ant algorithm for time and space assembly line balancing on different Nissan scenarios”, Expert Systems with
Applications, Vol. 38, No. 1, pp. 709–720, January 2011.
563. E. Herrera-Viedma and A.G. Lopez-Herrera, “A Review on Information Accessing Systems Based on Fuzzy Linguistic
Modelling”, International Journal of Computational Intelligence Systems, Vol. 3, No. 4, pp. 420–437, October 2010.
564. Cristobal Jose Carmona, Pedro Gonzalez, Maria Jose del Jesus and Francisco Herrera, “NMEEF-SD: Non-dominated
Multiobjective Evolutionary Algorithm for Extracting Fuzzy Rules in Subgroup Discovery”, IEEE Transactions on Fuzzy
Systems, Vol. 18, No. 5, pp. 958–970, October 2010.
565. Ranjan Bhattacharya and Susmita Bandyopadhyay, “Solving conflicting bi-objective facility location problem by NSGA
II evolutionary algorithm”, International Journal of Advanced Manufacturing Technology, Vol. 51, Nos. 1–4, pp. 397–
414, November 2010.
28
566. Manuel E. Fernandez Garcia, Enrique A. Marin and Raquel Quiroga Garcia, “Improving return using risk-return adjustment and incremental training in technical trading rules with GAPs”, Applied Intelligence, Vol. 33, No. 2, pp. 93–106,
October 2010.
567. Celine Badufle, Christophe Blondel, Thierry Druot, Christian Bes, Jean-Baptiste Hiriart-Urruty, “A heuristic-based
framework to solve a complex aircraft sizing problem”, Engineering Applications of Artificial Intelligence, Vol. 23, No.
5, pp. 704–714, August 2010.
568. Ibrahim Karahan and Murat Köksalan, “A Territory Defining Multiobjective Evolutionary Algorithms and Preference
Incorporation”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 4, pp. 636–664, August 2010.
569. Lily Rachmawati and Dipti Srinivasan, “Incorporating the Notion of Relative Importance of Objectives in Evolutionary
Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 4, pp. 530–546, August
2010.
570. Kalyanmoy Deb, Amkur Sinha, Pekka J. Korhonen and Jyrki Wallenius, “An Interactive Evolutionary Multiobjective
Optimization Based on Progressively Approximated Value Functions”, IEEE Transactions on Evolutionary Computation,
Vol. 14, No. 5, pp. 723–739, October 2010.
571. Murat Köksalan and Ibrahim Karahan, “An Interactive Territory Defining Evolutionary Algorithm: iTDEA”, IEEE
Transactions on Evolutionary Computation, Vol. 14, No. 5, pp. 702–722, October 2010.
572. Guilherme P. Coelho, Ana Estela A. da Silva and Fernando J. Von Zuben, “An immune-inspired multi-objective approach
to the reconstruction of phylogenetic trees”, Neural Computing & Applications, Vol. 19, No. 8, pp. 1103–1132, November
2010.
573. Huidong Jin and Man-Leung Wong, “Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms”, Expert Systems with Applications, Vol. 37, No. 12, pp. 8462–8470, December 2010.
574. A. Agarwal, U. Tewary, F. Pettersson, S. Das, H. Saxen H and N. Chakraborti, “Analysing blast furnace data using
evolutionary neural network and multiobjective genetic algorithms”, Ironmaking & Steelmaking, Vol. 37, No. 5, pp.
353–359, July 2010.
575. J.C. Fernandez, C. Hervas, F.J. Martinez-Estudillo and P.A. Gutierrez, “Memetic Pareto Evolutionary Artificial Neural
Networks to determine growth/no-growth in predictive microbiology”, Applied Soft Computing, Vol. 11, No. 1, pp.
534–550, January 2011.
576. Krzysztof Kurowski, Ariel Oleksiak and Jan Weglarz, “Multicriteria, multi-user scheduling in grids with advance reservation”, Journal of Scheduling, Vol. 13, No. 5, pp. 493–508, October 2010.
577. F. Günes and F. Tokan, “Pareto Optimal Synthesis of the Linear Array Geometry for Minimum Side lobe Level and
Null Control During Beam Scanning”, International Journal of RF and Microwave Computer-Aided Engineering, Vol.
20, No. 5, pp. 557–566, September 2010.
578. Sultan Noman Qasem and Siti Mariyam Shamsuddin, “Radial basis function network based on time variant multiobjective particle swarm optimization for medical diseases diagnosis”, Applied Soft Computing, Vol. 11, No. 1, pp.
1427–1438, January 2011.
579. Marco Cococcioni, Beatrice Lazzerini and Francesco Marcelloni, “On reducing computational overhead in multi-objective
genetic Takagi-Sugeno fuzzy systems”, Applied Soft Computing, Vol. 11, No. 1, pp. 675–688, January 2011.
580. Gideon Avigad and Amiram Moshaiov, “Simultaneous concept-based evolutionary multi-objective optimization”, Applied
Soft Computing, Vol. 11, No. 1, pp. 193–207, January 2011.
581. Piotr Wozniak, “Preferences in multi-objective evolutionary optimisation of electric motor speed control with hardware
in the loop”, Applied Soft Computing, Vol. 11, No. 1, pp. 49–55, January 2011.
582. Coromoto Leon, Gara Miranda and Carlos Segura, “METCO: A Parallel Plugin-Based Framework for Multi-Objective
Optimization”, International Journal on Artificial Intelligence Tools, Vol. 18, No. 4, pp. 569–588, August 2009.
583. M.N. Neema and A. Ohgai, “Multi-objective location modeling of urban parks and open spaces: Continuous optimization”, Computers Environment and Urban Systems, Vol. 34, No. 5, pp. 359–376, August 2010.
584. N. Chakraborti, R. Sreevathsan, R. Jayakanth and B. Bhattacharya, “Tailor-made material design: An evolutionary
approach using multi-objective genetic algorithms”, Computational Materials Science, Vol. 45, No. 1, pp. 1–7, March
2009.
585. Hassan K. Abdulrahim and Fuad N. Alasfour, “Multi-Objective Optimisation of hybrid MSF-RO desalination system
using Genetic Algorithm”, International Journal of Exergy, Vol. 7, No. 3, pp. 387–424, 2010.
586. Maria Jose Gacto, Rafael Alcala and Francisco Herrera, “Integration of an Index to Preserve the Semantic Interpretability
in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems”, IEEE Transactions on Fuzzy
Systems, Vol. 18, No. 3, pp. 515–531, June 2010.
587. B. Cobacho, R. Caballero, M. Gonzalez and J. Molina, “Planning federal public investment in Mexico using multiobjective
decision making”, Journal of the Operational Research Society, Vol. 61, No. 9, pp. 1328–1339, September 2010.
29
588. Tomas Petkus, Ernestas Filatovas and Olga Kurasova, “Investigation of Human Factors while Solving Multiple Criteria
Optimization Problems in Computer Network”, Technological and Economic Development of Economy, Vol. 15, No. 3,
pp. 464–479, 2009.
589. S.H. Yang and U. Natarajan, “Multi-objective optimization of cutting parameters in turning process using differential
evolution and non-dominated sorting genetic algorithm-II approaches”, International Journal of Advanced Manufacturing
Technology, Vol. 49, Nos. 5–8, pp. 773–784, July 2010.
590. Jiaquan Gao, Lei Fang and Jun Wang, “A weight-based multiobjective immune algorithm: WBMOIA”, Engineering
Optimization, Vol. 42, No. 8, pp. 719–745, 2010.
591. Manuel Chica, Oscar Cordon, Sergio Damas and Joaquin Bautista, “Multiobjective constructive heuristics for the 1/3
variant of the time and space assembly line balancing problem: ACO and random greedy search”, Information Sciences,
Vol. 180, No. 18, pp. 3465–3487, September 15, 2010.
592. F. Günes and F. Tokan, “Pareto Optimal Synthesis of the Linear Array Geometry for Minimum Side lobe Level and
Null Control During Beam Scanning”, International Journal of RF and Microwave Computer-Aided Engineering, Vol.
20, No. 5, pp. 557–566, September 2010.
593. Yu Liang, XiaoQuan Cheng, ZhengNeng Li and JinWu Xiang, “Effect of cavity flame holder configuration on combustion
flow field performance of integrated hypersonic vehicle”, Science China–Technological Sciences, Vol. 53, No. 10, pp.
2708–2717, October 2010.
594. Shuo Xu, Ze Ji, Duc Troung Pham and Fan Yu, “Bio-Inspired Binary Bees Algorithm for a Two-Level Distribution
Optimisation Problem”, Journal of Bionic Engineering, Vol. 7, No. 2, pp. 161–167, June 2010.
595. John Nicklow, Patrick Reed, Dragan Savic, Tibebe Dessalegne, Laura Harrell, Amy Chan-Hilton, Mohammad Karamouz,
Barbara Minsker, Avi Ostfeld, Abhishek Singh and Emily Zechman, “State of the Art for Genetic Algorithms and Beyond
in Water Resources Planning and Management”, Journal of Water Resources Planning and Management–ASCE, Vol.
136, No. 4, pp. 412–432, July-August 2010.
596. Milica Selmic, Dusan Teodorovic and Katarina Vukadinovic, “Locating inspection facilities in traffic networks: an
artificial intelligence approach”, Transportation Planning and Technology, Vol. 33, No. 6, pp. 481–493, 2010.
597. C. Fernandes, A.J. Pontes, J.C. Viana and A. Gaspar-Cunha, “Using Multiobjective Evolutionary Algorithms in the
Optimization of Operating Conditions of Polymer Injection Molding”, Polymer Engineering and Science, Vol. 50, No.
8, pp. 1667–1678, August 2010.
598. T. Aittokoski and K. Miettinen, “Efficient evolutionary approach to approximate the Pareto-optimal set in multiobjective
optimization, UPS-EMOA”, Optimization Methods & Software, Vol. 25, No. 6, pp. 841–858, 2010.
599. Francisco Luna, Juan J. Durillo, Antonio J. Nebro and Enrique Alba, “Evolutionary algorithms for solving the automatic
cell planning problem: a survey”, Engineering Optimization, Vol. 42, No. 7, pp. 671–690, 2010.
600. Dudy Lim, Yaochu Jin, Yew-Soon Ong and Bernhard Sendhoff, “Generalizing Surrogate-Assisted Evolutionary Computation”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp. 329–355, June 2010.
601. Santosh Tiwari, Georges Fadel and Peter Fenyes, “A Fast and Efficient Compact Packing Algorithm for SAE and ISO
Luggage Packing Problems”, Journal of Computing and Information Science in Engineering, Vol. 10, No. 2, Article
Number 021010, June 2010.
602. M.T. Yazdani Sabouni, F. Jolai and A. Mansouri, “Heuristics for minimizing total completion time and maximum
lateness on identical parallel machines with setup times”, Journal of Intelligent Manufacturing, Vol. 21, No. 4, pp.
439–449, August 2010.
603. Abdelaziz Hammache, Marzouk Benali and Francois Aube, “Multi-objective self-adaptive algorithm for highly constrained problems: Novel method and applications”, Applied Energy, Vol. 87, No. 8, pp. 2467–2478, August 2010.
604. Jiaquan Gao, Lei Fang and Jun Wang, “A weight-based multiobjective immune algorithm: WBMOIA”, Engineering
Optimization, Vol. 42, No. 8, pp. 719–745, 2010.
605. Shang-Jeng Tsai, Tsung-Ying Sun, Chan-Cheng Liu, Sheng-Ta Hsieh, Wun-Ci Wu and Shih-Yuan Chiu, “An improved
multi-objective particle swarm optimizer for multi-objective problems”, Expert Systems with Applications, Vol. 37, No.
8, pp. 5872–5886, August 2010.
606. K. Salmalian, N. Nariman-Zadeh, H. Gharababei, H. Haftchenari and A. Varvani-Farahani, “Multi-objective evolutionary
optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibrereinforced plastics composites”, Proceedings of the Institution of Mechanical Engineers Part L–Journal of MaterialsDesign and Applications, Vol. 224, No. L2, pp. 79–91, 2010.
607. N. Nariman-Zadeh, M. Salehpour, A. Jamali and E. Haghgoo, “Pareto optimization of a five-degree of freedom vehicle
vibration model using a multi-objective uniform-diversity genetic algorithm (MUGA)”, Engineering Applications of
Artificial Intelligence, Vol. 23, No. 4, pp. 543–551, June 2010.
608. Hemant Kumar Singh, Tapabrata Ray and Warren Smith, “C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization”, Information Sciences, Vol. 180, No. 13, pp. 2499–2513, July 1, 2010.
30
609. Rocio L. Cecchini, Carlos M. Lorenzetti, Ana G. Maguitman and Nelida B. Brignole, “Multiobjective Evolutionary
Algorithms for Context-Based Search”, Journal of the American Society for Information Science and Technology, Vol.
61, No. 6, pp. 1258–1274, June 2010.
610. Juan Carlos Fernandez Caballero, Francisco Jose Martinez, Cesar Hervas and Pedro Antonio Gutierrez, “Sensitivity
Versus Accuracy in Multiclass Problems Using Memetic Pareto Evolutionary Neural Networks”, IEEE Transactions on
Neural Networks, Vol. 21, No. 5, pp. 750–770, May 2010.
611. Gideon Avigad, Erella Eisenstadt and Alexander Goldvard, “Pareto layer: Its formulation and search by way of evolutionary multi-objective optimization”, Engineering Optimization, Vol. 42, No. 5, pp. 453–470, 2010.
612. J. Dipama, A. Teyssedou, F. Aube and L. Lizon-A-Lugrin, “A grid based multi-objective evolutionary algorithm for the
optimization of power plants”, Applied Thermal Engineering, Vol. 30, Nos. 8-9, pp. 807–816, June 2010.
613. J.R. Figueira, A. Liefooghe, E.-G. Talbi and A.P. Wierzbicki, “A parallel multiple reference point approach for multiobjective optimization”, European Journal of Operational Research, Vol. 205, No. 2, pp. 390–400, September 1, 2010.
614. Leandro M. Almeida and Teresa B. Ludermir, “A multi-objective memetic and hybrid methodology for optimizing the
parameters and performance of artificial neural networks”, Neurocomputing, Vol. 73, Nos. 7-9, pp. 1438–1450, March
2010.
615. Yee Ming Chen and Wen-Shiang Wang, “Environmentally constrained economic dispatch using Pareto archive particle
swarm optimisation”, International Journal of System Science, Vol. 41, No. 5, pp. 593–605, 2010.
616. Jesica de Armas, Coromoto Leon, Gara Miranda and Carlos Segura, “Optimisation of a multi-objective two-dimensional
strip packing problem based on evolutionary algorithms”, International Journal of Production Research, Vol. 48, No. 7,
pp. 2011–2028, 2010.
617. Ujjwal Maulik and Anasua Sarkar, “Evolutionary Rough Parallel Multi-Objective Optimization Algorithm”, Fundamenta
Informaticae, Vol. 99, No. 1, pp. 13–27, 2010.
618. Xiaoning Shen, Yu Guo, Qingwei Chen and Weili Hu, “A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic”, Computational Optimization and Applications, Vol. 46, No. 1, pp.
159–188, May 2010.
619. H.L. Wang, S. Kwong, Y.C. Jin, W. Wei and K.F. Man, “Multi-objective hierarchical genetic algorithm for interpretable
fuzzy rule-based knowledge extraction”, Fuzzy Sets and Systems, Vol. 149, No. 1, pp. 149–186, January 1, 2005.
620. R. Kumar and P. Rockett, “Effective evolutionary multimodal optimization by multiobjective reformulation without
explicit niching/sharing”, Applied Computing, Proceedings, Springer-Verlag, Lecture Notes in Computer Science Vol.
3285, pp. 1–8, 2004.
621. Gerulf K.M. Pedersen and David E. Goldberg, “Dynamic Uniform Scaling for Multiobjective Genetic Algorithms”, in
Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic and
Evolutionary Computation Conference. Part II, Springer-Verlag, Lecture Notes in Computer Science Vol. 3103, pp.
11–23, Seattle, Washington, USA, June 2004.
622. Hisao Ishibuchi and Kaname Narukawa, “Some Issues on the Implementation of Local Search in Evolutionary Multiobjective Optimization”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004.
Proceedings of the Genetic and Evolutionary Computation Conference. Part I, Springer-Verlag, Lecture Notes in Computer Science Vol. 3102, pp. 1246–1258, Seattle, Washington, USA, June 2004.
623. Hisao Ishibuchi and Youhei Shibata, “Mating Scheme for Controlling the Diversity-Convergence Balance for Multiobjective Optimization”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004.
Proceedings of the Genetic and Evolutionary Computation Conference. Part I, Springer-Verlag, Lecture Notes in Computer Science Vol. 3102, pp. 1259–1271, Seattle, Washington, USA, June 2004.
624. A.G.D. Garza, A.P.T.C. Licastro and R.M.O. Justo, “A hybrid knowledge-based and evolutionary process model of
airport gate scheduling”, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Singapur, Vol.
12, pp. 43–61, Suppl. S., October 2004.
625. S. Gunawan, A. Farhang-Mehr and S. Azarm, “On maximizing solution diversity in a multiobjective multidisciplinary
genetic algorithm for design optimization”, Mechanics Based Design of Structures and Machines, Estados Unidos, Vol.
32, No. 4, pp. 491–514, November 2004.
626. Jürgen Branke, Kalyanmoy Deb, Henning Dierolf and Matthias Osswald, “Finding knees in multi-objective optimization”,
in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag, Lecture Notes in
Computer Science, Vol. 3242, pp. 722–731, September 2004.
627. Tapio Tyni and Jari Ylinen, “Evolutionary Bi-objective Controlled Elevator Group Regulates Passenger Service Level
and Minimises Energy Consumption”, in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII,
Springer-Verlag, Lecture Notes in Computer Science, Vol. 3242, pp. 822–831, September 2004.
628. Eckart Zitzler and Simon Künzli, “Indicator-based Selection in Multiobjective Search”, in Xin Yao et al. (editors),
Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag, Lecture Notes in Computer Science, Vol. 3242, pp.
832–842, September 2004.
31
629. Xiufen Zou, Minzhong Liu, Lishan Kang and Jun He, “A high performance multi-objective evolutionary algorithm based
on the principles of thermodynamics”, in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII,
Springer-Verlag, Lecture Notes in Computer Science, Vol. 3242, pp. 922–931, September 2004.
630. Yan Zhang, Kus Hidajat and Ajay K. Ray, “Optimal design and operation of SMB bioreactor: production of high fructose
syrup by isomerization of glucose”, Biochemical Engineering Journal, Suiza, Vol. 21, No. 2, pp. 111–121, October 2004.
631. Jerzy Balicki, “Multi-criterion Evolutionary Algorithm with Model of the Immune System to Handle Constraints for Task
Assignments”, in Leszek Rutkowski, Jörg H. Siekmann, Ryszard Tadeusiewicz and Lotfi A. Zadeh (Editors), Artificial
Intelligence and Soft Computing - ICAISC 2004, 7th International Conference. Proceedings, Springer. Lecture Notes in
Computer Science Vol. 3070, pp. 394–399, Zakopane, Poland, June 2004.
632. Thomas A. White and Douglas B. Kell, “Comparative genomic assessment of novel broad-spectrum targets for antibacterial drugs”, Comparative and Functional Genomics, Inglaterra, Vol. 5, pp. 304–327, 2004.
633. Enrique Dunn and Gustavo Olague, “Multi-objective Sensor Planning for Efficient and Accurate Object Reconstruction”,
in Günther R. Raidl et al. (editors), Applications of Evolutionary Computing. Proceedings of Evoworkshops 2004:
EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Springer. Lecture Notes in Computer
Science, Volume 3005, pp. 312–321, Coimbra, Portugal, April 2004.
634. A. Jaszkiewicz, “On the computational efficiency of multiple objective metaheuristics. The knapsack problem case
study”, European Journal of Operational Research, Holanda, Vol. 158, No. 2, pp. 418–433, October 16, 2004.
635. B.J. Ross and H. Zhu, “Procedural texture evolution using multi-objective optimization”, New Generation Computing,
Estados Unidos, Vol. 22, No. 3, pp. 271–293, 2004.
636. Matthieu Basseur, Julien Lemesre, Clarisse Dhaenens and El-Ghazali Talbi, “Cooperation between Branch and Bound
and Evolutionary Approaches to Solve a Bi-objective Flow Shop Problem”, in Proceedings of the Third International
Workshop on Experimental and Efficient Algorithms (WEA’04), pp. 72–86, Springer-Verlag, Lecture Notes in Computer
Science, Vol. 3059, Angra dos Reis, Brazil, May 2004.
637. Guan-Chun Luh and Chung-Huei Chueh, “Multi-objective optimal design of truss structure with immune algorithm”,
Computers & Structures, Inglaterra, Vol. 82, Nos. 11–12, pp. 829–844, May 2004.
638. Alvaro Gomes, Carlos Henggeler Antunes and Antonio Gomes Martins, “Dealing with solution diversity in an EA for
multiple objective decision support - A case study”, in Jens Gottlieb and Günter R. Raidl (editors), Evolutionary
Computation in Combinatorial Optimization, Proceedings of the 4th European Conference, EvoCOP 2004, Springer, pp.
104–113, Lecture Notes in Computer Science, Vol. 3004, April 2004.
639. Marco Laumanns, Lothar Thiele and Eckart Zitzler, “Running Time Analysis of Multiobjective Evolutionary Algorithms
on Pseudo-Boolean Functions”, IEEE Transactions on Evolutionary Computation, Vol. 8, No. 2, pp. 170–182, April
2004.
640. J. Duggan, J. Byrne and G.J. Lyons, “A task allocation optimizer for software construction”, IEEE Software, Vol. 21,
No. 3, pp. 76–82, May-June 2004.
641. P.M. Grignon and G.M. Fadel, “A GA based configuration design optimization method”, Journal of Mechanical Design,
Estados Unidos, Vol. 126, No. 1, pp. 6–15, January 2004.
642. M. Stan and B. Reardon, “A Bayesian approach to evaluating the uncertainty of thermodynamic data and phase
diagrams”, Calphad–Computer Coupling of Phase Diagrams and Thermochemistry, Inglaterra, Vol. 27, No. 3, pp.
319–323, September 2003.
643. R. Kumar, “Multicriteria network design using distributed evolutionary algorithm”, in High Performance Computing—
HIPC 2003, India, Springer-Verlag, Lecture Notes in Computer Science, Vol. 2913, pp. 343–352, 2003.
644. Aaron Hula, Kiumars Jalali, Karim Hamza, Steven J. Skerlos and Kazuhiro Saitou, “Multi-criteria Decision-Making for
Optimization of Product Disassembly under Multiple Situations”, Environmental Science & Technology, Estados Unidos,
Vol. 37, No. 23, pp. 5303–5313, December 1, 2003.
645. R. Cela, J.A. Martinez, C. Gonzalez-Barreiro and M. Lores, “Multi-objective optimisation using evolutionary algorithms:
its application to HPLC separations”, Chemometrics and Intelligent Laboratory Systems, Holanda, Vol. 69, Nos. 1-2,
pp. 137–156, November 28, 2003.
646. Kalyanmoy Deb, “Unveiling innovative design principles by means of multiple conflicting objectives”, Engineering Optimization, Inglaterra, Vol. 35, No. 5, pp. 445–470, October 2003.
647. W.M. Chen, H.K. Hwang and T.H. Tsai, “Efficient maxima-finding algorithms for random planar samples”, Discrete
Mathematics and Theoretical Computer Science, Vol. 6, No. 1, pp. 107–122, 2003.
648. R. Gras, D. Hernandez, P. Hernandez, N. Zangger, Y. Mescam, J. Frey, O. Martin, J. Nicolas and R.D. Appel, “Cooperative metaheuristics for exploring proteomic data”, Artificial Intelligence Review, Holanda, Vol. 20, Nos. 1–2, pp.
95–120, October 2003.
32
649. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical design with constraints and preferences: application
to a purge valve”, Computer Methods in Applied Mechanics and Engineering, Suiza, Vol. 192, Nos. 39–40, pp. 4355–4378,
2003.
650. Rajeev Kumar and Nilanjan Banerjee, “Multicriteria Network Design Using Evolutionary Algorithm”, in Erick CantúPaz et al. (editors), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part II, pp. 2179–2190,
Springer. Lecture Notes in Computer Science Vol. 2724, July 2003.
651. Hisao Ishibuchi and Youhei Shibata, “A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization”,
in Erick Cantú-Paz et al. (editors), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part I, pp.
1065–1076, Springer. Lecture Notes in Computer Science Vol. 2723, July 2003.
652. Robin C. Purshouse and Peter J. Fleming, “Conflict, Harmony, and Independence: Relationships in Evolutionary Multicriterion Optimisation”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele
(editors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 16–30, Springer.
Lecture Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.
653. F. de Negro, J. Ortega, E. Ros, S. Mota, B. Paechter and J.M. Martı́n, “PSFGA: Parallel processing and evolutionary
computation for multiobjective optimisation”, Parallel Computing, Holanda, Vol. 30, Nos. 5–6, pp. 721–739, May-June
2004.
654. Hisao Ishibuchi and Youhei Shibata, “An Empirical Study on the Effect of Mating Restriction on the Search Ability
of EMO Algorithms”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 433–447, Springer.
Lecture Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.
655. Michael Guntsch and Martin Middendort, “Solving Multi-criteria Optimization Problems with Population-Based ACO”,
in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary
Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 464–478, Springer. Lecture Notes in
Computer Science. Volume 2632, Faro, Portugal, April 2003.
656. Kalyanmoy Deb, Pawan Zope and Abhishek Jain, “Distributed Computing of Pareto-Optimal Solutions with Evolutionary Algorithms”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors),
Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 534–549, Springer. Lecture Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.
657. Carlos A. Brizuela and Rodrigo Aceves, “Experimental Genetic Operators Analysis for the Multi-objective Permutation
Flowshop”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 578–592, Springer. Lecture
Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.
658. A. Gaspar-Cunha and J.A. Covas, “A Real-World Test Problem for EMO Algorithms”, in Carlos M. Fonseca, Peter
J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization.
Second International Conference, EMO 2003, pp. 752–766, Springer. Lecture Notes in Computer Science. Volume 2632,
Faro, Portugal, April 2003.
659. R.M. Hubley, E. Zitzler and J.C. Roach, “Evolutionary algorithms for the selection of single nucleotide polymorphisms”,
BMC Bioinformatics, Inglaterra, Vol. 4, Art. No. 30, July 23, 2003.
660. Andrea Toffolo and Ernesto Benini, “Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms”,
Evolutionary Computation, Estados Unidos, Vol. 11, No. 2, pp. 151–167, Summer 2003.
661. Hisao Ishibuchi, Tadashi Yoshida and Tadahiko Murata, “Balance Between Genetic Search and Local Search in Memetic
Algorithms for Multiobjective Permutation Flowshop Scheduling”, IEEE Transactions on Evolutionary Computation,
Estados Unidos, Vol. 7, No. 2, pp. 204–223, April 2003.
662. T. Wright, V.J. Gillet, D.V.S. Green and S.D. Pickett, “Optimizing the size and configuration of combinatorial libraries”,
Journal of Chemical Information and Computer Sciences, Estados Unidos, Vol. 43, No. 2, pp. 381–390, March-April
2003.
663. K.C. Tan, E.F. Khor, T.H. Lee and Y.J. Yang, “A tabu-based exploratory evolutionary algorithm for multiobjective
optimization”, Artificial Intelligence Review, Holanda, Vol. 19, No. 3, pp. 231–260, May 2003.
664. D. Kim, “Evolving internal memory for T-maze tasks in noisy environments”, Connection Science, Inglaterra, Vol. 16,
No. 3, pp. 183–210, September 2004.
665. M. Farina and P. Amato, “Linked interpolation-optimization strategies for multicriteria optimization problems”, Soft
Computing–A Fusion of Foundations, Methodologies and Applications, Springer-Verlag, Vol. 9, No. 1, pp. 54–65,
January 2005.
666. Hussein A. Abbass, “An Inexpensive Cognitive Approach for Biobjective Optimization using Bliss Points and Interaction”, in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag, Lecture Notes in
Computer Science, Vol. 3242, pp. 712–721, September 2004.
33
667. Frank Schlottmann and Detlef Seese, “A hybrid heuristic approach to discrete multi-objective optimization of credit
portfolios”, Computational Statistics & Data Analysis, Holanda, Vol. 47, No. 2, pp. 373–399, September 1, 2004.
668. Tatsuya Okabe, Yaochu Jin, Markus Olhofer and Bernhard Sendhoff, “On Test Functions for Evolutionary MultiObjective Optimization”, in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag,
Lecture Notes in Computer Science, Vol. 3242, pp. 792–802, September 2004.
669. J. Ku, X.J. Feng and H. Rabitz, “Closed-loop learning control of bio-networks”, Journal of Computational Biology,
Estados Unidos, Vol. 11, No. 4, pp. 642–659, 2004.
670. Carlos Garcı́a-Martinez, Oscar Cordón and Francisco Herrera, “An Empirical Analysis of Multiple Objective Ant Colony
Optimization Algorithms for the Bi-criteria TSP”, in Marco Dorigo, Mauro Birattari, Christian Blum, Luca M. Gambardella, Francesco Mondada and Thomas Stützle (editors), Proceedings of the 4th International Workshop on Ant
Colony Optimization and Swarm Intelligence, ANTS 2004, Bélgica, Springer, Lecture Notes in Computer Science, Vol.
3172, pp. 61–72, 2004.
671. D. Greiner, J.M. Emperador and G. Winter, “Single and multiobjective frame optimization by evolutionary algorithms
and the auto-adaptive rebirth operator”, Computer Methods in Applied Mechanics and Engineering, Suiza, Vol. 193,
Nos. 33–35, pp. 3711–3743, 2004.
672. R.B. Kasat and S.K. Gupta, “Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU)
using genetic algorithm (GA) with the jumping genes operator”, Computers & Chemical Engineering, Inglaterra, Vol.
27, No. 12, pp. 1785–1800, December 15, 2003.
673. Eric M. Koper, William D. Wood and Stephen W. Schneider, “Aircraft antenna coupling minimization using genetic
algorithms and approximations”, IEEE Transactions on Aerospace and Electronic Systems, Estados Unidos, Vol. 40,
No. 2, pp. 742–751, April 2004.
674. J. Mehnen, T. Micheltisch, T. Bartz-Beielstein and K. Schmitt, “Evolutionary optimization of mould temperature control
strategies: encoding and solving the multiobjective problem with standard evolution strategy and kit for evolution algorithms”, Proceedings of the Institution of Mechanical Engineers Part B—Journal of Engineering Manufacture, Inglaterra,
Vol. 218, No. 6, pp. 657–665, June 2004.
675. Karim Hamza and Kazuhiro Saitou, “Optimization of Constructive Solid Geometry Via a Tree-Based Multi-objective
Genetic Algorithm”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part II, Springer-Verlag, Lecture Notes in Computer
Science Vol. 3103, pp. 981–992, Seattle, Washington, USA, June 2004.
676. Julien Frey, Robin Gras, Patricia Hernandez and Ron Appel, “A hierarchical model of parallel genetic programming
applied to bioinformatic problems”, in Roman Wyrzykowski, Jack Dongarra, Marcin Paprzycki et al. (editors) Parallel
Processing and Applied Mathematics: 5th International Conference (PPAM 2003), Polonia, Springer, Lecture Notes in
Computer Science Vol. 3019, pp. 1146–1153, 2003.
677. P. Morillo, J.M. Orduña and M. Fernández, “A comparison study of evolutive algorithms for solving the partitioning
problem in distributed virtual environment systems”, Parallel Computing, Holanda, Vol. 30, Nos. 5–6, pp. 585–610,
May-June 2004.
678. A. Suppapitnarm, G.T. Parks, K. Shea and P.J. Clarkson, “Conceptual Design of Bicycle Frames by Multiobjective
Shape Annealing”, Engineering Optimization, Vol. 36, No. 2, pp. 165–188, April 2004.
679. H. Ishibuchi and T. Yamamoto, “Interpretability issues in fuzzy genetics-based machine learning for linguistic modelling”,
in Modelling with Words: Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework,
Springer-Verlag, Lecture Notes in Artificial Intelligence, Vol. 2873, pp. 209–228, 2003.
680. Hussein A. Abbass, “Pareto neuro-ensembles”, AI 2003: Advances in Artificial Intelligence, Australia, Lecture Notes in
Artificial Intelligence, Vol 2903, pp. 554–566, 2003.
681. O. Cordon, F. Gomide, F. Herrera, F. Hoffmann and L. Magdalena, “Ten years of genetic fuzzy systems: current
framework and new trends”, Fuzzy Sets and Systems, Holanda, Vol. 141, No. 1, pp. 5–31, January 1, 2004.
682. O. Cordon, E. Herrera-Viedma, M. Luque, F. de Moya and C. Zarco, “Analyzing the performance of a multiobjective
GA-P algorithm for learning fuzzy queries in a machine learning environment”, in Proceedings of Fuzzy Sets and Systems
(IFSA 2003), Turquı́a, Springer, Lecture Notes in Artificial Intelligence, Vol. 2715, pp. 611–619, 2003.
683. H.A. Abbass, “Speeding up backpropagation using multiobjective evolutionary algorithms”, Neural Computation, Vol.
15, No. 11, pp. 2705–2726, November 2003.
684. Xavier Llorà and David E. Goldberg, “Bounding the Effect of Noise in Multiobjective Learning Classifier Systems”,
Evolutionary Computation, Estados Unidos, Vol. 11, No. 3, pp. 279–298, Fall 2003.
685. Karim Hamza, Juan F. Reyes-Luna and Kazuhiro Saitou, “Simultaneous Assembly Planning and Assembly System
Design Using Multi-objective Genetic Algorithms”, in Erick Cantú-Paz et al. (editors), Genetic and Evolutionary
Computation—GECCO 2003. Proceedings, Part II, pp. 2096–2108, Springer. Lecture Notes in Computer Science Vol.
2724, July 2003.
34
686. Martin Brown and Robert E. Smith, “Effective Use of Directional Information in Multi-objective Evolutionary Computation”, in Erick Cantú-Paz et al. (editors), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part
I, pp. 778–789, Springer. Lecture Notes in Computer Science Vol. 2723, July 2003.
687. Andrew Wildman and Geoff Parks, “A Comparative Study of Selective Breeding Strategies in a Multiobjective Genetic
Algorithm”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 418–432, Springer. Lecture
Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.
688. Andrzej Jaszkiewicz, “Do Multiple-Objective Metaheuristics Deliver on Their Promises? A Computational Experiment
on the Set-Covering Problem”, IEEE Transactions on Evolutionary Computation, Vol. 7, No. 2, pp. 133–143, April
2003.
689. S. O’Hagan, W.B. Dunn, M. Brown, J.D. Knowles and D.B. Kell, “Closed-loop, multiobjective optimization of analytical
instrumentation: Gas chromatography / time-of-flight mass spectrometry of the metabolomes of human serum and of
yeast fermentations”, Analytical Chemistry, Vol. 77, No. 1, pp. 290–303, January 1, 2005.
690. Taghi M. Khoshgoftaar, Yi Liu and Naeem Seliya, “A Multiobjective Module-Order Model for Software Quality Enhancement”, IEEE Transactions on Evolutionary Computation, Estados Unidos, Vol. 8, No. 6, pp. 593–608, December
2004.
691. H. Aguirre and K. Tanaka, “Random bit climbers on multiobjective MNK-Landscapes: Effects of memory and population
climbing”, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, Vol. E88A,
No. 1, pp. 334–345, January 2005.
692. S. Gunawan and S. Azarm, “Multi-objective robust optimization using a sensitivity region concept”, Structural and
Multidisciplinary Optimization, Vol. 29, No. 1, pp. 50–60, January 2005.
693. R.F. Coelho and P. Bouillard, “A multicriteria evolutionary algorithm for mechanical design optimization with expert
rules”, International Journal for Numerical Methods in Engineering, Vol. 62, No. 4, pp. 516–536, January 28, 2005.
694. D.G. Mayer, B.P. Kinghorn and A.A. Archer, “Differential evolution - an easy and efficient evolutionary algorithm for
model optimisation”, Agricultural Systems, Vol. 83, No. 3, pp. 315–328, March 2005.
695. L. Luo, P.K. Kannan, B. Besharati and S. Azarm, “Design of robust new products under variability: Marketing meets
design”, Journal of Product Innovation Management, Vol. 22, No. 2, pp. 177–192, March 2005.
696. R. Kumar, R.K. Singh and P.P. Chakrabarti, “Improved quality of solutions for multiobjective spanning tree problem
using distributed evolutionary algorithm”, High Performance Computing - HIPC 2004, Springer-Verlag, Lecture Notes
in Computer Science Vol. 3296, pp. 494–503, 2004.
697. L. Samaniego and A. Bardossy, “Robust parametric models of runoff characteristics at the mesoscale”, Journal of
Hydrology, Vol. 303, Nos. 1-4, pp. 136–151, March 1, 2005.
698. Hui Li and Qingfu Zhang, “Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGAII”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 2, pp. 284–302, April 2009.
699. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “Performance assessment of multicriteria damage identification
genetic algorithms”, Computers & Structures, Vol. 87, Nos. 1-2, pp. 120–127, January 2009.
700. Asish Kumar Sharma, Chandramouli Kulshreshtha, Keemin Sohn and Kee-Sun Sohn, “Systematic Control of Experimental Inconsistency in Combinatorial Materials Science”, Journal of Combinatorial Chemistry, Vol. 11, No. 1, pp.
131–137, January-February 2009.
701. Wasim Raza and Kwang-Yong Kim, “Shape Optimization of 19-Pin Wire-Wrapped Fuel Assembly of LMR Using Multiobjective Evolutionary Algorithm”, Nuclear Science and Engineering, Vol. 161, No. 2, pp. 245–254, February 2009.
702. Kishalay Mitra, Sushanta Majumder and Venkatarama Runkana, “Multiobjective Pareto Optimization of an Industrial
Straight Grate Iron Ore Induration Process Using an Evolutionary Algorithm”, Materials and Manufacturing Processes,
Vol. 24, No. 3, pp. 331–342, 2009.
703. Andres L. Medaglia, Juan G. Villegas and Diana M. Rodriguez-Coca, “Hybrid biobjective evolutionary algorithms for
the design of a hospital waste management network”, Journal of Heuristics, Vol. 15, No. 2, pp. 153–176, April 2009.
704. M. Katebi, H. Tawfik and S.D. Katebi, “Limit Cycle Prediction Based on Evolutionary Multiobjective Formulation”,
Mathematical Problems in Engineering, Article Number 816707, 2009.
705. Severino F. Galán and Ole J. Mengshoel, “Constraint Handling Using Tournament Selection: Abductive Inference in
Partly Deterministic Bayesian Networks”, Evolutionary Computation, Vol. 17, No. 1, pp. 55–88, Spring 2009.
706. R. Baños, C. Gil, J. Reca and J. Martı́nez, “Implementation of scatter search for multi-objective optimization: a
comparative study”, Computational Optimization and Applications, Vol. 42, No. 3, pp. 421–441, April 2009.
707. Eduardo Raul Hruschka, Ricardo J.G.B. Campello, Alex A. Freitas, Andre C. Ponce de Leon F. de Carvalho, “A Survey
of Evolutionary Algorithms for Clustering”, IEEE Transactions on Systems, Man, and Cybernetics Part C—Applications
and Reviews, Vol. 39, No. 2, pp. 133–155, March 2009.
35
708. Hisao Ishibuchi, Yasuhiro Hitotsuyanagi, Noritaka Tsukamoto and Yusuke Nojima, “Use of biased neighborhood structures in multiobjective memetic algorithms”, Soft Computing, Vol. 13, Nos. 8–9, pp. 795–810, July 2009.
709. S.C. Chiam, K.C. Tan, C.K. Goh and A. Al Mamun, “Improving locality in binary representation via redundancy”,
IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 38, No. 3, pp. 808–825, June 2008.
710. Rafael Munoz-Salinas, Eugenio Aguirre, Oscar Cordon and Miguel Garcia-Silvente, “Automatic tuning of a fuzzy visual
system using evolutionary-algorithms: Single-objective versus multiobjective approaches”, IEEE Transactions on Fuzzy
Systems, Vol. 16, No. 2, pp. 485–501, April 2008.
711. Joana Dias, M. Eugenia Captivo and Joao Climaco, “A memetic algorithm for multi-objective dynamic location problems”, Journal of Global Optimization, Vol. 42, No. 2, pp. 221–253, October 2008.
712. Feili Yu, Fang Tu, Krishna R. Pattipati, “Integration of a holonic organizational control architecture and multiobjective
evolutionary algorithm for flexible distributed scheduling”, IEEE Transactions on Systems, Man, and Cybernetics Part
A–Systems and Humans, Vol. 38, No. 5, pp. 1001–1017, September 2008.
713. Christian Gagne, Julie Beaulieu, Marc Parizeau and Simon Thibault, “Human-competitive lens system design with
evolution strategies”, Applied Soft Computing, Vol. 8, No. 4, pp. 1439–1452, September 2008.
714. Leandro dos Santos Coelho and Piergiorgio Alotto, “Multiobjective electromagnetic optimization based on a nondominated sorting genetic approach with a chaotic crossover operator”, IEEE Transactions on Magnetics, Vol. 44, No. 6,
pp. 1078–1081, June 2008.
715. Laetitia Jourdan, Oliver Schuetze, Thomas Legrand, El-Ghazali Talbi and Jean Luc Wojkiewicz, “An Analysis of the Effect of Multiple Layers in the Multi-Objective Design of Conducting Polymer Composites”, Materials and Manufacturing
Processes, Vol. 24, No. 3, pp. 350–357, 2009.
716. Oliver Schuetze, Laetitia Jourdan, Thomas Legrand, El-Ghazali Talbi and Jean-Luc Wojkiewicz, “New analysis of
the optimization of electromagnetic shielding properties using conducting polymers and a multi-objective approach”,
Polymers for Advanced Technologies, Vol. 19, No. 7, pp. 762–769, July 2008.
717. Frank Pettersson, Arijit Biswas, Prodip Kumar Sen, Henrik Saxén and Nirupam Chakraborti, “Analyzing Leaching Data
for Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms”, Materials and Manufacturing
Processes, Vol. 24, No. 3, pp. 320–330, March 2009.
718. Akash Agarwal, Frank Pettersson, Arunima Singh, Chang Sun Kong, Henrik Saxén, Krishna Rajan, Shuichi Iwata and
Nirupam Chakraborti, “Identification and Optimization of AB2 Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms”, Materials and Manufacturing Processes, Vol. 24, No. 3, pp.
274–281, March 2009.
719. Oliver Schütze, Massimiliano Vasile, Oliver Junge, Michael Dellnitz and Dario Izzo, “Designing optimal low-thrust
gravity-assist trajectories using space pruning and a multi-objective approach”, Engineering Optimization, Vol. 41, No.
2, pp. 155–181, February 2009.
720. Jesús Garcı́a Herrero, Antonio Berlanga and José Manuel Molina López, “Effective Evolutionary Algorithms for ManySpecifications Attainment: Application to Air Traffic Control Tracking Filters”, IEEE Transactions on Evolutionary
Computation, Vol. 13, No. 1, pp. 151–168, February 2009.
721. Lam T. Bui, Hussein A. Abbass and Daryl Essam, “Local models—an approach to distributed multi-objective optimization”, Computational Optimization and Applications, Vol. 42, No. 1, pp. 105–139, January 2009.
722. Rocı́o C. Romero-Zaliz, Cristina Rubio-Escudero, J. Perren Cobb, Francisco Herrera, Óscar Cordón and Igor Zwir, “A
Multiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: A
Case Study on the Gene Ontology Database”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, pp.
679–701, December 2008.
723. R. Brits, A.P. Engelbrecht and F. van den Bergh, “Locating multiple optima using particle swarm optimization”, Applied
Mathematics and Computation, Vol. 189, No. 2, pp. 1859–1883, June 15, 2007.
724. K. Mitra, “Genetic algorithms in polymeric material production, design, processing and other applications: a review”,
International Materials Review, Vol. 53, No. 5, pp. 275–297, September 2008.
725. J.M. Nobrega, O.S. Carneiro, A. Gaspar-Cunha and N.D. Goncalves, “Design of calibrators for profile extrusion Optimizing multi-step systems”, International Polymer Processing, Vol. 23, No. 3, pp. 331–338, July 2008.
726. Shao Yong Zheng, Sai Ho Yeung, Wing Shing Chan, Kim Fung Man, Shu Hung Leung and Quan Xue, “Dual-band
rectangular patch hybrid coupler”, IEEE Transactions on Microwave Theory and Techniques, Vol. 56, No. 7, pp.
1721–1728, July 2008.
727. S.Y.S. Leung, W.K. Wong and P.Y. Mok, “Multiple-objective genetic optimization of the spatial design for packing and
distribution carton boxes”, Computers & Industrial Engineering, Vol. 54, No. 4, pp. 889–902, May 2008.
728. Xiufen Zou, Yu Chen, Minzhong Liu and Lishan Kang, “A New Evolutionary Algorithm for Solving Many-Objective
Optimization Problems”, IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol. 38, No. 5,
pp. 1402–1412, October 2008.
36
729. N. Chakraborti, A. Shekhar, A. Singhal, S. Chakraborty, S. Chowdhury and R. Sripriya, “Fluid flow in hydrocyclones
optimized through multi-objective genetic algorithms”, Inverse Problems in Science and Engineering, Vol. 16, No. 8,
pp. 1023–1046, December 2008.
730. Min Zhang, Wenjian Luo and Xufa Wang, “Differential evolution with dynamic stochastic selection for constrained
optimization”, Information Sciences, Vol. 178, No. 15, pp. 3043–3074, August 1, 2008.
731. Elizabeth F. Wanner, Frederico G. Guimarães, Ricardo H.C. Takahashi and Peter J. Fleming, “Local Search with
Quadratic Approximations into Memetic Algorithms for Optimization with Multiple Criteria”, Evolutionary Computation, Vol. 16, No. 2, pp. 185–224, Summer 2008.
732. Antonio J. Nebro, Francisco Luna, Enrique Alba, Bernabé Dorronsoro, Juan J. Durillo and Andreas Beham, “AbYSS:
Adapting Scatter Search to Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12,
No. 4, pp. 439–457, August 2008.
733. Frederic Ros, Serge Guillaume, Marco Pintore and Jacques R. Chretien, “Hybrid genetic algorithm for dual selection”,
Pattern Analysis and Applications, Vol. 11, No. 2, pp. 179–198, June 2008.
734. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjective immune algorithm with nondominated
neighbor-based selection”, Evolutionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.
735. Mohammed Khabzaoui, Clarisse Dhaenens and El-Ghazali Talbi, “Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery”, RAIRO–Operations Research, Vol. 42, No. 1, pp. 69–83, JanuaryMarch 2008.
736. J. Reca, J. Martinez, R. Banos and C. Gil, “Optimal design of gravity-fed looped water distribution networks considering
the resilience index”, Journal of Water Resources Planning and Management–ASCE, Vol. 134, No. 3, pp. 234–238,
May-June 2008.
737. Sanghamitra Bandyopadhyay, Sriparna Saha, Ujjwal Maulik and Kalyanmoy Deb, “A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 3, pp.
269–283, June 2008.
738. N. Chakraborti, B. Siva Kumar, V. Satish Babu, S. Moitra and A. Mukhopadhyay, “A new multi-objective genetic
algorithm applied to hot-rolling process”, Applied Mathematical Modelling, Vol. 32, No. 9, pp. 1781–1789, September
2008.
739. Hamidreza Eskandari and Christopher D. Geiger, “A fast Pareto genetic algorithm approach for solving expensive
multiobjective optimization problems”, Journal of Heuristics, Vol. 14, No. 3, pp. 203–241, June 2008.
740. Philipp Limbourg and Hans-Dieter Kochs, “Multi-objective optimization of generalized reliability design problems using
feature models - A concept for early design stages”, Reliability Engineering & System Safety, Vol. 93, No. 6, pp. 815–828,
June 2008.
741. Jun Guo, Yi Wang, Kit-Sang Tang, Sammy Chan, Eric W.M. Wong, Peter Taylor and Moshe Zukerman, “Evolutionary
optimization of file assignment for a large-scale video-on-demand system”, IEEE Transactions on Knowledge and Data
Engineering, Vol. 20, No. 6, pp. 836–850, June 2008.
742. Sai-Ho Yeung, Hoi-Kuen Ng and Kim-Fung Man, “Multi-criteria design methodology of a dielectric resonator antenna
with jumping genes evolutionary algorithm”, AEU-International Journal of Electronics and Communications, Vol. 62,
No. 4, pp. 266–276, 2008.
743. David S. Robin, Wan Weishi Wan, Fernando Sannibale and Victor P. Suller, “Global analysis of all linear stable settings
of a storage ring lattice”, Physical Review Special Topics–Accelerators and Beams, Vol. 11, No. 2, Article Number
024002, February 2008.
744. Nicolas Jozefowiez, Frederic Semet and El-Ghazali Talbi, “Multi-objective vehicle routing problems”, European Journal
of Operational Research, Vol. 189, No. 2, pp. 293–309, September 1, 2008.
745. Miguel Delgado, Manuel P. Cuellar and Maria Carmen Pegalajar, “Multiobjective hybrid optimization and training of
recurrent neural Networks”, IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol. 38, No.
2, pp. 381–403, April 2008.
746. Xingdong Zhang and Marc P. Armstrong, “Genetic algorithms and the corridor location problem: multiple objectives
and alternative solutions”, Environment and Planning B–Planning & Design, Vol. 35, No. 1, pp. 148–168, January
2008.
747. Wasim Raza and Kwang-Yong Kim, “Multiobjective optimization of a wire-wrapped LMR fuel assembly”, Nuclear
Technology, Vol. 162, No. 1, pp. 45–52, April 2008.
748. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Robust identification of non-linear greenhouse model
using evolutionary algorithms”, Control Engineering Practice, Vol. 16, No. 5, pp. 515–530, May 2008.
749. Ben Torben-Nielsen, Karl Tuyls and Eric Postma, “EvOL-NEURON: Neuronal morphology generation”, Neurocomputing, Vol. 71, Nos. 4–6, pp. 963–972, January 2008.
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750. Fabian Duddeck, “Multidisciplinary optimization of car bodies”, Structural and Multidisciplinary Optimization, Vol. 35,
No. 4, pp. 375–389, April 2008.
751. Ricardo Perera and Antonio Ruiz, “A multistage FE updating procedure for damage identification in large-scale structures based on multiobjective evolutionary optimization”, Mechanical Systems and Signal Processing, Vol. 22, No. 4,
pp. 970–991, May 2008.
752. Gio J. Kao and Sheldon H. Jacobson, “Finding preferred subsets of Pareto optimal solutions”, Computational Optimization and Applications, Vol. 40, No. 1, pp. 73–95, May 2008.
753. A. I. Olcer, “A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping”,
Computers & Operations Research, Vol. 35, No. 9, pp. 2760–2775, September 2008.
754. Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto and Yusuke Nojima, “An empirical study on similarity-based
mating for evolutionary multiobjective combinatorial optimization”, European Journal of Operational Research, Vol.
188, No. 1, pp. 57–75, July 1, 2008.
755. Annette Muetze, “A neglected stepchild”, IEEE Industry Applications Magazine, Vol. 14, No. 2, pp. 14–22, March-April
2008.
756. N. Amanifard, N. Nariman-Zadeh, M. Borji, A. Khalkhali and A. Habibdoust, “Modelling and Pareto optimization of
heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms”, Energy
Conversion and Management, Vol. 49, No. 2, pp. 311–325, February 2008.
757. N. Nariman-zadeh, A. Jamali and A. Hajiloo, “Frequency-based reliability Pareto optimum design of proportionalintegral-derivative controllers for systems with probabilistic uncertainty”, Proceedings of the Institution of Mechanical
Engineers Part I–Journal of Systems and Control Engineering, Vol. 221, No. I8, pp. 1061–1075, December 2007.
758. Shubhabrata Datta, Frank Pettersson, Subhas Ganguly, Henrik Saxen and Nirupam Chakraborti, “Identification of
factors governing mechanical properties of TRIP-aided steel using genetic algorithms and neural networks”, Materials
and Manufacturing Processes, Vol. 23, No. 2, pp. 131–138, 2008.
759. Bin Qian, Ling Wang, De-Xian Huang and Xiong Wang, “Scheduling multi-objective job shops using a memetic algorithm
based on differential evolution”, International Journal of Advanced Manufacturing Technology, Vol. 35, Nos. 9–10, pp.
1014–1027, January 2008.
760. O. Giustolisi, A. Doglioni, D.A. Savic and F. di Pierro, “An evolutionary multiobjective strategy for the effective
management of groundwater resources”, Water Resources Research, Vol. 44 No. 1, article number W01403, January 3,
2008.
761. Eduardo Fernandez, Nora Cancela and Rafael Olmedo, “Deriving a final ranking from fuzzy preferences: An approach
compatible with the Principle of Correspondence”, Mathematical and Computer Modelling, Vol. 47, Nos. 1–2, pp.
218–234, January 2008.
762. Javier Sanchis, Miguel A. Martinez and Xavier Blasco, “Integrated multiobjective optimization and a priori preferences
using genetic algorithms”, Information Sciences, Vol. 178, No. 4, pp. 931–951, February 15, 2008.
763. J. Sanchis, M. Martinez and X. Blasco, “Multi-objective engineering design using preferences”, Engineering Optimization,
Vol. 40, No. 3, pp. 253–269, 2008.
764. Paulo Fazendeiro, Jose Valente de Oliveira and Witold Pedrycz, “A multiobjective design of a patient and anaesthetistfriendly neuromuscular blockade controller”, IEEE Transactions on Biomedical Engineering, Vol. 54, No. 9, pp. 1667–
1678, September 2007.
765. Zbigniew Michalewicz and Matthew Michalewicz, “Machine intelligence, adaptive business intelligence, and natural
intelligence”, IEEE Computational Intelligence Magazine, Vol. 3, No. 1, pp. 54–63, 2008.
766. F. Pettersson, N. Chakraborti and S.B. Singh, “Neural Networks Analysis of Steel Plate Processing Augmented by
Multi-objective Genetic Algorithms”, Steel Research International, Vol. 78, No. 12, pp. 890–898, December 2007.
767. Qingfu Zhang, Aimin Zhou and Yaochu Jin, “RM-MEDA: A Regularity Model-Based Multiobjective Estimation of
Distribution Algorithm”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 41–63, February 2008.
768. A. Gaspar-Cunha and J.A. Covas, “Robustness in multi-objective optimization using evolutionary algorithms”, Computational Optimization and Applications, Vol. 39, No. 1, pp. 75–96, January 2008.
769. Genci Capi, “Multiobjective evolution of neural controllers and task complexity”, IEEE Transactions on Robotics, Vol.
23, No. 6, pp. 1225–1234, 2007.
770. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Non-linear robust identification of a greenhouse model
using multi-objective evolutionary algorithms”, Biosystems Engineering, Vol. 98, No. 3, pp. 335–346, November 2007.
771. Ang Yang, Hussein A. Abbass and Ruhul Sarker, “Characterizing warfare in red teaming”, IEEE Transactions on
Systems, Man, and Cybernetics, Part B–Cybernetics, Vol. 36, No. 2, pp. 268–285, April 2006.
772. Julian Molina, Manuel Laguna, Rafael Marti and Rafael Caballero, “SSPMO: A scatter tabu search procedure for
non-linear multiobjective optimization”, INFORMS Journal on Computing, Vol. 19, No. 1, pp. 91–100, January 2007.
38
773. Robic C. Purshouse and Peter J. Fleming, “On the Evolutionary Optimization of Many Conflicting Objectives”, IEEE
Transactions on Evolutionary Algorithms, Vol. 11, No. 6, pp. 770–784, December 2007.
774. Qingfu Zhang and Hui Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition”, IEEE
Transactions on Evolutionary Computation, Vol. 11, No. 6, pp. 712–731, December 2007.
775. Sebastián Ventura, Cristóbal Romero, Amelia Zafra, José A. Delgado and César Hervás, “JCLEC: a Java framework for
evolutionary computation”, Soft Computing, Vol. 12, No. 4, pp. 381–392, February 2008.
776. Joshua B. Kollat and Patrick Reed, “A framework for visually interactive decision-making and design using evolutionary
multi-objective optimization (VI(D)under-barEO)”, Environmental Modelling & Software, Vol. 22, No. 12, pp. 1691–
1704, December 2007.
777. Marco A. Panduro, Carlos A. Brizuela and David H. Covarrubias, “Design of electronically steerable linear arrays with
evolutionary algorithms”, Applied Soft Computing, Vol. 8, No. 1, pp. 46–54, January 2008.
778. Kay-Soon Low and Tze-Shyan Wong, “A multiobjective genetic algorithm for optimizing the performance of hard disk
drive motion control system”, IEEE Transactions on Industrial Electronics, Vol. 54, No. 3, pp. 1716–1725, June 2007.
779. Arlene G. Smithson, Karim Hamza and Kazuhiro Saitou, “Design for existing lines: Part and process plan optimization
to best utilize existing production lines”, Journal of Computing and Information Science in Engineering, Vol. 7, No. 2,
pp. 126–131, June 2007.
780. DaeEun Kim and Jaehong Park, “Application of adaptive control to the fluctuation of engine speed at idle”, Information
Sciences, Vol. 177, No. 16, pp. 3341–3355, August 15, 2007.
781. A.F. Gomez-Skarmeta, F. Jimenez and G. Sanchez, “Improving interpretability in approximative fuzzy models via
multiobjective evolutionary algorithms”, International Journal of Intelligent Systems, Vol. 22, No. 9, pp. 943–969,
September 2007.
782. Sarbajit Pal, Pankaj Ganguly and P.K. Biswas, “Cubic Bezier approximation of a digitized curve”, Pattern Recognition,
Vol. 40, No. 10, pp. 2730–2741, October 2007.
783. Maria Jose del Jesus, Pedro Gonzalez, Francisco Herrera and Mikel Mesonero, “Evolutionary fuzzy rule induction process
for subgroup discovery: A case study in marketing”, IEEE Transactions on Fuzzy Systems, Vol. 15, No. 4, pp. 578–592,
August 2007.
784. Luciano Sanchez and Ines Couso, “Advocating the use of imprecisely observed data in genetic fuzzy systems”, IEEE
Transactions on Fuzzy Systems, Vol. 15, No. 4, pp. 551–562, August 2007.
785. A. Tarafder, G.P. Rangaiah and Ajay K. Ray, “A study of finding many desirable solutions in multiobjective optimization
of chemical processes”, Computers & Chemical Engineering, Vol. 31, No. 10, pp. 1257–1271, October 2007.
786. S. Dehuri, S. Patnaik, A. Ghosh and R. Mall, “Application of elitist multi-objective genetic algorithm for classification
rule generation”, Applied Soft Computing, Vol. 8, No. 1, pp. 477–487, January 2008.
787. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “An evolutionary multiobjective framework for structural damage
localization and quantification”, Engineering Structures, Vol. 29, No. 10, pp. 2540–2550, October 2007.
788. M. Ye and G. Zhouz, “A local genetic approach to multi-objective, facility layout problems with fixed aisles”, International Journal of Production Research, Vol. 45, No. 22, pp. 5243–5264, 2007.
789. Guangtao Fu, David Butler and Soon-Thiam Khu, “Multiple objective optimal control of integrated urban wastewater
systems”, Environmental Modelling & Software, Vol. 23, No. 2, pp. 225–234, February 2008.
790. Yuren Zhou and Jun He, “Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids”,
Information Processing Letters, Vol. 104, No. 4, pp. 117–122, November 15, 2007.
791. David Midgley, Robert Marks and Dinesh Kunchamwar, “Building and assurance of agent-based models: An example
and challenge to the field”, Journal of Business Research, Vol. 60, No. 8, pp. 884–893, August 2007.
792. Shubhabrata Datta, Frank Pettersson, Subhas Ganguly, Henrik Saxén and Niruopam Chakraborti, “Designing High
Strength Multi-phase Steel for Improved Strength-Ductility Balance Using Neural Networks and Multi-objective Genetic
Algorithms”, ISIJ International, Vol. 47, No. 8, pp. 1195–1203, 2007
793. Yandra and Hiroyuki Tamura, “A new multiobjective genetic algorithm with heterogeneous population for solving
flowshop scheduling problems”, International Journal of Computer Integrated Manufacturing, Vol. 20, No. 5, pp.
465–477, 2007.
794. Frederico G. Guimaraes, Reinaldo M. Palhares, Felipe Campelo and Hajime Igarashi, “Design of mixed H-2/H infinity
control systems using algorithms inspired by the immune system”, Information Sciences, Vol. 177, No. 20, pp. 4368–
4386, October 15, 2007.
795. Kumara Sastry, D.D. Johnson, Alexis L. Thompson, David E. Goldberg, Todd J. Martinez, Jeff Leiding and Jane
Owens, “Optimization of Semiempirical Quantum Chemistry Methods via Multiobjective Genetic Algorithms: Accurate
Photodynamics for Larger Molecules and Longer Time Scales”, Materials and Manufacturing Processes, Vol. 22, No. 5,
pp. 553–561, 2007.
39
796. Henrik Saxén, Frank Pettersson and Kiran Gunturu, “Evolving Nonlinear Time-Series Models of the Hot Metal Silicon
Content in the Blast Furnace”, Materials and Manufacturing Processes, Vol. 22, Nos. 5-6, pp. 577–584, 2007.
797. Kaisa Miettinen, “Using Interactive Multiobjective Optimization in Continuous Casting of Steel”, Materials and Manufacturing Processes, Vol. 22, No. 5, pp. 585–593, 2007.
798. S. Ganguly, S. Datta and N. Chakraborti, “Genetic algorithms in optimization of strength and ductility of low-carbon
steels”, Materials and Manufacturing Processes, Vol. 22, Nos. 5–6, pp. 650–658, 2007.
799. Eleni Aggelogiannaki and Haralarnbos Sarimveis, “Simulated annealing algorithm for prioritized multiobjective optimizationimplementation in an adaptive model predictive control configuration”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 902–915, August 2007.
800. Christian Gagne and Marc Parizeau, “Genetic engineering of hierarchical fuzzy regional representations for handwritten
character recognition”, International Journal on Document Analysis and Recognition, Vol. 8, No. 4, pp. 223–231,
September 2006.
801. Don Jyh-Fu Jeng, Ikno Kim and Junzo Watada, “Bio-soft computing with fixed-length DNA to a group control optimization problem”, Soft Computing, Vol. 12, No. 3, pp. 223–228, February 2008.
802. Antonio Pinto, Daniele Peri and Emilio F. Campana, “Multiobjective optimization of a containership using deterministic
particle swarm optimization”, Journal of Ship Research, Vol. 51, No. 3, pp. 217–228, September 2007.
803. Jing Liu, Weicai Zhong and Licheng Hao, “An organizational evolutionary algorithm for numerical optimization”, IEEE
Transactions on Systems, Man and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 1052–1064, August 2007.
804. L.F. Gonzalez, J. Periaux, L. Damp and K. Srinivas, “Evolutionary methods for multidisciplinary optimization applied
to the design of UAV systems”, Engineering Optimization, Vol. 39, No. 7, pp. 773–795, October 2007.
805. Joern Mehnen, Thomas Michelitsch, Christian Lasarczyk and Thomas Bartz-Beielstein, “Multi-objective evolutionary
design of mold temperature control using DACE for parameter optimization”, International Journal of Applied Electromagnetics and Mechanics, Vol. 25, Nos. 1–4, pp. 661–667, 2007.
806. Pascal Côté, Lael Parrott and Robert Sabourin, “Multi-objective optimization of an ecological assembly model”, Ecological Informatics, Vol. 2, No. 1, pp. 23–31, January 1, 2007.
807. Maria Joao Alves and Marla Almeida, “MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem”, Computers & Operations Research, Vol. 34, No. 11, pp. 3458–3470, November
2007.
808. Mario Köppen, Katrin Franke and Raul Vicente-Garcia, “Tiny GAs for image processing applications”, IEEE Computational Intelligence Magazine, Vol. 1, No. 2, pp. 17–26, May 2006.
809. G. Li, M. Li, S. Azarm, J. Rambo and Y. Joshi, “Optimizing thermal design of data center cabinets with a new
multi-objective genetic algorithm”, Distributed and Parallel Databases, Vol. 21, Nos. 2–3, pp. 167–192, June 2007.
810. Chandan Guria, Mohan Varma, Surya P. Mehrotra and Santosh K. Gupta, “Simultaneous optimization of the performance of flotation circuits and their simplification using the jumping gene adaptations of genetic algorithm-II: More
complex problems”, International Journal of Mineral Processing, Vol. 79, No. 3, pp. 149–166, June 2006.
811. Y. Tang, P.M. Reed and J.B. Kollat, “Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications”, Advances in Water Resources, Vol. 30, No. 3, pp. 335–353, March 2007.
812. J.B. Kollat and P.M. Reed, “A computational scaling analysis of multiobjective evolutionary algorithms in long-term
groundwater monitoring applications”, Advances in Water Resources, Vol. 30, No. 3, pp. 408–419, March 2007.
813. Ivan Blecic, Arnaldo Cecchini and Giuseppe A. Trunfio, “A decision support tool coupling a causal model and a multiobjective genetic algorithm”, Applied Intelligence, Vol. 26, No. 2, pp. 125–137, April 2007.
814. J.W. Large, D.F. Jones and M. Tamiz, “Hyper-spherical inversion transformations in multi-objective evolutionary optimization”, European Journal of Operational Research, Vol. 177, No. 3, pp. 1678–1702, March 16, 2007.
815. Sahnan A. Khan and Andries P. Engelbrecht, “A new fuzzy operator and its application to topology design of distributed
local area networks”, Information Sciences, Vol. 177, No. 13, pp. 2692–2711, July 1, 2007.
816. M.R. Gholamian, S.M.T. Fatemi Ghomi and M. Ghazanfari, “A hybrid system for multiobjective problems - A case
study in NP-hard problems”, Knowledge-Based Systems, Vol. 20, No. 4, pp. 426–436, May 2007.
817. Patrick Reed, Joshua B. Kollat and V.K. Devireddy, “Using interactive archives in evolutionary multiobjective optimization: A case study for long-term groundwater monitoring design”, Environmental Modelling & Software, Vol. 22, No. 5,
pp. 683–692, May 2007.
818. Carlos Gomes da Silva, José Figueira and João Clı́maco, “Integrating partial optimization with scatter search for solving
bi-criteria {0,1}-knapsack problems”, European Journal of Operational Research, Vol. 177, No. 3, pp. 1656–1677, March
16, 2007.
819. M.R. Gholamian, S.M.T. Fatemi Ghomi and M. Ghazanfari, “A hybrid intelligent system for multiobjective decision
making problems”, Computers and Industrial Engineering, Vol. 51, No. 1, pp. 26–43, September 2006.
40
820. C. Gil, A. Marquez, R. Baños, M.G. Montoya and J. Gomez, “A hybrid method for solving multi-objective global
optimization problems”, Journal of Global Optimization, Vol. 38, No. 2, pp. 265–281, June 2007.
821. Ningchuan Xiao, David A. Bennett and Marc P. Armstrong, “Interactive evolutionary approaches to multiobjective
spatial decision making: A synthetic review”, Computers Environment and Urban Systems, Vol. 31, No. 3, pp. 232–252,
May 2007.
822. Julia Handl, Douglas B. Kell and Joshua Knowles, “Multiobjective optimization in bioinformatics and computational
biology”, IEEE-ACM Transactions on Computational Biology and Bioinformatics, Vol. 4, No. 2, pp. 279–292, AprilJune 2007.
823. David I. Broadhurst and Douglas B. Kell, “Statistical strategies for avoiding false discoveries in metabolomics and related
experiments”, Metabolomics, Vol. 2, No. 4, pp. 171–196, December 2006.
824. A.J. Rivera, I. Rojas, J. Ortega and M.J. del Jesus, “A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks”, Soft Computing, Vol. 11, No. 7, pp. 655–668, May 2007.
825. S.H. Yeung, W.T. Luk, H.K. Ng, K.F. Man and C.H. Chan, “A jumping genes paradigm for the design of wide-band
patch antenna with double shorting wall”, Microwave and Optical Technology Letters, Vol. 49, No. 3, pp. 706–709,
March 2007.
826. Andres L. Medaglia, Samuel B. Graves and Jeffrey L. Ringuest, “A multiobjective evolutionary approach for linearly
constrained project selection under uncertainty”, European Journal of Operational Research, Vol. 179, No. 3, pp.
869–894, June 16, 2007.
827. Richard S. Segall and Qingyu Zhang, “Data visualization and data mining of continuous numerical and discrete nominalvalued microarray databases for bioinformatics”, Kybernetes, Vol. 34, Nos. 9–10, pp. 1538–1566, 2006.
828. Sai-Ho Yeung and Kim-Fung Man, “A jumping genes paradigm with fuzzy rules for optimizing digital IIR filters”, Neural
Information Processing, Pt 2, Proceedings, pp. 568–577, Springer-Verlag, Lecture Notes in Computer Science Vol. 4233,
2006.
829. Satish V. Ukkusuri, Tom V. Mathew and S. Travis Waller, “Robust transportation network design under demand
uncertainty”, Computer-Aided Civil and Infrastructure Engineering, Vol. 22, No. 1, pp. 6–18, January 2007.
830. E. Alba, B. Dorronsoro, F. Luna, A.J. Nebro, P. Bouvry and L. Hogie, “A cellular multi-objective genetic algorithm for
optimal broadcasting strategy in metropolitan MANETs” , Computer Communications, Vol. 30, No. 4, pp. 685–697,
February 26, 2007.
831. N. Lyu and K. Saitou, “Decomposition-based assembly synthesis of a three-dimensional body-in-white model for structural stiffness”, Journal of Mechanical Design, Vol. 127, No. 1, pp. 34–48, January 2005.
832. L.A. Welser, R.C. Mancini, J.A. Koch, N. Izumi, H. Dalhed, H. Scott, T.W. Barbee, R.W. Lee, I.E. Golovkin, F.
Marshall, J. Delettrez and L. Klein, “Analysis of the spatial structure of inertial confinement fusion implosion cores
at OMEGA”, Journal of Quantitative Spectroscopy & Radiative Transfer, Inglaterra, Vol. 81, Nos. 1–4, pp. 487–497,
September-November 2003.
833. W.F. Yu and K. Hidajat and A.K. Ray, “Application of multiobjective optimization in the design and operation of
reactive SMB and its experimental verification”, Industrial & Engineering Chemistry Research, Estados Unidos, Vol. 42,
No. 26, pp. 6823–6831, December 24, 2003.
834. Patrick Reed, Barbara S. Minsker and David E. Goldberg, “Simplifying multiobjective optimization: An automated
design methodology for the nondominated sorted genetic algorithm-II”, Water Resources Research, Vol. 39, No. 7, Art.
No. 1196, July 30, 2003.
835. C. Guria, M. Verma, S.P. Mehrotra and S.K. Gupta, “Multi-objective optimal synthesis and design of froth flotation
circuits for mineral processing, using the jumping gene adaptation of genetic algorithm”, Industrial & Engineering
Chemistry Research, Vol. 44, No. 8, pp. 2621–2633, April 13, 2005.
836. B. Suman, “Study of self-stopping PDMOSA and performance measure in multiobjective optimization”, Computers &
Chemical Engineering, Vol. 29, No. 5, pp. 1131–1147, April 15, 2005.
837. V. Cotik, R.R. Zaliz and I. Zwir, “A hybrid promoter analysis methodology for prokaryotic genomes”, Fuzzy Sets and
Systems, Vol. 152, No. 1, pp. 83–102, May 16, 2005.
838. K.J. Kim and R.L. Smith, “Systematic procedure for designing processes with multiple environmental objectives”,
Environmental Science & Technology, Vol. 39, No. 7, pp. 2394–2405, April 1, 2005.
839. P. Di Barba, “Multiobjective design optimisation: A microeconomics-inspired strategy applied to electromagnetics”,
International Journal of Applied Electromagnetics and Mechanics, Vol. 21, No. 2, pp. 101–117, 2005.
840. N. Lyu and K. Saitou, “Topology optimization of multicomponent beam structure via decomposition-based assembly
synthesis”, Journal of Mechanical Design, Vol. 127, No. 2, pp. 170–183, March 2005.
841. M.S. Osman, M.A. Abo-Sinna and M.K. El-Sayed, “An algorithm for solving multi-stage decision making model with
multiple fuzzy goals based on genetic algorithms”, International Journal of Nonlinear Sciences and Numerical Simulation,
Vol. 5, No. 4, pp. 371–385, 2004.
41
842. M.R. Gholamian, S.M.T.F. Ghomi and M. Ghazanfari, “A hybrid systematic design for multiobjective market problems:
a case study in crude oil markets”, Engineering Applications of Artificial Intelligence, Vol. 18, No. 4, pp. 495–509, June
2005.
843. A. Singh and H.H. Lou, “Hierarchical Pareto optimization for the sustainable development of industrial ecosystems”,
Industrial & Engineering Chemistry Research, Vol. 45, No. 9, pp. 3265–3279, April 26, 2006.
844. K.C. Tan, Y.H. Chew and L.H. Lee, “A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem
with time windows”, Computational Optimization and Applications, Vol. 34, No. 1, pp. 115–151, May 2006.
845. K.C. Tan, Y.H. Chew and L.H. Lee, “A hybrid multi-objective evolutionary algorithm for solving truck and trailer
vehicle routing problems”, European Journal of Operational Research, Vol. 172, No. 3, pp. 855–885, August 1st, 2006.
846. H.A. Abbass, “An economical cognitive approach for bi-objective optimization using bliss points, visualization, and
interaction”, Soft Computing, Vol. 10, No. 8, pp. 687-,698, June 2006.
847. S. Tiwari and N. Chakraborti, “Multi-objective optimization of a two-dimensional cutting problem using genetic algorithms”, Journal of Materials Processing Technology, Vol. 173, No. 3, pp. 384–393, April 20, 2006.
848. C. Cagne and M. Parizeau, “Genericity in evolutionary computation software tools: Principles and case-study”, International Journal on Artificial Intelligence Tools, Vol. 15, No. 2, pp. 173–194, April 2006.
849. S.L. Avila, A.C. Lisboa, L. Krahenbuhl, W.P. Carpes, J.A. Vasconcelos, R.R. Saldanha and R.H.C. Takahashi, “Sensitivity analysis applied to decision making in multiobjective evolutionary optimization”, IEEE Transactions on Magnetics,
Vol. 42, No. 4, pp. 1103–1106, April 2006.
850. A. Gepperth and S. Roth, “Applications of multi-objective structure optimization”, Neurocomputing, Vol. 69, Nos. 7–9,
pp. 701–713, March 2006.
851. L.A. Welser, R.C. Mancini, J.A. Koch, N. Izumi, S.J. Louis, I.E. Golovkin, T.W. Barbee, S.W. Haan, J.A. Delettrez,
F.J. Marshall, R.P. Regan, V.A. Smalyuk, D.A. Haynes and R.W. Lee, “Multi-objective spectroscopic analysis of core
gradients: Extension from two to three objectives”, Journal of Quantitative Spectroscopy & Radiative Transfer, Vol. 99,
Nos. 1–3, pp. 649–657, May-June 2006.
852. Lyndon While, Phil Hingston, Luigi Barone, and Simon Huband, “A Faster Algorithm for Calculating Hypervolume”,
IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, pp. 29–38, February 2006.
853. P. Kuntz, B. Pinaud and R. Lehn, “Minimizing crossings in hierarchical digraphs with a hybridized genetic algorithm”,
Journal of Heuristics, Vol. 12, Nos. 1–2, pp. 23–36, January 2006.
854. K. Foli, T. Okabe, M. Olhofer, Y.C. Jin and B. Sendhoff, “Optimization of micro heat exchanger: CFD, analytical
approach and multi-objective evolutionary algorithms”, International Journal of Heat and Mass Transfer, Vol. 49, Nos.
5–6, pp. 1090–1099, March 2006.
855. J.J. Huang, G.H. Tzeng and C.S. Ong, “Optimal fuzzy multi-criteria expansion of competence sets using multi-objectives
evolutionary algorithms”, Expert Systems with Applications, Vol. 30, No. 4, pp. 739–745, May 2005.
856. Z.V.P. Murthy and J.C. Vengal, “Optimization of a reverse osmosis system using genetic algorithm”, Separation Science
and Technology, Vol. 41, No. 4, pp. 647–663, 2006.
857. Joshua Knowles, “ParEGO: A Hybrid Algorithm With On-Line Landscape Approximation for Expensive Multiobjective
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, pp. 50–66, February 2006.
858. E.G. Talbi and H. Meunier, “Hierarchical parallel approach for GSM mobile network design”, Journal of Parallel and
Distributed Computing, Vol. 66, No. 2, pp. 274–290, February 2006.
859. K.B. Matthews, K. Buchan, A.R. Sibbald and S. Craw, “Combining deliberative and computer-based methods for
multi-objective land-use planning”, Agricultural Systems, Vol. 87, No. 1, pp. 18–37, January 2006.
860. F. de Toro, E. Ros, S. Mota and J. Ortega, “Evolutionary algorithms for multiobjective and multimodal optimization
of diagnostic schemes”, IEEE Transactions on Biomedical Engineering, Vol. 53, No. 2, pp. 178–189, February 2006.
861. S. Meshoul, K. Mahdi and M. Batouche, “A quantum inspired evolutionary framework for multi-objective optimization”,
in Progress in Artificial Intelligence, Proceedings, pp. 190–201, Springer, Lecture Notes in Artificial Intelligence, Vol.
3808, 2005.
862. B. Ombuki, B.J. Ross and F. Hanshar, “Multi-objective genetic algorithms for vehicle routing problem with time
windows”, Applied Intelligence, Vol. 24, No. 1, pp. 17–30, February 2006.
863. M.A. Panduro, C.A. Brizuela, D. Covarrubias and C. Lopez, “A trade-off curve computation for linear antenna arrays
using an evolutionary multi-objective approach”, Soft Computing, Vol. 10, No. 2, pp. 125–131, January 2006.
864. M. Liu, S.A. Burns and Y.K. Wen, “Genetic algorithm based construction-conscious minimum weight design of seismic
steel moment-resisting frames”, Journal of Structural Engineering–ASCE, Vol. 132, No. 1, pp. 50–58, January 2006.
865. C.J.K. Lee, T. Furukawa and S. Yoshimura, “A human-like numerical technique for design of engineering systems”,
International Journal for Numerical Methods in Engineering, Vol. 64, No. 14, pp. 1915–1943, December 14, 2005.
42
866. K. Deb, M. Mohan and S. Mishra, “Evaluating the epsilon-domination based multi-objective evolutionary algorithm for
a quick computation of pareto-optimal solutions”, Evolutionary Computation, Vol. 13, No. 4, pp. 501–525, Winter 2005.
867. L. Poladian and L.S. Jermiin, “Multi-objective evolutionary algorithms and phylogenetic inference with multiple data
sets”, Soft Computing, Vol. 10, No. 4, pp. 359–368, February 2006.
868. F. Bellas, R.J. Duro and F. Lopez-Pena, “Blind signal separation through cooperating ANNs”, Knowledge-Based Intelligent Information and Engineering Systems, Part 1, Proceedings, pp. 847–853, Springer, Lecture Notes in Artificial
Intelligence Vol. 3681, 2005.
869. Martin Trefzer, Jörg Langeheine, Karlheinz Meier and Johannes Schemmel, “Operational Amplifiers: An Example for
Multi-objective Optimization on an Analog Evolvable Hardware Platform”, in J. Manuel Moreno, Jordi Madrenas and
Jordi Cosp (editors), Evolvable Systems: From Biology to Hardware, 6th International Conference, ICES 2005, pp.
86–97, Springer, Lecture Notes in Computer Science Vol. 3637, Sitges, Spain, September 2005.
870. O. Cordon, E. Herrera-Viedma and M. Luque, “Improving the learning of Boolean queries by means of a multiobjective
IQBE evolutionary algorithm”, Information Processing & Management, Vol. 42, No. 3, pp. 615–632, May 2006.
871. T.M. Chan, K.F. Man, K.S. Tang and S. Kwong, “A jumping gene algorithm for multiobjective resource management
in wideband CDMA systems”, Computer Journal, Vol. 48, No. 6, pp. 749–768, November 2005.
872. K.K. Kshetrapalapuram and M. Kirley, “Mining classification rules using evolutionary multi-objective algorithms”,
Knowledge-Based Intelligent Information and Engineering Systems, Part 3, Proceedings, Springer, pp. 959–965, Lecture
Notes in Artificial Intelligence Vol. 3683, 2005.
873. T. Ray and K.W. Won, “An evolutionary algorithm for constrained bi-objective optimization using radial slots”,
Knowledge-Based Intelligent Information and Engineering Systems, Part 4, Proceedings, Springer, pp. 49–56, Lecture
Notes in Artificial Intelligence Vol. 3684, 2005.
874. X.F. Zou and L.S. Kang, “Fast annealing genetic algorithm for multi-objective optimization problems”, International
Journal of Computer Mathematics, Vol. 82, No. 8, pp. 931–940, August 2005.
875. Tapio Tyni and Jari Ylinen, “Evolutionary bi-objective optimisation in the elevator car routing problem”, European
Journal of Operational Research, Vol. 169, No. 3, pp. 960–977, March 16, 2006.
876. E.K. Burke and J.D. Landa Silva, “The influence of the fitness evaluation method on the performance of multiobjective
search algorithms”, European Journal of Operational Research, Vol. 169, No. 3, pp. 875–897, March 16, 2006.
877. K. Atashkari, N. Nariman-Zadeh, A. Pilechi, A. Jamali and X. Yao, “Thermodynamic Pareto optimization of turbojet
engines using multi-objective genetic algorithms”, International Journal of Thermal Sciences, Vol. 44, No. 11, pp.
1061–1071, November 2005.
878. Y.R. Zhou and J. He, “The convergence of a multi-objective evolutionary algorithm based on grids”, Advances in Natural
Computation, Pt 2, Proceedings, Springer, pp. 1015–1024, Lecture Notes in Computer Science Vol. 3611, 2005.
879. Y. Yun, M. Yoon and H. Nakayama, “Genetic algorithm for multi-objective optimization using GDEA”, Advances in
Natural Computation, Pt 3, Proceedings, Springer, pp. 409–416, Lecture Notes in Computer Science Vol. 3612, 2005.
880. C.S. Ong, H.J. Huang and G.H. Tzeng, “A novel hybrid model for portfolio selection”, Applied Mathematics and
Computation, Vol. 169, No. 2, pp. 1195–1210, October 15, 2005.
881. N. Zong and X. Hong, “Nonlinear channel equalizer design using directional evolutionary multi-objective optimization”,
International Journal of Systems Science, Vol. 36, No. 12, pp. 737–755, October 10, 2005.
882. N. Chakraborti, “Genetic algorithms in these changing steel times”, Ironmaking & Steelmaking, Vol. 32, No. 5, pp.
401–404, October 2005.
883. A. Hadi and F. Rashidi, “Design of optimal power distribution networks using multiobjective genetic algorithm”, KI
2005: Advances in Artificial Intelligence, Springer, pp. 203–215, Lecture Notes in Artificial Intelligence Vol. 3698, 2005.
884. Carlos Gomes da Silva, João Clı́maco and José Figueira, “A scatter search method for bi-criteria {0,1}-knapsack problems”, European Journal of Operational Research, Vol. 169, No. 2, pp. 373–391, March 1st, 2006.
885. C. Guria, M. Verma, S.K. Gupta and S.P. Mehrotra, “Simultaneous optimization of the performance of flotation circuits
and their simplification using the jumping gene adaptations of genetic algorithm”, International Journal of Mineral
Processing, Vol. 77, No. 3, pp. 165–185, November 2005.
886. C. Guria, P.K. Bhattacharya and S.K. Gupta, “Multi-objective optimization of reverse osmosis desalination units using
different adaptations of the non-dominated sorting genetic algorithm (NSGA)”, Computers & Chemical Engineering,
Vol. 29, No. 9, pp. 1977–1995, August 15, 2005.
887. A. Gaspar-Cunha and J.C. Viana, “Using multi-objective evolutionary algorithms to optimize mechanical properties of
injection molded part”, International Polymer Processing, Vol. 20, No. 3, pp. 274–285, September 2005.
888. Fabio Freschi and Maurizio Repetto, “Multiobjective Optimization by a Modified Artificial Immune System Algorithm”,
in Christian Jacob, Marcin L. Pilat, Peter J. Bentley and Jonathan Timmis (editors), Artificial Immune Systems. 4th
International Conference, ICARIS 2005, pp. 248–261, Springer. Lecture Notes in Computer Science Vol. 3627, Banff,
Canada, August 2005.
43
889. S. Ruzika and M.M. Wiecek, “Approximation methods in multiobjective programming”, Journal of Optimization Theory
and Applications, Vol. 126, No. 3, pp. 473–501, September 2005.
890. R. Kachhap and C. Guria, “Multi-objective optimization of a batch copoly(ethylene-polyoxyethylene terephthalate)
reactor using different adaptations of nondominated sorting genetic algorithm”, Macromolecular Theory and Simulations,
Vol. 14, No. 6, pp. 358–373, July 19, 2005.
891. T. Hanne and S. Nickel, “A multiobjective evolutionary algorithm for scheduling and inspection planning in software
development projects”, European Journal of Operational Research, Vol. 167, No. 3, pp. 663–678, December 16, 2005.
892. S.A. Mansouri, “A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines”, European
Journal of Operational Research, Vol. 167, No. 3, pp. 696–716, December 16, 2005.
893. Rajeev Kumar and Nilanjan Banerjee, “Running time analysis of a multiobjective evolutionary algorithm on simple
and hard problems”, in Alden H. Wright, Michael D. Vose, Kenneth A. De Jong and Lothar M. Schmitt (editors),
Foundations of Genetic Algorithms. 8th International Workshop, FOGA 2005, Springer, Lecture Notes in Computer
Science Vol. 3469, pp. 112–131, Aizu-Wakamatsu City, Japan, January 2005.
894. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
895. J.M. Herrero, X. Blasco, M. Martinez and C. Ramos, “Nonlinear robust identification with epsilon-GA: FPS under
several norms simultaneously”, in Computational Intelligence and Bioinspired Systems. Proceedings, pp. 993–1001,
Springer-Verlag, Lecture Notes in Computer Science Vol. 3512, 2005.
896. F. Bellas, J.A. Becerra and R.J. Duro, “Evolution of cooperating ANNs through functional phenotypic affinity”, in
Computational Intelligence and Bioinspired Systems. Proceedings, Springer-Verlag, pp. 333–340, Lecture Notes in
Computer Science Vol. 3512, 2005.
897. M.A. Panduro, D.H. Covarrubias, C.A. Brizuela and F.R. Marante, “A multi-objective approach in the linear antenna
array design”, AEU-International Journal of Electronics and Communications, Vol. 59, No. 4, pp. 205–212, 2005.
898. A. Gaspar-Cunha, J.A. Covas and B. Vergnes, “Defining the configuration of co-rotating twin-screw extruders with
multiobjective evolutionary algorithms”, Polymer Engineering and Science, Vol. 45, No. 8, pp. 1159–1173, August
2005.
899. M.A. Martinez, J. Sanchis and X. Blasco, “Genetic algorithms for multiobjective controller design”, in Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. Part 2. Proceedings, Springer-Verlag, Lecture
Notes in Computer Science Vol. 3562, pp. 242–251, 2005.
900. K. Rodriguez-Vazquez and P.J. Fleming, “Evolution of mathematical models of chaotic systems based on multiobjective
genetic programming”, Knowledge and Information Systems, Vol. 8, No. 2, pp. 235–256, August 2005.
901. N. Nariman-Zadeh, K. Atashkari, A. Jamali, A. Pilechi and X. Yao, “Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms”, Engineering
Optimization, Vol. 37, No. 5, pp. 437–462, July 2005.
902. J. Balicki, “Immune systems in multi-criterion evolutionary algorithm for task assignments in distributed computer
system”, Advances in Web Intelligence, Springer, Lecture Notes in Computer Science Vol. 3528, pp. 51–56, 2005.
903. I. Blecic, A. Cecchini and G.A. Trunfio, “A decision support tool coupling a causal model and a multi-objective genetic
algorithm”, Innovations in Applied Intelligence, Springer, Lecture Notes in Artificial Intelligence Vol. 3533, pp. 628–637,
2005.
904. O.L. Cetin and S. Saitou, “Decomposition-based assembly synthesis of multiple structures for minimum manufacturing
cost”, Journal of Mechanical Design, Vol. 127, No. 4, pp. 572–579, July 2005.
905. Y. Vidyakiran, B. Mahanty and N. Chakraborti, “A genetic-algorithms-based multiobjective approach for a threedimensional guillotine cutting problem”, Materials and Manufacturing Processes, Vol. 20, No. 4, pp. 697–715, 2005.
906. Yaochu Jin, Bernhard Sendhoff and Edgar Körner, “Evolutionary Multi-objective Optimization for Simultaneous Generation of Signal-Type and Symbol-Type Representations”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and
Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp.
692–706, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
907. S.A. Mansouri, “Coordination of set-ups between two stages of a supply chain using multi-objective genetic algorithms”,
International Journal of Production Research, Vol. 43, No. 15, pp. 3163–3180, August 1, 2005.
908. Frank Schlottmann, Andreas Mitschele and Detlef Seese, “A Multi-objective Approach to Integrated Risk Management”,
in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 692–706, Springer. Lecture Notes in Computer Science Vol.
3410, Guanajuato, México, March 2005.
909. Hernán Aguirre and Kiyoshi Tanaka, “Selection, Drift, Recombination, and Mutation in Multiobjective Evolutionary
Algorithms on Scalable MNK-Landscapes”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 355–369, Springer.
Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
44
910. David Greiner, Gabriel Winter, José M. Emperador and Blas Galván, “Gray Coding in Evolutionary Multicriteria
Optimization: Application in Frame Structural Optimum Design”, in Carlos A. Coello Coello, Arturo Hernández Aguirre
and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005,
pp. 576–591, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
911. Juan Carlos Leyva-Lopez and Miguel Angel Aguilera-Contreras, “A Multiobjective Evolutionary Algorithm for Deriving
Final Ranking from a Fuzzy Outranking Relation”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart
Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 235–249,
Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
912. M. Laumanns and N. Laumanns, “Evolutionary multiobjective design in automotive development”, Applied Intelligence,
Vol. 23, No. 1, pp. 55–70, July 2005.
913. Jerzy Duda and Andrzej Osyczka, “Multiple Criteria Lot-Sizing in a Foundry Using Evolutionary Algorithms”, in Carlos
A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization.
Third International Conference, EMO 2005, pp. 651–663, Springer. Lecture Notes in Computer Science Vol. 3410,
Guanajuato, México, March 2005.
914. Christian Igel, “Multi-objective Model Selection for Support Vector Machines”, in Carlos A. Coello Coello, Arturo
Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 443–458, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March
2005.
915. Yusuke Nojima, Kaname Narukawa, Shiori Kaige and Hisao Ishibuchi, “Effects of Removing Overlapping Solutions
on the Performance of the NSGA-II Algorithm”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart
Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 341–354,
Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
916. Hisao Ishibuchi and Kaname Narukawa, “Recombination of Similar Parents in EMO Algorithms”, in Carlos A. Coello
Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 265–279, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato,
México, March 2005.
917. Carlos A. Brizuela and Everardo Gutiérrez, “Multi-objective Go with the Winners Algorithm: A Preliminary Study”,
in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 206–220, Springer. Lecture Notes in Computer Science Vol.
3410, Guanajuato, México, March 2005.
918. Christian Haubelt, Jürgen Gamenik and Jürgen Teich, “Initial Population Construction for Convergence Improvement
of MOEAs”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary MultiCriterion Optimization. Third International Conference, EMO 2005, pp. 191–205, Springer. Lecture Notes in Computer
Science Vol. 3410, Guanajuato, México, March 2005.
919. Matthieu Basseur, Franck Seynhaeve and El-Ghazali Talbi, “Path Relinking in Pareto Multi-objective Genetic Algorithms”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion
Optimization. Third International Conference, EMO 2005, pp. 120–134, Springer. Lecture Notes in Computer Science
Vol. 3410, Guanajuato, México, March 2005.
920. Adam Berry and Peter Vamplew, “The Combative Accretion Model–Multiobjective Optimisation Without Explicit
Pareto Ranking”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary
Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 77–91, Springer. Lecture Notes in
Computer Science Vol. 3410, Guanajuato, México, March 2005.
921. Michael Emmerich, Nicola Beume and Boris Naujoks, “An EMO Algorithm Using the Hypervolume Measure as Selection
Criterion”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary MultiCriterion Optimization. Third International Conference, EMO 2005, pp. 62–76, Springer. Lecture Notes in Computer
Science Vol. 3410, Guanajuato, México, March 2005.
922. Peter Fleming, Robin C. Purshouse and Robert J. Lygoe, “Many-Objective Optimization: An Engineering Design
Perspective”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary MultiCriterion Optimization. Third International Conference, EMO 2005, pp. 14–32, Springer. Lecture Notes in Computer
Science Vol. 3410, Guanajuato, México, March 2005.
923. Rajeev Kumar, P.K. Singh and P.P. Chakrabarti, “Multiobjective EA Approach for Improved Quality of Solutions for
Spanning Tree Problem”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary
Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 811–825, Springer. Lecture Notes in
Computer Science Vol. 3410, Guanajuato, México, March 2005.
924. Kwang Mong Sim and Bo An, “Evolving Best-Response Strategies for Market-Driven Agents Using Aggregative Fitness
GA”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 39, No. 3, pp.
284–298, May 2009.
45
925. R. Nandan, R. Rai, R. Jayakanth, S. Moitra, N. Chakraborti and A. Mukhopadhyay, “Regulating crown and flatness during hot rolling: A multiobjective optimization study using genetic algorithms”, Materials and Manufacturing Processes,
Vol. 20, No. 3, pp. 459–478, 2005.
926. A. Kumar, D. Sahoo, S. Chakraborty and N. Chakraborti. “Gas injection in steelmaking vessels: Coupling a fluid
dynamic analysis with a genetic algorithms-based pareto-optimality”, Materials and Manufacturing Processes, Vol. 20,
No. 3, pp. 363–379, 2005.
927. C. Romero, S. Ventura and P. De Bra, “Knowledge discovery with genetic programming for providing feedback to
courseware authors”, User Modeling and User-Adapted Interaction, Vol. 14, No. 5, pp. 425–464, 2004.
928. J. Mendoza, R. Lopez and D. Morales, “Minimal loss reconfiguration using genetic algorithms with restricted population
and addressed operators: Real application”, IEEE Transactions on Power Systems, Vol. 21, No. 2, pp. 948–954, May
2006.
929. S.H. Sun, K.F. Man, B.Z. Wang, et al., “An optimazed wideband quarter-wave patch antenna design”, IEEE Antennas
and Wireless Propagation Letters, Vol. 4, pp. 486–488, 2005.
930. J.A. Covas and A. Gaspar-Cunha, “Optimisation-based design of extruders” , Plastics Rubber and composites, Vol. 33,
No. 9-10, pp. 416–425, 2004.
931. M. Koppen, “On the benchmarking of multiobjective optimization algorithm”, Knowledge-Based Intelligent Information
and Engineering Systems, Pt 1, Proceedings, pp. 379–385, Springer, Lecture Notes in Artificial Intelligence Vol. 2773,
2003.
932. P. Kumar, D. Gospodaric and P. Bauer, “Improved genetic algorithm inspired by biological evolution”, Soft Computing,
Vol. 11, No. 10, pp. 923–941, August 2007.
933. Christian Igel, Nikolaus Hansen and Stefan Roth, “Covariance Matrix Adaptation for Multi-objective Optimization”,
Evolutionary Computation, Vol. 15, No. 1, pp. 1–28, Spring 2007.
934. S.R. Jangam and N. Chakraborti, “A novel method for alignment of two nucleic acid sequences using ant colony optimization and genetic algorithms”, Applied Soft Computing, Vol. 7, No. 3, pp. 1121–1130, June 2007.
935. Martin Josef Geiger, “On operators and search space topology in multi-objective flow shop scheduling”, European Journal
of Operational Research, Vol. 181, No. 1, pp. 195–206, August 16, 2007.
936. T.M. Chan, K.F. Man, K.S. Tang and S. Kwong, “A jumping-genes paradigm for optimizing factory WLAN network”,
IEEE Transactions on Industrial Informatics, Vol. 3, No. 1, pp. 33–43, February 2007.
937. E.-G. Talbi, S. Cahon and N. Melab, “Designing cellular networks using a parallel hybrid metaheuristic on the computational grid”, Computer Communications, Vol. 30, No. 4, pp. 698–713, February 26, 2007.
938. K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali and A. Jamali, “Modelling and multi-objective optimization
of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms”, Energy
Conversion and Management, Vol. 48, No. 3, pp. 1029–1041, March 2007.
939. Karl Doerner, Axel Focke and Walter J. Gutjahr, “Multicriteria tour planning for mobile healthcare facilities in a
developing country”, European Journal of Operational Research, Vol. 179, No. 3, pp. 1078–1096, June 16, 2007.
940. Hisao Ishibuchi and Yusuke Nojima, “Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective
fuzzy genetics-based machine learning”, International Journal of Approximate Reasoning, Vol. 44, No. 1, pp. 4–31,
January 2007.
941. L. Grandinetti, F. Guerriero, G. Lepera and M. Mancini, “A niched genetic algorithm to solve a pollutant emission
reduction problem in the manufacturing industry: A case study”, Computers & Operations Research, Vol. 34, No. 7,
pp. 2191–2214, July 2007.
942. Brian J. Ross and Eduardo Zuviria, “Evolving dynamic Bayesian networks with multi-objective genetic algorithms”,
Applied Intelligence, Vol. 26, No. 1, pp. 13–23, February 2007.
943. Lam T. Bui, Kalyanmoy Deb, Hussein A. Abbass and Daryl Essam, “Dual Guidance in Evolutionary Multi-objective
Optimization by Localization”, Simulated Evolution and Learning, SEAL 2006, pp. 384–391, Springer, Lecture Notes in
Computer Science Vol. 4247, Hefei, China, October, 2006.
944. Miguel A. Martinez, Javier Sanchis and Xavier Blasco, “Multiobjective controller design handling human preferences”,
Engineering Applications of Artificial Intelligence, Vol. 19, No. 8, pp. 927–938, December 2006.
945. Pedro P.B. de Oliveira, Jose C. Bortot and Gina M. B. Oliveira, “The best currently known class of dynamically
equivalent cellular automata rules for density classification”, Neurocomputing, Vol. 70, Nos. 1–3, pp. 35–43, December
2006.
946. Hisao Ishibuchi, Yusuke Nojima and Isao Kuwajima, “Finding simple fuzzy classification systems with high interpretability through multiobjective rule selection”, Knowledge-Based Intelligent Information and Engineering Systems,
Pt 2, Proceedings, pp. 86–93, Springer, Lecture Notes in Artificial Intelligence Vol. 4252, 2006.
46
947. Thomas Hanne, “A multiobjective evolutionary algorithm for approximating the efficient set”, European Journal of
Operational Research, Vol. 176, No. 3, pp. 1723–1734, February 1, 2007.
948. Kazi Shah Nawaz Ripon, Sam Kwong and K. F. Man, “A real-coding jumping gene genetic algorithm (RJGGA) for
multiobjective optimization”, Information Sciences, Vol. 177, No. 2, pp. 632–654, January 15, 2007.
949. Kalyanmoy Deb and Himanshu Gupta, “Introducing robustness in multi-objective optimization”, Evolutionary Computation, Vol. 14, No. 4, pp. 463–494, Winter 2006.
950. M. Ali-Tavoli, N. Nariman-Zadeh, A. Khakhali and M. Mehran, “Multi-objective optimization of abrasive flow machining
processes using polynomial neural networks and genetic algorithms”, Machining Science and Technology, Vol. 10, No.
4, pp. 491–510, October-December 2006.
951. F. Pettersson, N. Chakraborti and H. Saxén, “A genetic algorithms based multi-objective neural net applied to noisy
blast furnace data”, Applied Soft Computing, Vol. 7, pp. 387–397, 2007.
952. Dimo Brockhoff and Eckart Zitzler, “Are All Objectives Necessary? On Dimensionality Reduction in Evolutionary
Multiobjective Optimization”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós,
L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference,
pp. 533–542, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
953. Hernán E. Aguirre and Kiyoshi Tanaka, “Working principles, behavior, and performance of MOEAs on MNK-landscapes”,
European Journal of Operational Research, Vol. 181, No. 3, pp. 1670–1690, 16 September, 2007.
954. Pradyumn Kumar Shukla and Kalyanmoy Deb, “On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods” European Journal of Operational Research, Vol. 181, No. 3, pp. 1630–1652, 16 September,
2007.
955. Hiroyuki Sato, Hernán E. Aguirre and Kiyoshi Tanaka, “Local dominance and local recombination in MOEAs on 0/1
multiobjective knapsack problems”, European Journal of Operational Research, Vol. 181, No. 3, pp. 1708–1723, 16
September, 2007.
956. Julia Handl and Joshua Knowles, “An Evolutionary Approach to Multiobjective Clustering”, IEEE Transactions on
Evolutionary Computation, Vol. 11, No. 1, pp. 56–76, February 2007.
957. Francesco di Pierro, Shoon-Thiam Khu and Dragan A. Savic, “An Investigation on Preference Order Ranking Scheme
for Multiobjective Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 11, No. 1, pp.
17–45, February 2007.
958. C. Garcı́a-Martı́nez, O. Cordón and F. Herrera, “A taxonomy and an empirical analysis of multiple objective ant colony
optimization algorithms for the bi-criteria TSP”, European Journal of Operational Research, Vol. 180, No. 1, pp.
116–148, July 1, 2007.
959. Darı́o Maravall and Javier de Lope, “Multi-objective dynamic optimization with genetic algorithms for automatic parking”, Soft Computing, Vol. 11, No. 3, pp. 249–257, February 2007.
960. J.K.L. Wong, A.J. Mason, M.J. Neve and K.W. Sowerby, “Base station placement in indoor wireless systems using
binary integer programming”, IEE Proceedings—Communications, Vol. 153, No. 5, pp. 771–778, October 2006.
961. L. Araujo, “Multiobjective Genetic Programming for Natural Language Parsing and Tagging”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós, L. Darrell Whitley and Xin Yao (editors), Parallel
Problem Solving from Nature - PPSN IX, 9th International Conference, pp. 433–442, Springer. Lecture Notes in
Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
962. P.A. Castillo, M.G. Arenas, J.J. Merelo, V.M. Rivas and G. Romero, “Multiobjective Optimization of Ensembles of
Multilayer Perceptrons for Pattern Classification”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke,
Juan J. Merelo-Guervós, L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX,
9th International Conference, pp. 453–462, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland,
September 2006.
963. Mike Preuss, Boris Naujoks and Günter Rudolph, “Pareto Set and EMOA Behavior for Simple Multimodal Multiobjective
Functions”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós, L. Darrell Whitley
and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, pp. 513–522,
Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
964. Cagkan Erbas, Selin Cerac-Erbas and Andy D. Pimentel, “Multiobjective Optimization and Evolutionary Algorithms
for the Application Mapping Problem in Multiprocessor System-on-Chip Design”, IEEE Transactions on Evolutionary
Computation, Vol. 10, No. 3, pp. 358–374, June 2006.
965. Min Liu and Dan M. Frangopol, “Optimizing bridge network maintenance management under uncertainty with conflicting
criteria: Life-cycle maintenance, failure, and user costs”, Journal of Structural Engineering–ASCE, Vol. 132, No. 11,
pp. 1835–1845, November 2006.
966. Fabio Freschi and Maurizio Repetto, “VIS: an artificial immune network for multi-objective optimization”, Engineering
Optimization, Vol. 38, No. 8, pp. 975–996, December 2006.
47
967. B. Qian, L. Wang, D.X. Huang and X. Wang, “Multi-objective flow shop scheduling using differential evolution”,
Intelligent Computing in Signal Processing and Pattern Recognition, Springer-Verlag, pp. 1125–1136, Lecture Notes in
Control and Information Sciences Vol. 345, 2006.
968. F. Luna, A.J. Nebro and E. Alba, “Observations in using Grid-enabled technologies for solving multi-objective optimization problems”, Parallel Computing, Vol. 32, Nos. 5-6, pp. 377–393, June 2006.
969. E. Nobile, F. Pinto and G. Rizzetto, “Geometric parameterization and multiobjective shape optimization of convective
periodic channels”, Numerical Heat Transfer Part B–Fundamentals, Vol. 50, No. 5, pp. 425–453, November 2006.
970. J.G. Villegas, F. Palacios and A.L. Medaglia, “Solution methods for the bi-objective (cost-coverage) unconstrained
facility location problem with an illustrative example”, Annals of Operations Research, Vol. 147, No. 1, pp. 109–141,
October 2006.
971. D.T. Pham and M. Castellani, “Evolutionary learning of fuzzy models”, Engineering Applications of Artificial Intelligence, Vol. 19, No. 6, pp. 583–592, September 2006.
972. K.C. Tan, Y.J. Yang and C.K. Goh, “A Distributed Cooperative Coevolutionary Algorithm for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 5, pp. 527–549, October 2006.
973. J.L. Bernal-Agustin, R. Dufo-Lopez and D.M. Rivas-Ascaso, “Design of isolated hybrid systems minimizing costs and
pollutant emissions”, Renewable Energy, Vol. 31, No. 14, pp. 2227–2244, November 2006.
974. F. Jimenez, J.M. Cadenas, G. Sanchez, A.F. Gomez-Skarmeta and J.L. Verdegay, “Multi-objective evolutionary computation and fuzzy optimization”, International Journal of Approximate Reasoning, Vol. 43, No. 1, pp. 59–75, September
2006.
975.
976. F. Berlanga, M.J. del Jesus, P. Gonzalez, F. Herrera and M. Mesonero, “Multiobjective evolutionary induction of subgroup discovery fuzzy rules: A case study in marketing”, in P. Perner (Editor), Advances in Data Mining - Applications in
Medicine, Web Mining, Marketing, Image and Signal Mining, pp. 337–349, Springer-Verlag, Lecture Notes in Artificial
Intelligence Vol. 4065, Leipzig, Germany, July 14-15, 2006.
977. A.S. Kurup, K. Hidajat and A.K. Ray, “Comparative study of modified simulated moving bed systems at optimal
conditions for the separation of ternary mixtures of xylene isomers”, Industrial & Engineering Chemistry Research, Vol.
45, No. 18, pp. 6251–6265, August 30, 2006.
978. T. Biondi, A. Ciccazzo, V. Cutello, S. D’Antona, G. Nicosia and S. Spinella, “Multi-objective evolutionary algorithms
and pattern search methods for circuit design problems”, Journal of Universal Computer Science, Vol. 12, No. 4, pp.
432–449, 2006.
979. R. Kumar and N. Banerjee, “Analysis of a Multiobjective Evolutionary Algorithm on the 0-1 knapsack problem”,
Theoretical Computer Science, Vol. 358, No. 1, pp. 104–120, July 31, 2006.
980. Y. Tang, P. Reed and T. Wagener, “How effective and efficient are multiobjective evolutionary algorithms at hydrologic
model calibration?”, Hydrology and Earth System Sciences, Vol. 10, No. 2, pp. 289–307, 2006.
981. J.B. Kollat and P.M. Reed, “Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design”, Advances in Water Resources, Vol. 29, No. 6, pp. 792–807, June 2006.
982. B.M. Hodge, F. Pettersson and N. Chakraborti, “Re-evaluation of the optimal operating conditions for the primary end
of an integrated steel plant using multi-objective genetic algorithms and Nash equilibrium”, Steel Research International,
Vol. 77, No. 7, pp. 459–461, July 2006.
983. P. Nikitas, A. Pappa-Louisi and P. Agrafiotou, “Multilinear gradient elution optimisation in reversed-phase liquid chromatography using genetic algorithms”, Journal of Chromatography A, Vol. 1120, Nos. 1–2, pp. 299–307, July 7, 2006.
984. L. Siwik and M. Kisiel-Dorohinicki, “Semi-elitist evolutionary multi-agent system for multiobjective optimization”,
Computational Science – ICCS 2006, Pt 3, Proceedings, pp. 831–838, Springer-Verlag, Lecture Notes in Computer
Science Vol. 3993, 2006.
985. J. Balicki, “Negative selection with ranking procedure in tabu-based multi-criterion evolutionary algorithm for task
assignment”, Computational Science - ICCS 2006, Pt 3, Proceedings, pp. 863–870, Springer-Verlag, Lecture Notes in
Computer Science Vol. 3993, 2006.
986. N. Nariman-Zadeh, A. Darvizeh and A. Jamali, “Pareto optimization of energy absorption of square aluminium columns
using multi-objective genetic algorithms”, Proceedings of the Institution of Mechanical Engineers Part B–Journal of
Engineering Manufacture, Vol. 220, No. 2, pp. 213–224, February 2006.
987. P. Lacomme, C. Prins and M. Sevaux, “A genetic algorithm for a bi-objective capacitated arc routing problem”, Computers & Operations Research, Vol. 33, No. 12, pp. 3473–3493, December 2006.
988. H.S. Kim and P.N. Roschke, “Fuzzy control of base-isolation system using multi-objective genetic algorithm”, ComputerAided Civil and Infrastructure Engineering, Vol. 21, No. 6, pp. 436–449, August 2006.
48
989. R. Romero-Zaliz, C. Rubio-Escudero, O. Cordon, O. Harari, C. del Val and I. Zwir, “Mining structural databases:
An evolutionary multi-objetive conceptual clustering methodology”, in Applications of Evolutionary Computing, pp.
159–171, Springer, Lecture Notes in Computer Science Vol. 3907, 2006.
990. Giuseppe Ascia, Vincenzo Catania and Maurizio Palesi, “A multi-objective genetic approach to mapping problem on
Network-on-Chip”, Journal of Universal Computer Science, Vol. 12, No. 4, pp. 370–394, 2006.
991. M.A. Elsays, M. Naguib Aly and A.A. Badawi, “Design optimization of shell-and-tube heat exchangers using single
objective and multiobjective particle swarm optimization”, Kerntechnik, Vol. 75, Nos. 1–2, pp. 38–46, March 2010.
992. Pedro G. Espejo, Sebastian Ventura and Francisco Herrera, “A Survey on the Application of Genetic Programming to
Classification”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 40, No.
2, pp. 121–144, March 2010.
993. Hans Ole Rafaelsen, Frank Eliassen and Sharath Babu Musunoori, “Towards self-organizing distribution structures for
streaming media”, in R. Meersman and Z. Tari (editors), On the Move to Meaningful Internet Systems 2006: COOPIS,
DOA, GADA, and ODBASE, pp. 1825–1842, Springer, Lecture Notes in Computer Science Vol. 4276, 2006.
994. Shin Yoo and Mark Harman, “Using hybrid algorithm for Pareto efficient multi-objective test suite minimisation”,
Journal of Systems and Software, Vol. 83, No. 4, pp. 689–701, April 2010.
995. Anna Syberfeldt, Amos Ng, Robert I. John and Philip Moore, “Evolutionary optimisation of noisy multi-objective
problems using confidence-based dynamic resampling”, European Journal of Operational Research, Vol. 204, No. 3, pp.
533–544, August 1, 2010.
996. Tobias Friedrich, Nils Hebbinghaus and Frank Neumann, “Plateaus can be harder in multi-objective optimization”,
Theoretical Computer Science, Vol. 411, No. 6, pp. 854–864, February 6, 2010.
997. Sonda Elloumi and Philippe Fortemps, “A hybrid rank-based evolutionary algorithm applied to multi-mode resourceconstrained project scheduling problem”, European Journal of Operational Research, Vol. 205, No. 1, pp. 31–41, August
16, 2010.
998. Rajeev Kumar and P.K. Singh, “Assessing solution quality of biobjective 0-1 knapsack problem using evolutionary and
heuristic algorithms”, Applied Soft Computing, Vol. 10, No. 3, pp. 711–718, June 2010.
999. Manojkumar Ramteke and Santosh K. Gupta, “Biomimetic Adaptation of the Evolutionary Algorithm, NSGA-II-aJG,
Using the Biogenetic Law of Embryology for Intelligent Optimization”, Industrial & Engineering Chemistry Research,
Vol. 48, No. 17, pp. 8054–8067, September 2, 2009.
1000. Michael Dellnitz, Sina Ober-Blobaum, Marcus Post, Oliver Schütze and Bianca Thiere, “A multi-objective approach to
the design of low thrust space trajectories using optimal control”, Celestial Mechanics & Dynamical Astronomy, Vol.
105, Nos. 1–3, pp. 33–59, November 2009.
1001. Jessica A. Carballido, Ignacio Ponzoni and Nelida B. Brignole, “SID-GA: An evolutionary approach for improving
observability and redundancy analysis in structural instrumentation design”, Computers & Industrial Engineering, Vol.
56, No. 4, pp. 1419–1428, May 2009.
1002. X. B. Lam, Y.S. Kim, A.D. Hoang and C.W. Park, “Coupled Aerostructural Design Optimization Using the Kriging
Model and Integrated Multiobjective Optimization Algorithm”, Journal of Optimization Theory and Applications, Vol.
142, No. 3, pp. 533–556, September 2009.
1003. Robert D. Clark and Edmond Abrahamian, “Using a staged multi-objective optimization approach to find selective
pharmacophore models”, Journal of Computer-Aided Molecular Design, Vol. 23, No. 11, pp. 765–771, November 2009.
1004. G. Nildem Demir, A. Sima Uyar and Sule Gunduz-Oguducu, “Multiobjective evolutionary clustering of Web user sessions:
a case study in Web page recommendation”, Soft Computing, Vol. 14, No. 6, pp. 579–597, April 2010.
1005. K.H. Gudmundsson, F. Jonsdottir and F. Thorsteinsson, “A geometrical optimization of a magneto-rheological rotary
brake in a prosthetic knee”, Smart Materials & Structures, Vol. 19, No. 3, Article Number: 035023, March 2010.
1006. S.H. Yeung and K.F. Man, “Narrow Band-Stop Filters Design with I-Shape Resonators”, Microwave and Optical Technology Letters, Vol. 52, No. 3, pp. 757–763, March 2010.
1007. P. Rocca, M. Benedetti, M. Donelli, D. Franceschini and A. Massa, “Evolutionary optimization as applied to inverse
scattering problems”, Inverse Problems, Vol. 25, No. 12, Article Number: 123003, December 2009.
1008. Manojkumar Ramteke and Santosh K. Gupta, “Biomimicking Altruistic Behavior of Honey Bees in Multi-objective
Genetic Algorithm”, Industrial & Engineering Chemistry Research, Vol. 48, No. 21, pp. 9671–9685, November 4, 2009.
1009. Ujjwal Maulik, Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay, “Finding Multiple Coherent Biclusters in
Microarray Data Using Variable String Length Multiobjective Genetic Algorithm”, IEEE Transactions on Information
Technology in Biomedicine, Vol. 13, No. 6, pp. 969–975, November 2009.
1010. M.H. Kobayashi, H-T. C. Pedro, R.M. Kolonay and G.W. Reich, “On a cellular division method for aircraft structural
design”, Aeronautical Journal, Vol. 113, No. 1150, pp. 821–831, December 2009.
49
1011. David Daum and Nicolas Morel, “Identifying important state variables for a blind controller”, Building and Environment,
Vol. 45, No. 4, pp. 887–900, April 2010.
1012. Chung Min Kwan and C.S. Chang, “Timetable synchronization of mass rapid transit system using multiobjective evolutionary approach”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 38,
No. 5, pp. 636–648, September 2008.
1013. S. Ganguly, S. Datta, P.P. Chattopadhyay and N. Chakraborti, “Designing the Multiphase Microstructure of Steel for
Optimal TRIP Effect: A Multiobjective Genetic Algorithm Based Approach”, Materials and Manufacturing Processes,
Vol. 24, No. 1, pp. 31–37, 2009.
1014. Arijit Biswas, N. Chakraborti and P.K. Sen, “Multiobjective Optimization of Manganese Recovery from Sea Nodules
Using Genetic Algorithms”, Materials and Manufacturing Processes, Vol. 24, No. 1, pp. 22–30, 2009.
1015. Nicolas Jozefowiez, Frederic Semet and El-Ghazali Talbi, “An evolutionary algorithm for the vehicle routing problem
with route balancing”, European Journal of Operational Research, Vol. 195, No. 3, pp. 761–769, June 16, 2009.
1016. Lino J. Alvarez-Vazquez, Eva Balsa-Canto and Aurea Martinez, “Optimal design and operation of a wastewater purification system”, Mathematics and Computers in Simulation, Vol. 79, No. 3, pp. 668–682, December 1, 2008.
1017. A. Rama Mohan Rao and P.P. Shyju, “A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of Hybrid
Laminate Composite Structures”, Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 3, pp. 149–170,
April 2010.
1018. G. Nildem Demir, A. Şima Uyar and Şule Gündüz-Öǧüdücü, “Multiobjective evolutionary clustering of Web user sessions:
a case study in Web page recommendation”, Soft Computing - A Fusion of Foundations, Methodologies and Applications,
Vol. 14, No. 6, pp. 579–597, January, 2010.
1019. Kaushik Suresh, Debarati Kundu, Sayan Ghosh, Swagatam Das and Ajith Abraham, “Data Clustering Using Multiobjective Differential Evolution Algorithms”, Fundamenta Informaticae, Vol. 97, No. 4, pp. 381–403, 2009.
1020. Kaushik Suresh, Debarati Kundu, Sayan Ghosh, Swagatam Das, Ajith Abraham and Sang Yong Han, “Multi-Objective
Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis”, Sensors, Vol. 9, No.
5, pp. 3981–4004, May 2009.
1021. Silvia Curteanu and Maria Cazacu, “Optimization of a Polysiloxane Synthesis Process using Artificial Intelligence Methods”, Revue Roumaine de Chimie, Vol. 53, No. 12, pp. 1141–1148, December 2008.
1022. Zhanpeng Jin and Allen C. Cheng, “Evolutionary Benchmark Subsetting”, IEEE Micro, Vol. 28, No. 6, pp. 20–36,
November-December 2008.
1023. Zhen Gao, Dan Zhang and Yunjian Ge, “Design optimization of a spatial six degree-of-freedom parallel manipulator
based on artificial intelligence approaches”, Robotics and Computer-Integrated Manufacturing, Vol. 26, No. 2, pp.
180–189, April 2010.
1024. Luis Gerardo de la Fraga and Oliver Schutze, “Direct Calibration by Fitting of Cuboids to a Single Image Using
Differential Evolution”, International Journal of Computer Vision, Vol. 81, No. 2, pp. 119–127, February 2009.
1025. Chris Thachuk, Jose Crossa, Jorge Franco, Susanne Dreisigacker, Marilyn Warburton and Guy F. Davenport, “Core
Hunter: an algorithm for sampling genetic resources based on multiple genetic measures”, BMC Bioinformatics, Vol.
10, Article Number 243, August 6, 2009.
1026. Babak Forouraghi, “Optimal tolerance allocation using a multiobjective particle swarm optimizer”, International Journal
of Advanced Manufacturing Technology, Vol. 44, Nos. 7–8, pp. 710–724, October 2009.
1027. Yusuke Nojima, Hisao Ishibuchi and Isao Kuwajima, “Parallel distributed genetic fuzzy rule selection”, Soft Computing,
Vol. 13, No. 5, pp. 511–519, March 2009.
1028. A.F. Carazo, Trinidad Gomez, Julian Molina, Alfredo G. Hernandez-Diaz, Flor M. Guerrero and Rafael Caballero,
“Solving a comprehensive model for multiobjective project portfolio selection”, Computers & Operations Research, Vol.
37, No. 4, pp. 630–639, April 2010.
1029. Eduardo Fernandez, Jorge Navarro and Sergio Bernal, “Handling multicriteria preferences in cluster analysis”, European
Journal of Operational Research, Vol. 202, No. 3, pp. 819–827, May 1, 2010.
1030. Leila Dridi, Alain Mailhot, Marc Parizeau and Jean-Pierre Villeneuve, “Multiobjective Approach for Pipe Replacement
Based on Bayesian Inference of Break Model Parameters”, Journal of Water Resources Planning and Management–
ASCE, Vol. 135, No. 5, pp. 344–354, September-October 2009.
1031. Francisco Martinez-Lopez and Jorge Casillas, “Marketing Intelligent Systems for consumer behaviour modelling by a
descriptive induction approach based on Genetic Fuzzy Systems”, Industrial Marketing Management, Vol. 38, No. 7,
pp. 714–731, October 2009.
1032. David Greiner, Juan J. Aznarez, Orlando Maeso and Gabriel Winter, “Single- and multi-objective shape design of Ynoise barriers using evolutionary computation and boundary elements”, Advances in Engineering Software, Vol. 41, No.
2, pp. 368–378, February 2010.
50
1033. Axel Soto, Rocio L. Cecchini, Gustavo E. Vazquez and Ignacio Ponzoni, “Multi-Objective Feature Selection in QSAR
Using a Machine Learning Approach”, QSAR & Combinatorial Science, Vol. 28, Nos. 11–12, pp. 1509–1523, December
2009.
1034. Kishalay Mitra, “Multiobjective optimization of an industrial grinding operation under uncertainty”, Chemical Engineering Science, Vol. 64, No. 23, pp. 5043–5056, December 1, 2009.
1035. J.E. Mendoza, L.A. Villaleiva, M.A. Castro and E.A. Lopez, “Multi-objective Evolutionary Algorithms for DecisionMaking in Reconfiguration Problems Applied to the Electric Distribution Networks”, Studies in Informatics and Control,
Vol. 18, No. 4, pp. 325–336, December 2009.
1036. A. Liefooghe, L. Jourdan and E.-G. Talbi, “Metaheuristics and cooperative approaches for the Bi-objective Ring Star
Problem”, Computers & Operations Research, Vol. 37, No. 6, pp. 1033–1044, June 2010.
1037. Jeffrey S. Parker and George H. Born, “Direct Lunar Halo Orbit Transfers”, Journal of the Astronautical Sciences, Vol.
56, No. 4, pp. 441–476, October-December 2008.
1038. K.P. Anagnostopoulos and G. Mamanis, “A portfolio optimization model with three objectives and discrete variables”,
Computers & Operations Research, Vol. 37, No. 7, pp. 1285–1297, July 2010.
1039. Nicola Beume, “S-Metric Calculation by Considering Dominated Hypervolume as Klee’s Measure Problem”, Evolutionary
Computation, Vol. 17, No. 4, pp. 477–492, Winter 2009.
1040. J.R. Kasprzyk, P.M. Reed, B.R. Kirsch and G.W. Characklis, “Managing population and drought risks using manyobjective water portfolio planning under uncertainty”, Water Resources Research, Vol. 45, Article Number: W12401,
December 3, 2009.
1041. M.H. Khoshgoftar Manesh and Majid Amidpour, “Multi-objective thermoeconomic optimization of coupling MSF desalination with PWR nuclear power plant through evolutionary algorithms”, Desalination, Vol. 249, No. 3, pp. 1332–1344,
December 25, 2009.
1042. Jacek Zak, Andrzej Jaszkiewicz and Adam Redmer, “Multiple Criteria Optimization Method for the Vehicle Assignment
Problem in a Bus Transportation Company”, Journal of Advanced Transportation, Vol. 43, No. 2, pp. 203–243, 2009.
1043. J.A. Covas and A. Gaspar-Cunha, “Extrusion Scale-up: An Optimization-based Methodology”, International Polymer
Processing, Vol. 24, No. 1, pp. 67–82, March 2009.
1044. Arijit Biswas, N. Chakraborti and P.K. Sen, “A Genetic Algorithms Based Multi-Objective Optimization Approach
Applied to a Hydrometallurgical Circuit for Ocean Nodules”, Mineral Processing and Extractive Metallurgy Review, Vol.
30, No. 2, pp. 163–189, 2009.
1045. A.M. Mora, J.J. Merelo, J.L.J. Laredo, C. Millan and J. Torrecillas, “CHAC, A MOACO Algorithm for Computation of
Bi-Criteria Military Unit Path in the Battlefield: Presentation and First Results”, International Journal of Intelligent
Systems, Vol. 24, No. 7, pp. 818–843, July 2009.
1046. A. Jamali, A. Hajiloo and N. Nariman-zadeh, “Reliability-based robust Pareto design of linear state feedback controllers
using a multi-objective uniform-diversity genetic algorithm (MUGA)”, Expert Systems with Applications, Vol. 37, No.
1, pp. 401–413, January 2010.
1047. Hamidreza Eskandari and Christopher D. Geiger, “Evolutionary multiobjective optimization in noisy problem environments”, Journal of Heuristics, Vol. 15, No. 6, pp. 559–595, December 2009.
1048. Jawed Iqbal and Chandan Guria, “Optimization of an operating domestic wastewater treatment plant using elitist nondominated sorting genetic algorithm”, Chemical Engineering Research & Design, Vol. 87, No. 11A, pp. 1481–1496,
November 2009.
1049. Juliane Muller, “Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows”, European
Journal of Operational Research, Vol. 202, No. 1, pp. 223–231, April 1, 2010.
1050. Kostas Florios, George Mavrotas and Danae Diakoulaki, “Solving multiobjective, multiconstraint knapsack problems
using mathematical programming and evolutionary algorithms”, European Journal of Operational Research, Vol. 203,
No. 1, pp. 14–21, May 16, 2010.
1051. Anirban Dhar and Bithin Datta, “Saltwater Intrusion Management of Coastal Aquifers. I: Linked Simulation-Optimization”,
Journal of Hydrologic Engineering, Vol. 14, No. 12, pp. 1263–1272, December 2009.
1052. Nicola Beume, Boris Naujoks and Guenter Rudolph, “SMS-EMOA - Effective Evolutionary Multiobjective Optimization”, AT-Automatisierungstechnik, Vol. 56, No. 7, pp. 357–364, 2008.
1053. M. Pouraghaie, K. Atashkari, S.M. Besarati and N. Nariman-Zadeh, “Thermodynamic performance optimization of a
combined power/cooling cycle”, Energy Conversion and Management, Vol. 51, No. 1, pp. 204–211, January 2010.
1054. Anthony Chen, Juyoung Kim, Seungjae Lee and Youngchan Kim, “Stochastic multi-objective models for network design
problem”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1608–1619, March 2010.
1055. Ioannis C. Kampolis and Kyriakos C. Giannakoglou, “Distributed evolutionary algorithms with hierarchical evaluation”,
Engineering Optimization, Vol. 41, No. 11, pp. 1037–1049, November 2009.
51
1056. Patrick M. Reed, Joshua B. Kollat, Matthew P. Ferringer and Timothy G. Thompson, “Parallel Evolutionary MultiObjective Optimization on Large, Heterogeneous Clusters: An Applications Perspective”, Journal of Aerospace Computing Information and Communication, Vol. 5, No. 11, pp. 460–478, 2008.
1057. N. Chakraborti, S. Moitra, A. Mitra and A. Mukhopadhyay, “Evolutionary and genetic algorithms applied to hot rolling:
A multi-objective rolling schedule studied using particle swarm algorithm”, Transactions of the Indian Institute of Metals,
Vol. 59, No. 5, pp. 681–688, October 2006.
1058. Yujia Wang and Yupu Yang, “Particle swarm optimization with preference order ranking for multi-objective optimization”, Information Sciences, Vol. 179, No. 12, pp. 1944–1959, May 30, 2009.
1059. Yujia Wang and Yupu Yang, “Particle swarm with equilibrium strategy of selection for multi-objective optimization”,
European Journal of Operational Research, Vol. 200, No. 1, pp. 187–197, January 1, 2010.
1060. Baidurya Bhattacharya, G.R. Dinesh Kumar, Akash Agarwal, Sakir Erkoc, Arunima Singh and Nirupam Chakraborti,
“Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms”,
Computational Materials Science, Vol. 46, No. 4, pp. 821–827, October 2009.
1061. Aimin Zhou, Qingfu Zhang and Yaochu Jin, “Approximating the Set of Pareto-Optimal Solutions in Both the Decision
and Objective Spaces by an Estimation of Distribution Algorithm”, IEEE Transactions on Evolutionary Computation,
Vol. 13, No. 5, pp. 1167–1189, October 2009.
1062. Gisele L. Pappa and Alex A. Freitas, “Evolving rule induction algorithms with multi-objective grammar-based genetic
programming”, Knowledge and Information Systems, Vol. 19, No. 3, pp. 283–309, June 2009.
1063. Nicola Beume, Carlos M. Fonseca, Manuel Lopez-Ibanez, Luis Paquete and Jan Vahrenhold, “On the Complexity of
Computing the Hypervolume Indicator”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp. 1075–
1082, October 2009.
1064. Anthony Finkelstein, Mark Harman, S. Afshin Mansouri, Jian Ren, Yuanyuan Zhang, “A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making”, Requirements Engineering,
Vol. 14, No. 4, pp. 231–245, December 2009.
1065. A. Rama Mohan Rao and K. Lakshmi, “Multi-objective Optimal Design of Hybrid Laminate Composite Structures Using
Scatter Search”, Journal of Composite Materials, Vol. 43, No. 20, pp. 2157–2182, September 2009.
1066. Mario Camara, Julio Ortega and Francisco de Toro, “A single front genetic algorithm for parallel multi-objective optimization in dynamic environments”, Neurocomputing, Vol. 72, Nos. 16–18, pp. 3570–3579, October 2009.
1067. Parames Chutima and Penpak Pinkoompee, “Multi-objective sequencing problems of mixed-model assembly systems
using memetic algorithms”, Scienceasia, Vol. 35, No. 3, pp. 295–305, September 2009.
1068. Lam T. Bui, Hussein A. Abbass and Daryl Essam, “Localization for Solving Noisy Multi-Objective Optimization Problems”, Evolutionary Computation, Vol. 17, No. 3, pp. 379–409, Fall 2009.
1069. Aimin Zhou, Qingfu Zhang and Yaochu Jin, “Approximating the Set of Pareto-Optimal Solutions in Both the Decision
and Objective Spaces by an Estimation of Distribution Algorithm”, IEEE Transactions on Evolutionary Computation,
Vol. 13, No. 5, pp. 1167–1189, October 2009.
1070. Nicola Beume, Carlos M. Fonseca, Manuel Lopez-Ibanez, Luis Paquete and Jan Vahrenhold, “On the Complexity of
Computing the Hypervolume Indicator”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp. 1075–
1082, October 2009.
1071. Jiaquan Gao and Jun Wang, “WBMOAIS: A novel artificial immune system for multiobjective optimization”, Computers
& Operations Research, Vol. 37, No. 1, pp. 50–61, January 2010.
1072. Rafael Alcala, Pietro Ducange, Francisco Herrera, Beatrice Lazzerini and Francesco Marcelloni, “A Multiobjective
Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems”, IEEE
Transactions on Fuzzy Systems, Vol. 17, No. 5, pp. 1106–1122, October 2009.
1073. Anthony Chen, Kitti Subprasom and Zhaowang Ji, “A simulation-based multi-objective genetic algorithm (SMOGA)
procedure for BOT network design problem”, Optimization and Engineering, Vol. 7, No. 3, pp. 225–247, September
2006.
1074. Yang Zhang and Peter Rockett, “A Comparison of three evolutionary strategies for multiobjective genetic programming”,
Artificial Intelligence Review, Vol. 27, Nos. 2–3, pp. 149–163, March 2007.
1075. Manojkumar Ramteke and Santosh K. Gupta, “Multiobjective Optimization of an Industrial Nylon-6 Semi Batch Reactor
Using the a-Jumping Gene Adaptations of Genetic Algorithm and Simulated Annealing”, Polymer Engineering and
Science, Vol. 48, No. 11, pp. 2198–2215, November 2008.
1076. Andrzej Jaszkiewicz and Piotr Zielniewicz, “Pareto memetic algorithm with path relinking for bi-objective traveling
salesperson problem”, European Journal of Operational Research, Vol. 193, No. 3, pp. 885–890, March 16, 2009.
1077. Jose L. Ceciliano Meza, Mehmet Bayram Yildirim and Abu S.M. Masud, “A Multiobjective Evolutionary Programming
Algorithm and Its Applications to Power Generation Expansion Planning”, IEEE Transactions on Systems, Man, and
Cybernetics, Part A–Systems and Humans, Vol. 39, No. 5, pp. 1086–1096, September 2009.
52
1078. Hai-Lin Liu, Yuping Wang and Yiu-Ming Cheung, “A Multi-Objective Evolutionary Algorithm using Min-Max Strategy
and Sphere Coordinate Transformation”, Intelligent Automation and Soft Computing, Vol. 15, No. 3, pp. 361–384,
2009.
1079. Hussein A. Abbass, Sameer Alam and Axel Bender, “MEBRA: Multiobjective Evolutionary-Based Risk Assessment”,
IEEE Computational Intelligence Magazine, Vol. 4, No. 3, pp. 29–36, August 2009.
1080. K.F. Doerner, W.J. Gutjahr, R.F. Hartl, C. Strauss and C. Stummer, “Nature-inspired metaheuristics for multiobjective
activity crashing”, Omega–International Journal of Management Science, Vol. 36, No. 6, pp. 1019–1037, December
2008.
1081. Petra Kersting and Andreas Zabel, “Optimizing NC-tool paths for simultaneous five-axis milling based on multipopulation multi-objective evolutionary algorithms”, Advances in Engineering Software, Vol. 40, No. 6, pp. 452–463,
June 2009.
1082. A. Jamali, N. Nariman-zadeh, A. Darvizeh, A. Masoumi and S. Hamrang, “Multi-objective evolutionary optimization
of polynomial neural networks for modelling and prediction of explosive cutting process”, Engineering Applications of
Artificial Intelligence, Vol. 22, Nos. 4-5, pp. 676–687, June 2009.
1083. Vijay Pratap Singh, Bertrand Duquet, Michel Leger and Marc Schoenauer, “Automatic wave-equation migration velocity
inversion using multiobjective evolutionary algorithms”, Geophysics, Vol. 73, No. 5, pp. 61–73, September-October 2008.
1084. Jose L. Bernal-Agustin and Rodolfo Dufo-Lopez, “Multi-objective design and control of hybrid systems minimizing costs
and unmet load”, Electric Power Systems Research, Vol. 79, No. 1, pp. 170–180, January 2009.
1085. Christos Baloukas, Jose L. Risco-Martin, David Atienza, Christophe Poucet, Lazaros Papadopoulos, Stylianos Mamagkakis, Dimitrios Soudris, J. Ignacio Hidalgo, Francky Catthoor and Juan Lanchares, “Optimization methodology
of dynamic data structures based on genetic algorithms for multimedia embedded systems”, Journal of Systems and
Software, Vol. 82, No. 4, pp. 590–602, April 2009.
1086. Wei Wei, Yixiong Feng, Jianrong Tan and Zhongkai Li, “Product platform two-stage quality optimization design based
on multiobjective genetic algorithm”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 1929–1937,
June 2009.
1087. Leila Dridi, Marc Parizeau, Alain Mailhot and Jean-Pierre Villeneuve, “Using evolutionary optimization techniques for
scheduling water pipe renewal considering a short planning horizon”, Computer-Aided Civil and Infrastructure Engineering, Vol. 23, No. 8, pp. 625–635, November 2008.
1088. David L. Overbye, “The Influence of Darwin on Evolutionary Algorithms from ”Dinner with Darwin””, American Biology
Teacher, Vol. 71, No. 2, pp. 81–83, February 2009.
1089. Ata Allah Taleizadeh, Seyed Taghi Akhavan Niaki and Mir-Bahador Aryanezhad, “A hybrid method of Pareto, TOPSIS and genetic algorithm to optimize multi-product multi-constraint inventory control systems with random fuzzy
replenishments”, Mathematical and Computer Modelling, Vol. 49, Nos. 5-6, pp. 1044–1057, March 2009.
1090. S. Afshin Mansouri, S. Hamed Hendizadeh and Nasser Salmasi, “Bicriteria scheduling of a two-machine flowshop with
sequence-dependent setup times”, International Journal of Advanced Manufacturing Technology, Vol. 40, Nos. 11–12,
pp. 1216–1226, February 2009.
1091. Mohammed Shalaby and Kazuhiro Saitou, “High-Stiffness, Lock-and-Key Heat-Reversible Locator-Snap Systems for the
Design for Disassembly”, Journal of Mechanical Design, Vol. 131, No. 4, Article Number: 041005, April 2009.
1092. Matteo Nicolini and Luigino Zovatto, “Optimal Location and Control of Pressure Reducing Valves in Water Networks”,
Journal of Water Resources Planning and Management–ASCE, Vol. 135, No. 3, pp. 178–187, May-June 2009.
1093. Mohammed M. Shalaby, Zhongde Wang, Linda L-W. Chow, Brian D. Jensen, John L. Volakis, Katsuo Kurabayashi and
Kazuhiro Saitou, “Robust Design of RF-MEMS Cantilever Switches Using Contact Physics Modeling”, IEEE Transactions on Industrial Electronics, Vol. 56, No. 4, pp. 1012–1021, April 2009.
1094. Y. Shi and R.D. Reitz, “Optimization study of the effects of bowl geometry, spray targeting, and swirl ratio for a
heavy-duty diesel engine operated at low and high load”, International Journal of Engine Research, Vol. 9, No. 4, pp.
325–346, August 2008.
1095. Alexandre M. Baltar and Darrell G. Fontane, “Use of multiobjective particle swarm optimization in water resources
management”, Journal of Water Resources Planning and Management–ASCE, Vol. 134, No. 3, pp. 257–265, May-June
2008.
1096. M.A. Elsays, M. Naguib Aly and A.A. Badawi, “Optimizing the dynamic response of the H. B. Robinson nuclear plant
using multiobjective particle swarm optimization”, Kerntechnik, Vol. 74, Nos. 1–2, pp. 70–78, April 2009.
1097. Rodolfo Dufo-Lopez and Jose L. Bernal-Agustin, “Multi-objective design of PV-wind-diesel-hydrogen-battery systems”,
Renewable Energy, Vol. 33, No. 12, pp. 2559–2572, December 2008.
1098. Asish Kumar Sharma, Chandramouli Kulshreshtha and Kee-Sun Sohn, “Discovery of New Green Phosphors and Minimization of Experimental Inconsistency Using a Multi-Objective Genetic Algorithm-Assisted Combinatorial Method”,
Advanced Functional Materials, Vol. 19, No. 11, pp. 1705–1712, June 9, 2009.
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1099. Franklin Mendoza, Jose L. Bernal-Agustin and Jose A. Dominguez-Navarro, “NSGA and SPEA applied to multiobjective
design of power distribution systems”, IEEE Transactions on Power Systems, Vol. 21, No. 4, pp. 1938–1945, November
2006.
1100. G.N. Beligiannis, C. Moschopoulos, S.D. Likothanassis, “A genetic algorithm approach to school timetabling”, Journal
of the Operational Research Society, Vol. 60, No. 1, pp. 23–42, January 2009.
1101. Benjamin Torben-Nielsen and Klaus M. Stiefel, “Systematic mapping between dendritic function and structure”, NetworkComputation in Neural Systems, Vol. 20, No. 2, pp. 59–105, 2009.
1102. J. Branke, B. Scheckenbach, M. Stein, K. Deb and H. Schmeck, “Portfolio optimization with an envelope-based multiobjective evolutionary algorithm”, European Journal of Operational Research, Vol. 199, No. 3, pp. 684–693, December
16, 2009.
1103. A.G. Lopez-Herrera, E. Herrera-Viedma and F. Herrera, “Applying multi-objective evolutionary algorithms to the automatic learning of extended Boolean queries in fuzzy ordinal linguistic information retrieval systems”, Fuzzy Sets and
Systems, Vol. 160, No. 15, pp. 2192–2205, August 1, 2009.
1104. Antonio Nebro, Juan J. Durillo, Francisco Luna, Bernabé Dorronsoro and Enrique Alba, “MOCell: A Cellular Genetic
Algorithm for Multiobjective Optimization”, International Journal of Intelligent Systems, Vol. 24, No. 7, pp. 726–746,
July 2009.
1105. Ricardo Brunelli and Christian von Lücken, “Optimal Crop Selection Using Multiobjective Evolutionary Algorithms”,
AI Magazine, Vol. 30, No. 2, pp. 96–105, Summer 2009.
1106. Brahim Aghezzaf and Mohamed Naimi, “The two-stage recombination operator and its application to the multiobjective
0/1 knapsack problem: A comparative study”, Computers & Operations Research, Vol. 36, No. 12, pp. 3247–3262,
December 2009.
1107. J.M. Herrero, S. Garcia-Nieto, X. Blasco, V. Romero-Garcia, J.V. Sanchez-Perez, L.M. Garcia-Raffi, “Optimization of
sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm”, Structural and Multidisciplinary Optimization, Vol. 39, No. 2, pp. 203–215, August 2009.
1108. Maria Jose Gacto, Rafael Alcala and Francisco Herrera, “Adaptation and application of multi-objective evolutionary
algorithms for rule reduction and parameter tuning of fuzzy rule-based systems”, Soft Computing, Vol. 13, No. 5, pp.
419–436, March 2009.
1109. R. Alcala, M.J. Gacto, F. Herrera and J. Alcala-Fdez, “A multi-objective genetic algorithm for tuning and rule selection
to obtain accurate and compact linguistic fuzzy rule-based systems”, International Journal of Uncertainty Fuzziness and
Knowledge-Based Systems, Vol. 15, No. 5, pp. 539–557, October 2007.
1110. Dimo Brockhoff, Tobias Friedrich, Nils Hebbinghaus, Christian Klein, Frank Neumann and Eckart Zitzler, “On the
Effects of Adding Objectives to Plateau Functions”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3,
pp. 591–603, July 2009.
1111. Shashi Mittal and Kalyanmoy Deb, “Optimal Strategies of the Iterated Prisoner’s Dilemma Problem for Multiple Conflicting Objectives”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 554–565, July 2009.
1112. A.G. Lopez-Herrera, E. Herrera-Viedma and F. Herrera, “A Study of the Use of Multi-Objective Evolutionary Algorithms to Learn Boolean Queries: A Comparative Study”, Journal of the American Society for Information Science and
Technology, Vol. 60, No. 6, pp. 1192–1207, June 2009.
1113. Yeboon Yun, Min Yoon and Hirotaka Nakayama, “Multi-objective optimization based on meta-modeling by using support
vector regression”, Optimization and Engineering, Vol. 10, No. 2, pp. 167–181, June 2009.
1114. V. Romero-Garcia, J.V. Sanchez-Perez, L.M. Garcia-Raffi, J.M. Herrero, S. Garcia-Nieto and X. Blasco, “Hole distribution in phononic crystals: Design and optimization”, Journal of the Acoustical Society of America, Vol. 125, No. 6, pp.
3774–3783, June 2009.
1115. Eduardo Fernandez, Jorge Navarro and Sergio Bernal, “Multicriteria sorting using a valued indifference relation under a
preference disaggregation paradigm”, European Journal of Operational Research, Vol. 198, No. 2, pp. 602–609, October
16, 2009.
1116. Dimo Brockhoff and Eckart Zitzler, “Objective Reduction in Evolutionary Multiobjective Optimization: Theory and
Applications”, Evolutionary Computation, Vol. 17, No. 2, pp. 135–166, Summer 2009.
1117. Annette Chmielewski, Boris Naujoks, Michael Janas and Uwe Clausen, “Optimizing the Door Assignment in LTLTerminals”, Transportation Science, Vol. 43, No. 2, pp. 198–210, May 2009.
1118. H.C.W. Lau, T.M. Chan, W.T. Tsui, F.T.S. Chan, G.T.S. Ho, K.L. Choy, “A fuzzy guided multi-objective evolutionary
algorithm model for solving transportation problem”, Expert Systems with Applications, Vol. 36, No. 4, pp. 8255–8268,
May 2009.
1119. Carlos Henggeler Antunes, Dulce Fernao Pires, Carlos Barrico, Alvaro Gomes and Antonio Gomes Martins, “A multiobjective evolutionary algorithm for reactive power compensation in distribution networks”, Applied Energy, Vol. 86,
Nos. 7–8, pp. 977–984, July-August 2009.
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1120. Dongdong Yang, Licheng Jiao and Maoguo Gong, “Adaptive Multi-Objective Optimization Based on Nondominated
Solutions”, Computational Intelligence, Vol. 25, No. 2, pp. 84–108, May 2009.
1121. V. Romero-Garcia, J.V. Sanchez-Perez, L.M. Garcia-Raffi, J.M. Herrero, S. Garcia-Nieto and X. Blasco, “High optimization process for increasing the attenuation properties of acoustic metamaterials by means of the creation of defects”,
Applied Physics Letters, Vol. 93, No. 22, Article Number: 223502, December 1, 2008.
Capı́tulos de Libros
• Guillermo Leguizamón and Carlos A. Coello Coello, “Multi-Objective Ant Colony Optimization: A Taxonomy
and Review of Approaches”, in Satchidanada Dehuri, Susmita Ghosh and Sung Bae Cho (editors), Integration
of Swarm Intelligence and Artificial Neural Network, Chapter 3, pp. 67–94, World Scientific, Singapore, 2011,
ISBN 978-981-4280-14-3.
1. Ruby L.V. Moritz, Enrico Reich, Maik Schwarz, Matthias Bernt and Martin Middendorf, “Refined ranking relations for
selection of solutions in multi objective metaheuristics”, European Journal of Operational Research, Vol. 243, No. 2, pp.
454–464, June 1, 2015.
2. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-Objective
Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.
• Juan Carlos Fuentes Cabrera and Carlos A. Coello Coello, “Micro-MOPSO: A Multi-Objective Particle
Swarm Optimizer that Uses a Very Small Population Size”, in Nadia Nedjah, Leandro dos Santos Coelho and
Luiza de Macedo de Mourelle (editors), Multi-Objective Swarm Intelligent Systems. Theory & Experiences,
Chapter 4, pp. 83–104, Springer, Berlin, Germany, 2010, ISBN 978-3-642-05164-7.
1. T. Krausse, J. Cullmann, P. Saile and G.H. Schmitz, “Robust multi-objective calibration strategies - possibilities for
improving flood forecasting”, Hydrology and Earth System Sciences, Vol. 16, No. 10, pp. 3579–3606, 2012.
• Alfredo Arias Montaño, Carlos A. Coello Coello and Efrén Mezura-Montes, “Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization”, in Slawomir Koziel and Xin-She Yang (editors),
Computational Optimization, Methods and Algorithms, Chapter 10, pp. 211–240, Springer, Berlin, Germany,
2011, ISBN 978-3-642-20858-4.
1. Amir Nejat, Pooya Mirzabeygi and Masoud Shariat Panahi, “Airfoil shape optimization using improved Multiobjective
Territorial Particle Swarm algorithm with the objective of improving stall characteristics”, Structural and Multidisciplinary Optimization, Vol. 49, No. 6, pp. 953–967, June 2014.
2. Ni Li, Zeya Su, Zhuming Bi, Chao Tian, Zhiming Ren and Guanghong Gong, “A supportive architecture for CFD-based
design optimisation”, Enterprise Information Systems, Vol. 8, No. 2, pp. 246–278, March 4, 2014.
• Carlos A. Coello Coello, “An Introduction to Multi-Objective Particle Swarm Optimizers”, in António
Gaspar-Cunha, Ricardo Takahashi, Gerald Schaefer and Lino Costa (editors), Soft Computing in Industrial Applications, pp. 3–12, Springer, Advances in Intelligent and Soft Computing Series, Vol. 96, Berlin,
2011, ISBN 978-3-642-20504-0.
1. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
2. J.M. Herrero, G. Reynoso-Meza, M. Martinez, X. Blasco and J. Sanchis, “A Smart-Distributed Pareto Front Using the
ev-MO GA Evolutionary Algorithm”, International Journal on Artificial Intelligence Tools, Vol. 23, No. 2, Article
Number: 1450002, April 2014.
• Luis V. Santana-Quintero, Alfredo Arias Montaño and Carlos A. Coello Coello, “A Review of Techniques
for Handling Expensive Functions in Evolutionary Multi-Objective Optimization”, in Yoel Tenne and ChiKeong Goh (editors), Computational Intelligence in Expensive Optimization Problems, Chapter 2, pp. 29–59,
Springer, Berlin, Germany, 2010, ISBN 978-3-642-10700-9.
1. Luc Wismans, Eric Van Berkum and Michiel Bliemer, “Acceleration of Solving the Dynamic Multi-Objective Network
Design Problem Using Response Surface Methods”, Journal of Intelligent Transporation Systems, Vol. 18, No. 1, pp.
17–29, January 2, 2014.
2. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
55
3. Alexandru-Ciprian Zavoianu, Gerd Bramerdorfer, Edwin Lughofer, Siegfried Silber, Wolfgang Amrhein and Erich Peter
Klement, “Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the
performance of electrical drives”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 8, pp. 1781–1794,
September 2013.
4. Jiandao Zhu, Yi-Jen Wang and Matthew Collette, “A multi-objective variable-fidelity optimization method for genetic
algorithms”, Engineering Optimization, Vol. 46, No. 4, pp. 521–542, April 3, 2014.
5. Rommel G. Regis, “Constrained optimization by radial basis function interpolation for high-dimensional expensive blackbox problems with infeasible initial points”, Engineering Optimization, Vol. 46, No. 2, pp. 218–243, February 1, 2014.
6. Tsung-Che Chiang, “Enhancing rule-based scheduling in wafer fabrication facilities by evolutionary algorithms: Review
and opportunity”, Computers & Industrial Engineering, Vol. 64, No. 1, pp. 524–535, January 2013.
7. Minh Nghia Le, Yew Soon Ong, Stefan Menzel, Yaochu Jin and Bernhard Sendhoff, “Evolution by Adapting Surrogates”,
Evolutionary Computation, Vol. 21, No. 2, pp. 313–340, Summer 2013.
• Carlos A. Coello Coello, Clarisse Dhaenens and Laetitia Jourdan, “Multi-Objective Combinatorial Optimization: Problematic and Context”, in Carlos A. Coello Coello, Clarisse Dhaenens and Laetitia Jourdan
(editors), Advances in Multi-Objective Nature Inspired Computing, pp. 1–21, Springer, Berlin, Studies in
Computational Intelligence Vol. 272, 2010, ISBN 978-3-642-11217-1.
1. I-Tung Yang, Yo-Ming Hsieh and Li-Ou Kung, “Parallel Computing Platform for Multiobjective Simulation Optimization
of Bridge Maintenance Planning”, Journal of Construction Engineering and Management–ASCE, Vol. 138, No. 2, pp.
215–226, February 2012.
• Efrén Mezura-Montes, Lucı́a Muñoz-Dávila and Carlos A. Coello Coello, “A Preliminary Study of Fitness Inheritance in Evolutionary Constrained Optimization”, in Natalio Krasnogor, Giuseppe Nicosia, Mario Pavone
and David Pelta (editors), Nature Inspired Cooperative Strategies for Optimization, pp. 1–14, Springer,
Berlin, Germany, 2008, ISBN 978-3-540-78986-4.
1. Ali Kaveh, Karim Laknejadi and Babak Alinejad, “Performance-based multi-objective optimization of large steel structures”, Acta Mechanica, Vol. 223, No. 2, pp. 355–369, February 2012.
• Julio Barrera and Carlos A. Coello Coello, “A Review of Particle Swarm Optimization Methods used for Multimodal Optimization”, in Chee-Peng Lim, Lakhmi C. Jain and Satchidananda Dehuri (editors), Innovations
in Swarm Intelligence, Chapter 2, pp. 9–37, Springer-Verlag, Berlin, Germany, 2009, ISBN 978-3-642-042256.
1. Joshua T. Knight, Frank T. Zahradka, David J. Singer and Matthew D. Collette, “Multiobjective Particle Swarm
Optimization of a Planing Craft with Uncertainty”, Journal of Ship Production and Design, Vol. 30, No. 4, pp.
194–200, November 2014.
2. Kalyanmoy Deb and Nikhil Padhye, “Enhancing performance of particle swarm optimization through an algorithmic
link with genetic algorithms”, Computational Optimization and Applications, Vol. 57, No. 3, pp. 761–794, April 2014.
3. Aniruddha Basak, Swagatam Das and Kay Chen Tan, “Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance-Based Selection”, IEEE Transactions on Evolutionary Computation,
Vol. 17, No. 5, pp. 666–685, October 2013.
4. Kalyanmoy Deb and Amit Saha, “Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm”, Evolutionary
Computation, Vol. 20, No. 1, pp. 27–62, Spring 2012.
• Ruhul Sarker and Carlos A. Coello Coello, “Assessment Methodologies for Multiobjective Evolutionary
Algorithms”, in Ruhul Sarker, Masoud Mohammadian and Xin Yao (Editores), Evolutionary Optimization,
Chapter 7, pp. 177–195, Kluwer Academic Publishers, Boston, USA, February 2002, ISBN 0-7923-7654-4.
1. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm Part
III Analysis of the results”, Polish Maritime Research, Vol. 18, No. 4, pp. 3–13, 2011.
2. Zbigniew Sekulski, “Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm Part
II Computational simulations”, Polish Maritime Research, Vol. 18, No. 3, pp. 3–30, 2011.
• El-Ghazali Talbi, Sanaz Mostaghim, Tatsuya Okabe, Hisao Ishibuchi, Günter Rudolph and Carlos A. Coello
Coello, “Parallel Approaches for Multi-objective Optimization”, in Jürgen Branke, Kalyanmoy Deb, Kaisa
Miettinen and Roman Slowinski (editors), Multiobjective Optimization. Interactive and Evolutionary Approaches, pp. 349–372, Springer, Lecture Notes in Computer Science Vol. 5252, Berlin, Germany, 2008.
1. Steffen Limmer and Dietmar Fey, “Porting of the transfer-matrix method for multilayer thin-film computations on
graphics processing units”, Optical Engineering, Vol. 52, No. 7, Article Number: 075103, July 2013.
56
2. Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano and Alexandre Claudio Botazzo Delbem, “General Subpopulation Framework and Taming the Conflict Inside Populations”, Evolutionary Computation, Vol. 23, No. 1, pp.
1–36, 2015.
3. Hossein Rajabalipour Cheshmehgaz, Mohammad Ishak Desa and Antoni Wibowo, “Effective local evolutionary searches
distributed on an island model solving bi-objective optimization problems”, Applied Intelligence, Vol. 38, No. 3, pp.
331–356, April 2013.
4. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionary
searches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,
March 2015.
5. Bilel Derbel, Jeremie Humeauc, Arnaud Liefooghe and Sebastien Verel, “Distributed localized bi-objective search”,
European Journal of Operational Research, Vol. 239, No. 3, pp. 731–743, December 16, 2014.
6. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “An effective model of multiple multiobjective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs”,
Applied Soft Computing, Vol. 13, No. 5, pp. 2863–2895, May 2013.
7. Matjaz Depolli, Roman Trobec and Bogdan Filipic, “Asynchronous Master-Slave Parallelization of Differential Evolution
for Multi-Objective Optimization”, Evolutionary Computation, Vol. 21, No. 2, pp. 261–291, Summer 2013.
8. Van Vinh Nguyen, Dietrich Hartmann and Markus König, “A distributed agent-based approach for simulation-based
optimization”, Advanced Engineering Informatics, Vol. 26, No. 4, pp. 814–832, October 2012.
9. Christian Grimme, Joachim Lepping and Alexander Papaspyrou, “Parallel predator-prey interaction for evolutionary
multi-objective optimization”, Natural Computing, Vol. 11, No. 3, pp. 519–533, September 2012.
10. Nima Safaei, Dragan Banjevic and Andrew K.S. Jardine, “Multi-threaded simulated annealing for a bi-objective maintenance scheduling problem”, International Journal of Production Research, Vol. 50, No. 1, pp. 63–80, 2012.
11. Gualtiero Colombo and Stuart M. Allen, “A comparison of problem decomposition techniques for the FAP”, Journal of
Heuristics, Vol. 16, No. 3, pp. 259–288, June 2010.
12. Tomas Petkus, Ernestas Filatovas and Olga Kurasova, “Investigation of Human Factors while Solving Multiple Criteria
Optimization Problems in Computer Network”, Technological and Economic Development of Economy, Vol. 15, No. 3,
pp. 464–479, 2009.
• Antonio López Jaimes, Luis Vicente Santana Quintero and Carlos A. Coello Coello, “Ranking Methods
in Many-objective Evolutionary Algorithms”, in Raymond Chiong (editor), Nature-Inspired Algorithms for
Optimisation, Chapter 15, pp. 413–434, Springer, Berlin, Germany, 2009, ISBN 978-3-642-00266-3.
1. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-Objective
Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.
2. Thomas Weise, Raymond Chiong and Ke Tang, “Evolutionary Optimization: Pitfalls and Booby Traps”, Journal of
Computer Science and Technology, Vol. 27, No. 5, pp. 907–936, September 2012.
3. Andre B. de Carvalho and Aurora Pozo, “Measuring the convergence and diversity of CDAS Multi-Objective Particle
Swarm Optimization Algorithms: A study of many-objective problems”, Neurocomputing, Vol. 75, No. 1, pp. 43–51,
January 1, 2012.
4. Slim Bechikh, Lamjed Ben Said and Khaled Ghédira, “Searching for knee regions of the Pareto front using mobile
reference points”, Soft Computing, Vol. 15, No. 9, pp. 1807–1823, 2011.
• Margarita Reyes Sierra and Carlos A. Coello Coello, “A Study of Techniques to Improve the Efficiency of a
Multi-Objective Particle Swarm Optimizer”, in Shengxiang Yang, Yew Soon Ong and Yaochu Jin (editors),
Evolutionary Computation in Dynamic and Uncertain Environments, pp. 269–296, Springer, 2007, ISBN
978-3-540-49772-1.
1. Carlos Cruz, Juan R. Gonzalez and David A. Pelta, “Optimization in dynamic environments: a survey on problems,
methods and measures”, Soft Computing, Vol. 15, No. 7, pp. 1427–1448, July 2011.
• Fabio Freschi, Carlos A. Coello Coello and Maurizio Repetto, “Multiobjective Optimization and Artificial
Immune Systems: A Review”, in Hongwei Mo (editor), Handbook of Research on Artificial Immune Systems
and Natural Computing: Applying Complex Adaptive Technologies, Chapter I, pp. 1–21, Medical Information
Science Reference, Hershey, USA, 2009, ISBN 978-1-60566-310-4.
1. Arnaud Zinflou, Caroline Gagne and Marc Gravel, “GISMOO: A new hybrid genetic/immune strategy for multipleobjective optimization”, Computers & Operations Research, Vol. 39, No. 9, pp. 1951–1968, September 2012.
2. Zhuhong Zhang and Shuqu Qian, “Artificial immune system in dynamic environments solving time-varying non-linear
constrained multi-objective problems”, Soft Computing, Vol. 15, No. 7, pp. 1333–1349, July 2011.
57
• Efrén Mezura-Montes, Margarita Reyes-Sierra and Carlos A. Coello Coello, “Multi-Objective Optimization
using Differential Evolution: A Survey of the State-of-the-Art”, in Uday K. Chakraborty (editor), Advances
in Differential Evolution, Chapter 7, pp. 173–196, Springer-Verlag, Berlin, Germany, 2008, ISBN 978-3-54068827-3.
1. J.A. Adeyemo and O.O. Olofintoye, “Evaluation of Combined Pareto Multiobjective Differential Evolution on Tuneable
Problems”, International Journal of Simulation Modelling, Vol. 13, No. 3, pp. 276–287, September 2014.
2. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
3. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Sergio Garcia-Nieto, “Physical programming for preference
driven evolutionary multi-objective optimization”, Applied Soft Computing, Vol. 24, pp. 341–362, November 2014.
4. Hu Xia, Jian Zhuang and Dehong Yu, “Combining Crowding Estimation in Objective and Decision Space With Multiple
Selection and Search Strategies for Multi-Objective Evolutionary Optimization”, IEEE Transactions on Cybernetics,
Vol. 44, No. 3, pp. 378–393, March 2014.
5. Ilhern Boussaid, Julien Lepagnot and Patrick Siarry, “A survey on optimization metaheuristics”, Information Sciences,
Vol. 237, pp. 82–117, July 10, 2013.
6. Andre Schardong, Slobodan P. Simonovic and A. Vasan, “Multiobjective Evolutionary Approach to Optimal Reservoir
Operation”, Journal of Computing in Civil Engineering, Vol. 27, No. 2, pp. 139–147, March 2013.
7. Jun-fang Li, Bu-han Zhang, Yi-fang Liu, Kui Wang and Xiao-shan Wu, “Spatial evolution character of multi-objective
evolutionary algorithm based on self-organized criticality theory”, Physica A–Statistical Mechanics and its Applications,
Vol. 391, No. 22, pp. 5490–5499, November 15, 2012.
8. Gilberto Reynoso-Meza, Sergio Garcia-Nieto, Javier Sanchis and F. Xavier Blasco, “Controller Tuning by Means of MultiObjective Optimization Algorithms: A Global Tuning Framework”, IEEE Transactions on Control Systems Technology,
Vol. 21, No. 2, pp. 445–458, March 2013.
9. Xiang Li and Gang Du, “BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems”,
Computers & Operations Research, Vol. 40, No. 1, pp. 282–302, January 2013.
10. P.M. Mateo and I. Alberto, “A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms”,
Journal of Heuristics, Vol. 18, No. 1, pp. 53–89, February 2012.
11. Feng Qian, Bing Xu, Rongbin Qi and Huaglory Tianfield, “Self-adaptive differential evolution algorithm with αconstrained-domination principle for constrained multi-objective optimization”, Soft Computing, Vol. 16, No. 8, pp.
1353–1372, August 2012.
12. Chunhua Peng, Huijuan Sun, Jianfeng Guo and Gang Liu, “Multi-objective optimal strategy for generating and bidding
in the power market”, Energy Conversion and Management, Vol. 57, pp. 13–22, May 2012.
13. I. Alberto and P.M. Mateo, “A crossover operator that uses Pareto optimality in its definition”, TOP, Vol. 19, No. 1,
pp. 67–92, July 2011.
14. Ferrante Neri and Ville Tirronen, “Recent advances in differential evolution: a survey and experimental analysis”,
Artificial Intelligence Review, Vol. 33, Nos. 1-2, pp. 61–106, February 2010.
• Luis V. Santana-Quintero, Noel Ramı́rez-Santiago and Carlos A. Coello Coello, “Towards a More Efficient
Multi-Objective Particle Swarm Optimizer”, in Lam Thu Bui and Sameer Alam (editors), Multi-Objective
Optimization in Computational Intelligence: Theory and Practice, Chapter IV, pp. 76–105, Information
Science Reference, Hershey, USA, 2008, ISBN 978-1-59904-498-9.
1. Miltiadis Kotinis, “Implementing co-evolution and parallelization in a multi-objective particle swarm optimizer”, Engineering Optimization, Vol. 43, No. 6, pp. 635–656, June 2011.
• Antonio López Jaimes and Carlos A. Coello Coello, “Multi-Objective Evolutionary Algorithms: A Review
of the State-of-the-Art and some of their Applications in Chemical Engineering”, in Rangaiah Gade Pandu
(editor), Multi-Objective Optimization Techniques and Applications in Chemical Engineering, Chapter 3, pp.
61–90, World Scientific, Singapore, 2009, ISBN 978-981-283-651-9.
1. Karthik Sindhya, Kaisa Miettinen and Kalyanmoy Deb, “A Hybrid Framework for Evolutionary Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 4, pp. 495–511, August 2013.
2. J. Novo, M.G. Penedo and J. Santos, “Evolutionary multiobjective optimization of Topological Active Nets”, Pattern
Recognition Letters, Vol. 31, No. 13, pp. 1781–1794, October 1, 2010.
• Carlos A. Coello Coello, “Evolutionary Multi-Objective Optimization in Finance”, in Jean-Philippe Rennard
(editor), Handbook of Research on Nature Inspired Computing for Economy and Management, pp. 74–88,
Vol. I, Idea Group Reference, Hershey, UK, 2006, ISBN 1-59140-984-5.
58
1. Khin Lwin, Rong Qu and Graham Kendall, “A learning-guided multi-objective evolutionary algorithm for constrained
portfolio optimization”, Applied Soft Computing, Vol. 24, pp. 757–772, November 2014.
2. K. Metaxiotis and K. Liagkouras, “Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive
literature review”, Expert Systems with Applications, Vol. 39, No. 14, pp. 11685–11698, October 15, 2012.
3. Chao Song, Ming Liu, Jiannong Cao, Yuan Zheng, Haigang Gong and Guihai Chen, “Maximizing network lifetime
based on transmission range adjustment in wireless sensor networks”, Computer Communications, Vol. 32, No. 11, pp.
1316–1325, July 3, 2009.
4. A. Slowik and J. Slowik, “Multi-objective optimization of surface grinding process with the use of evolutionary algorithm
with remembered Pareto set”, The International Journal of Advanced Manufacturing Technology, Vol. 37, Nos. 7–8, pp.
657–669, June 2008.
• Carlos A. Coello Coello, “20 Years of Evolutionary Multi-Objective Optimization: What Has Been Done
and What Remains to be Done”, in Gary Y. Yen and David B. Fogel (editors), Computational Intelligence:
Principles and Practice, Chapter 4, pp. 73–88, IEEE Computational Intelligence Society, 2006, ISBN 09787135-0-8.
1. Carlos Garcia, Guillermo Botella, Fermin Ayuso, Manuel Prieto and Francisco Tirado, “Multi-GPU based on multicriteria
optimization for motion estimation system”, EURASIP Journal on Advances in Signal Processing, Article Number: 23,
2013.
2. Abhishek Singh, Barbara S. Minsker and Albert J. Valocchi, “An interactive multi-objective optimization framework for
groundwater inverse modeling”, Advances in Water Resources, Vol. 31, No. 10, pp. 1269–1283, October 2008.
3. Pletari Pulkkinen, Jarmo Hytonen and Hannu Kolvisto, “Developing a bioaerosol detector using hybrid genetic fuzzy
systems”, Engineering Applications of Artificial Intelligence, Vol. 21, No. 8, pp. 1330–1346, December 2008.
4. Pletari Pulkkinen and Hannu Koivisto, “Fuzzy classifier identification using decision tree and multiobjective evolutionary
algorithms”, International Journal of Approximate Reasoning, Vol. 48, No. 2, pp. 526–543, June 2008.
5. Parames Chutima and Palida Chimklai, “Multi-objective two-sided mixed-model assembly line balancing using particle
swarm optimisation with negative knowledge”, Computers & Industrial Engineering, Vol. 62, No. 1, pp. 39–55, February
2012.
6. Lily Rachmawati and Dipti Srinivasan, “Incorporating the Notion of Relative Importance of Objectives in Evolutionary
Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 4, pp. 530–546, August
2010.
7. A.A. Aguilar-Lasserre, L. Pibouleau, C. Azzaro-Pantel and S. Domenech, “Enhanced genetic algorithm-based fuzzy
multiobjective strategy to multiproduct batch plant design”, Applied Soft Computing, Vol. 9, No. 4, pp. 1321–1330,
September 2009.
8. Jingqiao Zhang and Arthur C. Sanderson, “JADE: Adaptive Differential Evolution with Optional External Archive”,
IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp. 945–958, October 2009.
9. Chuan Shi, Zhenyu Yan, Kevin Lu, Zhingzhi Shi and Bai Wang, “A dominance tree and its application in evolutionary
multi-objective optimization”, Information Sciences, Vol. 179, No. 20, pp. 3540–3560, September 29, 2009.
10. Xiufen Zou, Yu Chen, Minzhong Liu and Lishan Kang, “A New Evolutionary Algorithm for Solving Many-Objective
Optimization Problems”, IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol. 38, No. 5,
pp. 1402–1412, October 2008
• Carlos A. Coello Coello and Carlos E. Mariano Romero, “Evolutionary Algorithms and Multiple Objective
Optimization”, in Xavier Gandibleux & Matthias Ehrgott (editors), Multiple Criteria Optimization. State of
the Art Annotated Bibliographic Survey, Chapter 6, pp. 277-331, Kluwer’s International Series in Operations
Research and Management Science, Volume 52, Kluwer Academic Publishers, ISBN 1-4020-7128-0, June
2002.
1. Hans-Friedrich Köhn, “A review of multiobjective programming and its application in quantitative psychology”, Journal
of Mathematical Psychology, Vol. 55, No. 5, pp. 386–396, October 2011.
2. Samya Elaoud, Jacques Teghem and Bassem Bouaziz, “Genetic algorithms to solve the cover printing problem”, Computers & Operations Research, Vol. 34, No. 11, pp. 3346–3361, November 2007.
3. Samya Elaoud, Taicir Loukil and Jacques Teghem, “The Pareto fitness genetic algorithm: Test function study”, European
Journal of Operational Research, Vol. 177, No. 3, pp. 1703–1719, March 16, 2007.
• Ricardo Landa Becerra and Carlos A. Coello Coello, “A Cultural Algorithm for Solving the Job-Shop Scheduling Problem”, en Yaochu Jin (editor) Knowledge Incorporation in Evolutionary Computation, Springer, pp.
37–55, Studies in Fuzziness and Soft Computing, Vol. 167, ISBN 3-540-22902-7, 2005.
59
1. Jesus Garcia, Antonio Berlanga and Jose M. Molina, “Evolutionary algorithms in multiply-specified engineering. The
MOEAs and WCES strategies”, Advanced Engineering Informatics, Vol. 21, No. 1, pp. 3–21, January 2007.
• Carlos A. Coello Coello, “Evolutionary Multi-Objective Optimization: A Critical Review”, in Ruhul Sarker,
Masoud Mohammadian and Xin Yao (Editores), Evolutionary Optimization, Chapter 5, pp. 117–146, Kluwer
Academic Publishers, Boston, ISBN 0-7923-7654-4, February 2002.
1. Marcelo H. Kobayashi, “On a biologically inspired topology optimization method”, Communications in Nonlinear Science
and Numerical Simulation, Vol. 15, No. 3, pp. 787–802, March 2010.
2. Hossein Ghiasi, Damiano Pasini and Larry Lessard, “A non-dominated sorting hybrid algorithm for multi-objective
optimization of engineering problems”, Engineering Optimization, Vol. 43, No. 1, pp. 39–59, January 2011.
3. Jae-Yon Jung and James A. Reggia, “A Descriptive Encoding Language for Evolving Modular Neural Networks”, in
Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic and
Evolutionary Computation Conference. Part II, Springer-Verlag, Lecture Notes in Computer Science Vol. 3103, pp.
519–530, Seattle, Washington, USA, June 2004.
• Carlos A. Coello Coello, Gregorio Toscano Pulido and Efrén Mezura Montes, “Current and Future Research
Trends in Evolutionary Multiobjective Optimization”, in Manuel Graña, Richard Duro, Alicia d’Anjou, and
Paul P. Wang (editors), Information Processing with Evolutionary Algorithms: From Industrial Applications
to Academic Speculations, pp. 213–231, Springer-Verlag, ISBN 1-8523-3866-0, 2005.
1. J.M. Herrero, G. Reynoso-Meza, M. Martinez, X. Blasco and J. Sanchis, “A Smart-Distributed Pareto Front Using the
ev-MO GA Evolutionary Algorithm”, International Journal on Artificial Intelligence Tools, Vol. 23, No. 2, Article
Number: 1450002, April 2014.
2. Sajad Tabatabaei, “A new gravitational search optimization algorithm to solve single and multiobjective optimization
problems”, Journal of Intelligent & Fuzzy Systems, Vol. 26, No. 2, pp. 993–1006, 2014.
3. El-Sayed M. El-Alfy, Syed N. Mujahid and Shokri Z. Selim, “A Pareto-based hybrid multiobjective evolutionary approach for constrained multipath traffic engineering optimization in MPLS/GMPLS networks”, Journal of Network and
Computer Applications, Vol. 36, No. 4, pp. 1196–1207, July 2013.
4. Eduardo Fernandez Gonzalez, Edy Lopez Cervantes, Jorge Navarro Castillo and Ines Vega Lopez, “Application of MultiObjective Metaheuristics to Public Portfolio Selection Through Multidimensional Modelling of Social Return”, Gestion
y Politica Publica, Vol. 20, No. 2, pp. 381–432, 2011.
5. Xianshun Chen, Yew-Soon Ong, Meng-Hiot Lim and Kay Chen Tan, “A Multi-Facet Survey on Memetic Computation”,
IEEE Transactions on Evolutionary Computation, Vol. 15, No. 5, pp. 591–607, October 2011.
6. Deo Vidyarthi and Lutfi Khanbary, “Multi-objective optimization for channel allocation in mobile computing using
NSGA-II”, International Journal of Network Management, Vol. 21, No. 3, pp. 247–266, May 2011.
7. J.R. Jimenez-Octavio, O. Lopez-Garcia, E. Pilot and A. Carnicero, “Coupled electromechanical optimization of power
transmission”, CMES-Computer Modeling in Engineering & Sciences, Vol. 25, No. 2, pp. 81–97, February 2008.
8. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Non-linear robust identification of a greenhouse model
using multi-objective evolutionary algorithms”, Biosystems Engineering, Vol. 98, No. 3, pp. 335–346, 2007.
9. Daniel E. Salazar and Claudio M. Rocco, “Solving advanced multi-objective robust designs by means of multiple objective
evolutionary algorithms (MOEA): A reliability application”, Reliability Engineering & System Safety, Vol. 92, No. 6,
pp. 697–706, June 2007.
10. Diego Sal and Manuel Graña, “Hyperspectral image watermarking with an evolutionary algorithm”, Knowledge-Based
Intelligent Information and Engineering Systems, Pt 1, Proceedings, pp. 833–839, Springer, Lecture Notes in Artificial
Intelligence Vol. 3681, 2005.
11. Yujia Wang and Yupu Yang, “Particle swarm optimization with preference order ranking for multi-objective optimization”, Information Sciences, Vol. 179, No. 12, pp. 1944–1959, May 30, 2009.
12. J.M. Herrero, S. Garcia-Nieto, X. Blasco, V. Romero-Garcia, J.V. Sanchez-Perez, L.M. Garcia-Raffi, “Optimization of
sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm”, Structural and Multidisciplinary Optimization, Vol. 39, No. 2, pp. 203–215, August 2009.
13. R. Alcala, M.J. Gacto, F. Herrera and J. Alcala-Fdez, “A multi-objective genetic algorithm for tuning and rule selection
to obtain accurate and compact linguistic fuzzy rule-based systems”, International Journal of Uncertainty Fuzziness and
Knowledge-Based Systems, Vol. 15, No. 5, pp. 539–557, October 2007.
• Carlos A. Coello Coello, “Recent Trends in Evolutionary Multiobjective Optimization”, in Ajith Abraham,
Lakhmi Jain and Robert Goldberg (editors), Evolutionary Multiobjective Optimization: Theoretical Advances
And Applications, pp. 7–32, Springer-Verlag, London, 2005, ISBN 1-85233-787-7.
60
1. Gideon Avigad, Erella Eisenstadt, Alex Goldvard and Shaul Salomon, “Transient responses’ optimization by means of
set-based multi-objective evolution”, Engineering Optimization, Vol. 44, No. 4, pp. 407–426, 2012.
2. Christopher Priester, Sebastian Schmitt and Tiago P. Peixoto, “Limits and Trade-Offs of Topological Network Robustness”, Plos One, Vol. 9, No. 9, Article Number: e108215, September 24, 2014.
3. Alvaro Garcia-Piquer, Albert Fornells, Jaume Bacardit, Albert Orriols-Puig and Elisabet Golobardes, “Large-Scale
Experimental Evaluation of Cluster Representations for Multiobjective Evolutionary Clustering”, IEEE Transactions on
Evolutionary Computation, Vol. 18, No. 1, pp. 36–53, February 2014.
4. Yun Yang, Jianfeng Wu, Xiaomin Sun, Jichun Wu and Chunmiao Zheng, “A niched Pareto tabu search for multi-objective
optimal design of groundwater remediation systems”, Journal of Hydrology, Vol. 490, pp. 56–73, May 20, 2013.
5. El-Sayed M. El-Alfy, Syed N. Mujahid and Shokri Z. Selim, “A Pareto-based hybrid multiobjective evolutionary approach for constrained multipath traffic engineering optimization in MPLS/GMPLS networks”, Journal of Network and
Computer Applications, Vol. 36, No. 4, pp. 1196–1207, July 2013.
6. Maoguo Gong, Xiaowei Chen, Lijia Ma, Qingfu Zhang and Licheng Jiao, “Identification of multi-resolution network
structures with multi-objective immune algorithm”, Applied Soft Computing, Vol. 13, No. 4, pp. 1705–1717, April 2013.
7. Renata Furtuna, Silvia Curteanu and Carmen Racles, “NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants”, Central European Journal of Chemistry, Vol. 9, No. 6, pp.
1080–1095, December 2011.
8. Wenping Zou, Yunlong Zhu, Hanning Chen and Beiwei Zhang, “Solving Multiobjective Optimization Problems Using
Artificial Bee Colony Algorithm”, Discrete Dynamics in Nature and Society, Article Number: 569784, 2011.
9. Nhu Binh Ho and Joc Cing Tay, “Solving multiple-objective flexible job shop problems by evolution and local search”,
IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 38, No. 5, pp. 674–685,
September 2008.
10. Joc Cing Tay and Nhu Binh Ho, “Evolving dispatching rules using genetic programming for solving multi-objective
flexible job-shop problems”, Computers & Industrial Engineering, Vol. 54, No. 3, pp. 453–473, April 2008.
11. Hui Li and Qingfu Zhang, “A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós,
L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference,
pp. 583–592, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
12. I. Alberto and P.M. Mateo, “A crossover operator that uses Pareto optimality in its definition”, TOP, Vol. 19, No. 1,
pp. 67–92, July 2011.
13. Renata Furtuna, Silvia Curteanu and Florin Leon, “An elitist non-dominated sorting genetic algorithm enhanced with a
neural network applied to the multi-objective optimization of a polysiloxane synthesis process”, Engineering Applications
of Artificial Intelligence, Vol. 24, No. 5, pp. 772–785, August 2011.
14. Hiroshi Wada, Junichi Suzuki, Yuji Yamano and Katsuya Oba, “Evolutionary deployment optimization for serviceoriented clouds”, Software–Practice & Experience, Vol. 41, No. 5, pp. 469–493, April 2011.
15. Juan C. Vidal, Manuel Mucientes, Alberto Bugarı́n and Manuel Lama, “Machine scheduling in custom furniture industry
through neuro-evolutionary hybridization”, Applied Soft Computing, Vol. 11, No. 2, pp. 1600–1613, March 2011.
16. Yixiong Feng, Bing Zheng and Zhongkai Li, “Exploratory study of sorting particle swarm optimizer for multiobjective
design optimization”, Mathematical and Computer Modelling, Vol. 52, Nos. 11-12, pp. 1966–1975, December 2010.
17. Miguel Rocha, Pedro Sousa, Paulo Cortez and Miguel Rio, “Quality of Service constrained routing optimization using
Evolutionary Computation”, Applied Soft Computing, Vol. 11, No. 1, pp. 356–364, January 2011.
18. Ricardo Perera and Sheng-En Fang, “Influence of Objective Functions in Structural Damage Identification using Refined
and Simple Models”, International Journal of Structural Stability and Dynamics, Vol. 9, No. 4, pp. 607–625, December
2009.
19. Andreas Efstratiadis and Demetris Koutsoyiannis, “One decade of multi-objective calibration approaches in hydrological
modelling: a review”, Hydrological Sciences Journal–Journal Des Sciences Hydrologiques, Vol. 55, No. 1, pp. 58–78,
2010.
20. Elisabete Figueiredo, Sandra Valente, Celeste Coelho and Luisa Pinho, “Coping with risk: analysis on the importance
of integrating social perceptions on flood risk into management mechanisms - the case of the municipality of Agueda,
Portugal”, Journal of Risk Research, Vol. 12, No. 5, pp. 581–602, 2009.
21. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “Performance assessment of multicriteria damage identification
genetic algorithms”, Computers & Structures, Vol. 87, Nos. 1-2, pp. 120–127, January 2009.
22. Ricardo Perera, Sheng-En Fang and C. Huerta, “Structural crack detection without updated baseline model by single
and multiobjective optimization”, Mechanical Systems and Signal Processing, Vol. 23, No. 3, pp. 752–768, April 2009.
61
23. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjective immune algorithm with nondominated
neighbor-based selection”, Evolutionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.
24. Ricardo Perera and Antonio Ruiz, “A multistage FE updating procedure for damage identification in large-scale structures based on multiobjective evolutionary optimization”, Mechanical Systems and Signal Processing, Vol. 22, No. 4,
pp. 970–991, May 2008.
25. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “An evolutionary multiobjective framework for structural damage
localization and quantification”, Engineering Structures, Vol. 29, No. 10, pp. 2540–2550, October 2007.
26. Siew-Chin Neoh, Norhashimah Morad, Chee-Peng Lim and Zalina Abdul Aziz, “A Layered-Encoding Cascade Optimization Approach to Product-Mix Planning in High-Mix-Low-Volume Manufacturing”, IEEE Transactions on Systems,
Man, and Cybernetics Part A—Systems and Humans, Vol. 40, No. 1, pp. 133–146, January 2010.
27. Jing Tian and Lincheng Shen, “A multi-objective evolutionary algorithm for multi-UAV cooperative reconnaissance
problem”, Neural Information Processing, Part 3, Proceedings, pp. 900–909, Springer, Lecture Notes in Computer
Science Vol. 4234, 2006.
28. Pedro Sousa, Miguel Rocha, Miguel Rio and Paulo Cortez, “Efficient OSPF weight allocation for intra-domain QoS
optimization”, Autonomic Principles of IP Operations and Management, Proceedings, pp. 37–48, Springer, Lecture
Notes in Computer Science Vol. 4268, 2006.
29. David Coulot, Arnaud Pollet, Xavier Collilieux and Philippe Berio, “Global optimization of core station networks for
space geodesy: application to the referencing of the SLR EOP with respect to ITRF”, Journal of Geodesy, Vol. 84, No.
1, pp. 31–50, January 2010.
30. Gideon Avigad and Amiram Moshaiov, “Interactive Evolutionary Multiobjective Search and Optimization of Set-Based
Concepts”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 39, No. 4, pp. 1013–1027,
August 2009.
31. R. Alcala, M.J. Gacto, F. Herrera and J. Alcala-Fdez, “A multi-objective genetic algorithm for tuning and rule selection
to obtain accurate and compact linguistic fuzzy rule-based systems”, International Journal of Uncertainty Fuzziness and
Knowledge-Based Systems, Vol. 15, No. 5, pp. 539–557, October 2007.
32. Dimo Brockhoff and Eckart Zitzler, “Objective Reduction in Evolutionary Multiobjective Optimization: Theory and
Applications”, Evolutionary Computation, Vol. 17, No. 2, pp. 135–166, Summer 2009.
33. Dongdong Yang, Licheng Jiao and Maoguo Gong, “Adaptive Multi-Objective Optimization Based on Nondominated
Solutions”, Computational Intelligence, Vol. 25, No. 2, pp. 84–108, May 2009.
• Carlos A. Coello Coello, “Evolutionary Multiobjective Optimization: Current and Future Challenges”, in
Jose Benitez, Oscar Cordon, Frank Hoffmann and Rajkumar Roy (editors), Advances in Soft Computing—
Engineering, Design and Manufacturing, pp. 243–256, Springer-Verlag, ISBN 1-85233-755-9, September
2003.
1. Paulo Cesar Ribas, Lia Yamamoto, Helton Luis Polli, L.V.R. Arruda and Flavio Neves, Jr., “A micro-genetic algorithm
for multi-objective scheduling of a real world pipeline network”, Engineering Applications of Artificial Intelligence, Vol.
26, No. 1, pp. 302–313, January 2013.
2. Peter von Buelow, “Suitability of genetic based exploration in the creative design process”, Digital Creativity, Vol. 19,
No. 1, pp. 51–61, 2008.
3. Olcay Ersel Canyurt and Prabhat Hajela, “Cellular genetic algorithm technique for the multicriterion design optimization”, Structural and Multidisciplinary Optimization, Vol. 40, Nos. 1–6, pp. 201–214, January 2010.
4. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjective immune algorithm with nondominated
neighbor-based selection”, Evolutionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.
5. Antonio Pinto, Daniele Peri and Emilio F. Campana, “Multiobjective optimization of a containership using deterministic
particle swarm optimization”, Journal of Ship Research, Vol. 51, No. 3, pp. 217–228, September 2007.
6. Wangshu Yao, Chen Shifu and Chen Zhaoqian, “SDMOGA: A New Multi-objective Genetic Algorithm Based on Objective Space Divided”, in Irwin King, Jun Wang, Laiwan Chan and DeLiang L. Wang (editors), Neural Information
Processing, 13th International Conference, ICONIP 2006, Part III, pp. 754–762, Springer-Verlag. Lecture Notes in
Computer Science Vol. 4234, Hong Kong, China, October 2006.
7. L. Grandinetti, F. Guerriero, G. Lepera and M. Mancini, “A niched genetic algorithm to solve a pollutant emission
reduction problem in the manufacturing industry: A case study”, Computers & Operations Research, Vol. 34, No. 7,
pp. 2191–2214, July 2007.
8. MaoGuo Gong, LiCheng Jiao, WenPing Ma and HaiFeng Du, “Multiobjective optimization using an immunodominance
and clonal selection inspired algorithm”, Science in China Series F–Information Sciences, Vol. 51, No. 8, pp. 1064–1082,
August 2008.
62
• Dragan Cvetkovic and Carlos A. Coello Coello, “Human Preferences and Their Applications in Evolutionary
Multi-Objective Optimization”, in Yaochu Jin (editor) Knowledge Incorporation in Evolutionary Computation, Springer, pp. 479–502, Studies in Fuzziness and Soft Computing Vol. 167, ISBN 3-540-22902-7,
2005.
1. D. Greiner, J.M. Emperador, B. Galvan, M. Mendez and G. Winter, “Engineering Knowledge-Based Variance-Reduction
Simulation and G-Dominance for Structural Frame Robust Optimization”, Advances in Mechanical Engineering, Article
Number: 680359, 2013.
2. David Coulot, Arnaud Pollet, Xavier Collilieux and Philippe Berio, “Global optimization of core station networks for
space geodesy: application to the referencing of the SLR EOP with respect to ITRF”, Journal of Geodesy, Vol. 84, No.
1, pp. 31–50, January 2010.
• Efrén Mezura-Montes and Carlos A. Coello Coello, “Constrained Optimization via Multiobjective Evolutionary Algorithms”, in Joshua Knowles, David Corne and Kalyanmoy Deb (Editors), Multi-Objective Problem
Solving from Nature: From Concepts to Applications, pp. 53–75, Springer, 2008, ISBN 978-3-540-72963-1.
1. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.
53, No. 4, pp. 1168–1183, July 2014.
2. A. Villagra, D. Pandolfi and G. Leguizamon, “ Handling constraints with an evolutionary tool for scheduling oil wells
maintenance visits”, Engineering Optimization, Vol. 45, No. 8, pp. 963–981, July-September, 2013.
3. Blaze Gjorgiev and Marko Cepin, “A multi-objective optimization based solution for the combined economic-environmental
power dispatch problem”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 1, pp. 417–429, January 2013.
4. Romanas Puisa and Heinrich Streckwall, “Prudent constraint-handling technique for multiobjective propeller optimisation”, Optimization and Engineering, Vol. 12, No. 4, pp. 657–680, December 2011.
5. Andreas Konstantinidis and Kun Yang, “Multi-objective K-connected Deployment and Power Assignment in WSNs
using a problem-specific constrained evolutionary algorithm based on decomposition”, Computer Communications, Vol.
34, No. 1, pp. 83–98, January 15, 2011.
6. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique for
constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,
pp. 1501–1534, March 2009.
7. Dimo Brockhoff, Tobias Friedrich, Nils Hebbinghaus, Christian Klein, Frank Neumann and Eckart Zitzler, “On the
Effects of Adding Objectives to Plateau Functions”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3,
pp. 591–603, July 2009.
Journals Internacionales
• Jorge E. Rodrı́guez, Andrés L. Medaglia and Carlos A. Coello Coello, “Design of a motorcycle frame using
neuroacceleration strategies in MOEAs”, Journal of Heuristics, Vol. 15, No. 2, pp. 177–196, April 2009.
1. Joseph Y.J. Chow and Amelia C. Regan, “A surrogate-based multiobjective metaheuristic and network degradation
simulation model for robust toll pricing”, Optimization and Engineering, Vol. 15, No. 1, pp. 137–165, March 2014.
• Antonio López Jaimes and Carlos A. Coello Coello, “Including preferences into a multiobjective evolutionary
algorithm to deal with many-objective engineering optimization problems”, Information Sciences, Vol. 277,
pp. 1–20, September 1, 2014.
1. Ernestas Filatovas, Olga Kurasova and Karthik Sendhya, “Synchronous R-NSGA-II: An Extended Preference-Based
Evolutionary Algorithm for Multi-Objective Optimization”, Informatica, Vol. 26, No. 1, pp. 33–50, 2015.
• Carlos A. Coello Coello, “Research Directions in Evolutionary Multi-Objective Optimization. Current and
Future Research Topics”, Transactions of the Japanese Society for Evolutionary Computation, Vol. 3, No.
3, pp. 110–121, December 2012.
1. Shahin Rostami, Dean O’Reilly, Alex Shenfield and Nicholas Bowring, “A novel preference articulation operator for the
Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection”, Information Sciences, Vol.
295, pp. 494–520, February 20, 2015.
• Victoria S. Aragón, Susana C. Esquivel and Carlos A. Coello Coello, “A T-Cell Algorithm for Solving Dynamic
Optimization Problems”, Information Sciences, Vol. 181, No. 17, pp. 3614–3637, 1 September 2011.
63
1. Min Han, Chuang Liu and Jun Xing, “An evolutionary membrane algorithm for global numerical optimization problems”,
Information Sciences, Vol. 276, pp. 219–241, August 20, 2014.
2. Motjabe Ghasemi, Mohammad Mehdi Ghanbarian, Sahand Ghavidel, Shima Rahmani and Esmaeil Mahboubi Moghaddam, “Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power
dispatch problem: A comparative study”, Information Sciences, Vol. 278, pp. 231–249, September 10, 2014.
3. Changhe Li, Thanh Nguyen Trung, Ming Yang, Shengxiang Yang and Sanyou Zeng, “Multi-population methods in
unconstrained continuous dynamic environments: The challenges”, Information Sciences, Vol. 296, pp. 95–118, March
1, 2015.
• D.A. Bloch and C.A. Coello Coello, “Smiling at Evolution”, Applied Soft Computing, Vol. 11, No. 8, pp.
5724–5734, December 2011.
1. Adam P. Piotrowski, “Differential Evolution algorithms applied to Neural Network training suffer from stagnation”,
Applied Soft Computing, Vol. 21, pp. 382–406, August 2014.
• Antonin Ponsich and Carlos A. Coello Coello, “A Hybrid Differential Evolution-Tabu Search Algorithm for
the Solution of Job-Shop Scheduling Problems”, Applied Soft Computing, Vol. 13, No. 1, pp. 462–474,
January 2013.
1. Adam P. Piotrowski, “Differential Evolution algorithms applied to Neural Network training suffer from stagnation”,
Applied Soft Computing, Vol. 21, pp. 382–406, August 2014.
2. Hao Gao, Sam Kwong, Baojie Fan and Ran Wang, “A Hybrid Particle-Swarm Tabu Search Algorithm for Solving Job
Shop Scheduling Problems”, IEEE Transactions on Industrial Informatics, Vol. 10, No. 4, pp. 2044–2054, November
2014.
• Eduardo Fernández, Edy López, Gustavo Mazcorro, Rafael Olmedo and Carlos A. Coello Coello, “Application
of the non-outranked sorting genetic algorithm to public project portfolio selection”, Information Sciences,
Vol. 228, pp. 131–149, 10 April 2013.
1. Serkan Altuntas and Turkay Dereli, “A novel approach based on DEMATEL method and patent citation analysis for
prioritizing a portfolio of investment projects”, Expert Systems with Applications, Vol. 42, No. 3, pp. 1003–1012,
February 15, 2015.
• Antonin Ponsich, Antonio López Jaimes and Carlos A. Coello Coello, “A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics
Applications”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 3, pp. 321–344, June 2013.
1. Khin Lwin, Rong Qu and Graham Kendall, “A learning-guided multi-objective evolutionary algorithm for constrained
portfolio optimization”, Applied Soft Computing, Vol. 24, pp. 757–772, November 2014.
• Miguel A. Medina, Swagatam Das, Carlos A. Coello Coello, and Juan M. Ramı́rez, “Decomposition-based
Modern Metaheuristic Algorithms for Multi-Objective Optimal Power Flow–A Comparative Study”, Engineering Applications of Artificial Intelligence, Vol. 32, pp. 10–20, June 2014.
1. Krzysztof Michalak, “The effects of asymmetric neighborhood assignment in the MOEA/D algorithm”, Applied Soft
Computing, Vol. 25, pp. 97–106, December 2014.
• Carlos Segura, Carlos A. Coello Coello, Gara Miranda and Coromoto León, “Using Multi-objective Evolutionary Algorithms for Single-Objective Optimization”, 4OR–A Quarterly Journal of Operations Research,
Vol. 11, No. 3, pp. 201–228, September 2013.
1. Mario Garza-Fabre, Eduardo Rodriguez-Tello and Gregorio Toscano-Pulido, “Constraint-handling through multi-objective
optimization: The hydrophobic-polar model for protein structure prediction”, Computers & Operations Research, Vol.
53, pp. 128–153, January 2015.
2. Mohammad Amin Safarzadeh and Seyyed Mahdia Motahhari, “Co-optimization of carbon dioxide storage and enhanced
oil recovery in oil reservoirs using a multi-objective genetic algorithm (NSGA-II)”, Petroleum Science, Vol. 11, No. 3,
pp. 460–468, September 2014.
• Mario Villalobos-Arias, Gregorio Toscano Pulido and Carlos A. Coello Coello, “A New Mechanism to Maintain Diversity in Multi-Objective Metaheuristics”, Optimization, Vol. 61, No. 7, pp. 823–854, July 2012.
1. Nitin Narang, J.S. Dhillon and D.P. Kothari, “Weight pattern evaluation for multiobjective hydrothermal generation
scheduling using hybrid search technique”, International Journal of Electrical Power & Energy Systems, Vol. 62, pp.
665–678, November 2014.
64
• J.J. Durillo, A.J. Nebro, F. Luna, C.A. Coello Coello and E. Alba, “Convergence Speed in Multi-Objective
Metaheuristics: Efficiency Criteria and Empirical Study”, International Journal for Numerical Methods in
Engineering, Vol. 84, No. 11, pp. 1344–1375, 10 December, 2010.
1. Bin Zi, Huafeng Ding, Jianbin Cao, Zhencai Zhu and Andres Kecskemethy, “Integrated Mechanism Design and Control
for Completely Restrained Hybrid-Driven Based Cable Parallel Manipulators”, Journal of Intelligent & Robotic Systems,
Vol. 74, Nos. 3-4, pp. 643–661, June 2014.
2. Ngaarn J. Cheung and Hong-Bin Shen, “Hierarchical particle swarm optimizer for minimizing the non-convex potential
energy of molecular structure”, Journal of Molecular Graphics & Modelling, Vol. 54, pp. 114–122, November 2014.
3. Di Lu, Bende Wang, Yaodong Wang, Huicheng Zhou, Qiuhua Liang, Yong Peng and Tony Roskilly, “Optimal operation
of cascade hydropower stations using hydrogen as storage medium”, Applied Energy, Vol. 137, pp. 56–63, January 1,
2015.
4. Mohammad Mortazavi-Naeini, George Kuczera and Lijie Cui, “Efficient multi-objective optimization methods for computationally intensive urban water resources models”, Journal of Hydroinformatics, Vol. 17, No. 1, pp. 36–55, 2015.
5. F. Bourennani, S. Rahnamayan and G.F. Naterer, “Optimal Design Methods for Hybrid Renewable Energy Systems”,
International Journal of Green Energy, Vol. 12, No. 2, pp. 148–159, February 1, 2015.
6. Aris Lanaridis and Andreas Stafylopatis, “An artificial immune network for multiobjective optimization problems”,
Engineering Optimization, Vol. 46, No. 8, pp. 1008–1031, August 3, 2014.
• Oliver Schütze, Massimiliano Vasile and Carlos A. Coello Coello, “Computing the Set of epsilon-efficient
Solutions in Multi-Objective Space Mission Design”, Journal of Aerospace Computing, Information, and
Communication, Vol. 8, No. 3, pp. 53–70, March 2011.
1. Hu Xia, Jian Zhuang and Dehong Yu, “Multi-objective unsupervised feature selection algorithm utilizing redundancy
measure and negative epsilon-dominance for fault diagnosis”, Neurocomputing, Vol. 146, pp. 113–124, December 25,
2014.
2. Hu Xia, Jian Zhuang and Dehong Yu, “Combining Crowding Estimation in Objective and Decision Space With Multiple
Selection and Search Strategies for Multi-Objective Evolutionary Optimization”, IEEE Transactions on Cybernetics,
Vol. 44, No. 3, pp. 378–393, March 2014.
• Gideon Avigad and Carlos A. Coello Coello, “Highly Reliable Optimal Solutions to Multi Objective Problems
and their Evolution by Means of Worst-case Analysis”, Engineering Optimization, Vol. 42, No. 1, pp. 1095–
1117, December 2010.
1. Michael de Paly, Claudius M. Burger and Peter Bayer, “ Optimization under worst case constraints-a new global
multimodel search procedure”, Structural and Multidisciplinary Optimization, Vol. 48, No. 6, pp. 1153–1172, December
2013.
• Leticia Cecilia Cagnina, Susana Cecilia Esquivel and Carlos A. Coello Coello, “Solving Constrained Optimization Problems with a Hybrid Particle Swarm Optimization Algorithm”, Engineering Optimization, Vol.
43, No. 8, pp. 843–866, August 2011.
1. Kazuhiro Izui, Takayuki Yamada, Shinji Nishiwaki and Kazuto Tanaka, “Multiobjective optimization using an aggregative gradient-based method”, Structural and Multidisciplinary Optimization, Vol. 51, No. 1, pp. 173–182, January
2015.
2. Kedar Nath Das and Raghav Prasad Parouha, “An ideal tri-population approach for unconstrained optimization and
applications”, Applied Mathematics and Computation, Vol. 256, pp. 666–701, April 1, 2015.
3. Nantiwat Pholdee, Won-Woong Park, Dong-Kyu Kim, Yong-Taek Im, Sujin Bureerat, Hyuck-Cheol Kwon and MyungSik Chun, “Efficient hybrid evolutionary algorithm for optimization of a strip coiling process”, Engineering Optimization,
Vol. 47, No. 4, pp. 521–532, April 3, 2015.
4. Mostafa Z. Ali and Noor H. Awad, “A novel class of niche hybrid Cultural Algorithms for continuous engineering
optimization”, Information Sciences, Vol. 267, pp. 158–190, May 20, 2014.
5. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionary
searches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,
March 2015.
6. Saber M. Elsayed, Ruhul A. Sarker and Efren Mezura-Montes, “Self-adaptive mix of particle swarm methodologies for
constrained optimization”, Information Sciences, Vol. 277, pp. 216–233, September 1, 2014.
7. Hossein Rajabalipour Cheshmehgaz, Md. Nazrul Islam and Mohammad Ishak Desa, “A polar-based guided multiobjective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network
design problem”, Journal of Intelligent Manufacturing, Vol. 25, No. 4, pp. 699-726, August 2014.
65
8. Nantiwat Pholdee and Sujin Bureerat, “Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design”, Engineering Optimization, Vol. 46, No. 8, pp. 1032–1051, August 3, 2014.
9. Yufei Zhuang and Haibin Huang, “Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle
swarm optimization algorithm”, Acta Astronautica, Vol. 94, No. 2, pp. 690–698, February 2014.
10. Takashi Okamoto and Hironori Hirata, “Constrained optimization using a multipoint type chaotic Lagrangian method
with a coupling structure”, Engineering Optimization, Vol. 45, No. 3, pp. 311–336, March 1, 2013.
11. Rommel G. Regis, “Constrained optimization by radial basis function interpolation for high-dimensional expensive blackbox problems with infeasible initial points”, Engineering Optimization, Vol. 46, No. 2, pp. 218–243, February 1, 2014.
• Leticia Cecilia Cagnina, Susana Cecilia Esquivel and Carlos A. Coello Coello, “A Fast Particle Swarm Algorithm For Solving Smooth and Non-smooth Economic Dispatch Problems”, Engineering Optimization, Vol.
43, No. 5, pp. 485–505, May 2011.
1. Shunkui Ke, Doudou Guo, Qingliang Niu and Danfeng Huang, “Optimized production planning model for a multi-plant
cultivation system under uncertainty”, Engineering Optimization, Vol. 47, No. 2, pp. 204–220, February 1, 2015.
2. Yu-Jun Zheng, “Water wave optimization: A new nature-inspired metaheuristic”, Computers & Operations Research,
Vol. 55, pp. 1–11, March 2015.
3. Maoguo Gong, Qing Cai, Xiaowei Chen and Lijia Ma, “Complex Network Clustering by Multiobjective Discrete Particle
Swarm Optimization Based on Decomposition”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 1, pp.
82–97, February 2014.
4. Jiuping Xu, Ziqiang Zeng, Bernard Han and Xiao Lei, “A dynamic programming-based particle swarm optimization
algorithm for an inventory management problem under uncertainty”, Engineering Optimization, Vol. 45, No. 7, pp.
851–880, July 1, 2013.
• M. Davarynejad, C. W. Ahn, J. Vrancken, J. van den Berg and C. A. Coello Coello, “Evolutionary Hidden
Information Detection by Granulation-Based Fitness Approximation”, Applied Soft Computing, Vol. 10, No.
3, pp. 719–729, June 2010.
1. Yaochu Jin, “Surrogate-assisted evolutionary computation: Recent advances and future challenges”, Swarm and Evolutionary Computation, Vol. 1, No. 2, pp. 61–70, June 2011.
2. Luc Wismans, Eric Van Berkum and Michiel Bliemer, “Acceleration of Solving the Dynamic Multi-Objective Network
Design Problem Using Response Surface Methods”, Journal of Intelligent Transporation Systems, Vol. 18, No. 1, pp.
17–29, January 2, 2014.
3. Alexander E.I. Brownlee, John A.W. McCall and Qingfu Zhang, “Fitness Modeling With Markov Networks”, IEEE
Transactions on Evolutionary Computation, Vol. 17, No. 6, pp. 862–879, December 2013.
4. Alexander E. Brownlee, Olivier Regnier-Coudert, John A.W. McCall, Stewart Massie and Stefan Stulajter, “An application of a GA with Markov network surrogate to feature selection”, International Journal of Systems Science, Vol. 44,
No. 11, pp. 2039–2056, November 1, 2013.
• Daniel Ortiz-Arroyo, Francisco Rodrı́guez-Henrı́quez and Carlos A. Coello Coello, “The Turing-850 Project:
Developing a Personal Computer in the Early 1980s in Mexico”, IEEE Annals of the History of Computing,
Vol. 32, No. 4, pp. 60–71, October-December 2010.
1. Ramesh Subramanian, “Technology Policy and National Identity: The Microcomputer Comes to India”, IEEE Annals
of the History of Computing, Vol. 36, No. 3, pp. 19–29, July-September 2014.
2. James W. Cortada, “How New Technologies Spread Lessons from Computing Technologies”, Technology and Culture,
Vol. 54, No. 2, pp. 229–261, April 2013.
• Oliver Schütze, Xavier Esquivel, Adriana Lara and Carlos A. Coello Coello, “Using the Averaged Hausdorff
Distance as a Performance Measure in Evolutionary Multi-Objective Optimization”, IEEE Transactions on
Evolutionary Computation, Vol. 16, No. 4, pp. 504–522, August 2012.
1. Yi Liu, ShiQi Li, JunFeng Wang, Hongmei Zeng and JiPing Lu, “A computer vision-based assistant system for the
assembly of narrow cabin products”, International Journal of Advanced Manufacturing Technology, Vol. 76, Nos. 1-4,
pp. 281–293, January 2015.
2. Jonathan E. Fieldsend and Richard M. Everson, “The Rolling Tide Evolutionary Algorithm: A Multiobjective Optimizer
for Noisy Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 103–117,
February 2015.
3. Gideon Avigad, Alex Goldvard and Shaul Salomon, “Time-response-based evolutionary optimization”, Engineering
Optimization, Vol. 47, No. 4, pp. 533–549, April 3, 2015.
66
4. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-Objective
Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.
5. Siwei Jiang, Yew-Soon Ong, Jie Zhang and Liang Feng, “Consistencies and Contradictions of Performance Metrics in
Multiobjective Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2391–2404, December 2014.
6. Edgar Galvan and Richard J. Malak, “P3GA: An Algorithm for Technology Characterization”, Journal of Mechanical
Design, Vol. 137, No. 1, Article Number: 011401, January 2015.
7. J.A. Adeyemo and O.O. Olofintoye, “Evaluation of Combined Pareto Multiobjective Differential Evolution on Tuneable
Problems”, International Journal of Simulation Modelling, Vol. 13, No. 3, pp. 276–287, September 2014.
8. Ioannis Giagkiozis and Peter J. Fleming, “Pareto Front Estimation for Decision Making”, Evolutionary Computation,
Vol. 22, No. 4, pp. 651–678, Winter 2014.
9. Ke Li, Qingfu Zhang, Sam Kwong, Miqing Li and Ran Wang, “Stable Matching-Based Selection in Evolutionary Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 6, pp. 909–923, December
2014.
10. Ke Li, Alvaro Fialho, Sam Kwong and Qingfu Zhang, “Adaptive Operator Selection With Bandits for a Multiobjective
Evolutionary Algorithm Based on Decomposition”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 1,
pp. 114–130, February 2014.
11. Vui Ann Shim, Kay Chen Tan, Chun Yew Cheong and Jun Yong Chia, “Enhancing the scalability of multi-objective
optimization via restricted Boltzmann machine-based estimation of distribution algorithm”, Information Sciences, Vol.
248, pp. 191–213, November 1, 2013.
• Oliver Schütze, Adriana Lara, Carlos A. Coello Coello, “On the Influence of the Number of Objectives on the
Hardness of a Multi-Objective Optimization Problem”, IEEE Transactions on Evolutionary Computation,
Vol. 15, No. 4, pp. 444–455, August 2011.
1. Sanghamitra Bandyopadhyay, Rudrasis Chakraborty and Ujjwal Maulik, “Priority based epsilon dominance: A new
measure in multiobjective optimization”, Information Sciences, Vol. 305, pp. 97–109, June 1, 2015.
2. Proteek Chandan Roy, Md. Monirul Islam, Kazuyuki Murase and Xin Yao, “Evolutionary Path Control Strategy for
Solving Many-Objective Optimization Problem”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 702–715, April
2015.
3. Miqing Li, Shengxiang Yang, Jinhua Zheng and Xiaohui Liu, “ETEA: A Euclidean Minimum Spanning Tree-Based
Evolutionary Algorithm for Multi-Objective Optimization”, Evolutionary Computation, Vol. 22, No. 2, pp. 189–230,
Summer 2014.
4. Miqing Li, Shengxiang Yang and Xiaohui Liu, “ Shift-Based Density Estimation for Pareto-Based Algorithms in ManyObjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 348–365, June 2014.
5. Miqing Li, Shengxiang Yang, Ke Li and Xiaohui Liu, “Evolutionary Algorithms with Segment-Based Search for Multiobjective Optimization Problems”, IEEE Transactions on Cybernetics, Vol. 44, No. 8, pp. 1295–1313, August 2014.
6. I. Giagkiozis and P.J. Fleming, “Methods for multi-objective optimization: An analysis”, Information Sciences, Vol.
293, pp. 338–350, February 1, 2015.
7. Vijay Rathod, Om Prakash Yadav, Ajay Rathore and Rakesh Jain, “Optimizing reliability-based robust design model
using multi-objective genetic algorithm”, Computers & Industrial Engineering, Vol. 66, No. 2, pp. 301–310, October
2013.
8. Christian von Lücken, Benjamı́n Barán and Carlos Brizuela, “A survey on multi-objective evolutionary algorithms for
many-objective problems”, Computational Optimization and Applications, Vol. 58, No. 3, pp. 707–756, July 2004.
9. Handing Wang and Xin Yao, “Corner Sort for Pareto-Based Many-Objective Optimization”, IEEE Transactions on
Cybernetics, Vol. 44, No. 1, pp. 92–102, January 2014.
10. Andre Britto and Aurora Pozo, “Using reference points to update the archive of MOPSO algorithms in Many-Objective
Optimization”, Neurocomputing, Vol. 127, pp. 78–87, March 15, 2014.
11. Yu Chen and Xiufen Zou, “Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts”, Information Sciences, Vol. 262, pp. 62–77, March 20, 2014.
12. Shengxiang Yang, Miqing Li, Xiaohui Liu and Jinhua Zheng, “ A Grid-Based Evolutionary Algorithm for Many-Objective
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 5, pp. 721–736, October 2013.
13. Rui Wang, Robin C. Purshouse and Peter J. Fleming, “Preference-Inspired Coevolutionary Algorithms for ManyObjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 4, pp. 474–494, August
2013.
14. Vui Ann Shim, Kay Chen Tan, Chun Yew Cheong and Jun Yong Chia, “Enhancing the scalability of multi-objective
optimization via restricted Boltzmann machine-based estimation of distribution algorithm”, Information Sciences, Vol.
248, pp. 191–213, November 1, 2013.
67
• B. Bernábe-Loranca, C.A. Coello-Coello and M. Osorio-Lama, “A Multiobjective Approach for the Heuristic
Optimization of Compactness and Homogeneity in the Optimal Zoning”, Journal of Applied Research and
Technology, Vol. 10, No. 3, pp. 447–457, June 2012.
1. A. Bustos, L. Herrera and E. Jimenez, “Efficient Frontier for Multi-Objective Stochastic Transportation Networks in
International Market of Perishable Goods”, Journal of Applied Research and Technology, Vol. 12, No. 4, pp. 654–665,
August 2014.
2. S. Elloumi and N. Benhad Braiek, “On Feedback Control Techniques of Nonlinear Analytic Systems”, Journal of Applied
Research and Technology, Vol. 12, No. 3, pp. 500–513, June 2014.
3. D.W. Kim, S. Ko and B.Y. Kang, “Structure Learning of Bayesian Networks by Estimation of Distribution Algorithms
with Transpose Mutation”, Journal of Applied Research and Technology, Vol. 11, pp. 586–596, August 2013.
• Edgar Alfredo Portilla-Flores, Efrén Mezura-Montes, Jaime Alvarez Gallegos, Carlos A. Coello Coello, Carlos A. Cruz-Villar and Miguel G. Villareal-Cervantes, “Parametric Reconfiguration Improvement in NonIterative Concurrent Mechatronic Design Using an Evolutionary-Based Approach”, Engineering Applications
of Artificial Intelligence, Vol. 24, No. 5, pp. 757–771, August 2011.
1. Badreddine El-Kribi, Ajmi Houidi, Zouhaier Affi and Lotfi Romdhane, “Application of multi-objective genetic algorithms
to the mechatronic design of a four bar system with continuous and discrete variables”, Mechanism and Machine Theory,
Vol. 61, pp. 68–83, March 2013.
• Alfredo Arias-Montaño, Carlos A. Coello Coello and Efrén Mezura-Montes, “Multi-Objective Evolutionary
Algorithms in Aeronautical and Aerospace Engineering”, IEEE Transactions on Evolutionary Computation,
Vol. 16, No. 5, pp. 662–694, October 2012.
1. Nikos D. Lagaros, “An efficient dynamic load balancing algorithm”, Computational Mechanics, Vol. 53, No. 1, pp.
59–76, January 2014.
2. Ocotlan Diaz-Parra, Jorge A. Ruiz-Vanoye, Beatriz Bernabe Loranca, Alejandro Fuentes-Penna and Ricardo A. BarreraCamara, “A Survey of Transportation Problems”, Journal of Applied Mathematics, Article Number: 848129, 2014.
3. Kazuhisa Chiba, Masahiro Kanazaki, Masaki Nakamiya, Koki Kitagawa and Toni Shimada, “Diversity of design knowledge for launch vehicle in view of fuels on hybrid rocket engine”, Journal of Advanced Mechanical Design Systems and
Manufacturing, Vol. 8, No. 3, Article Number: 14-00001, 2014.
4. Zhiwei Feng, Qingbin Zhang, Qiangang Tang, Tao Yang and Jianquan Ge, “Control-structure integrated multiobjective
design for flexible spacecraft using MOEA/D”, Structural and Multidisciplinary Optimization, Vol. 50, No. 2, pp.
347–362, August 2014.
5. Ya-zhong Luo and Li-ni Zhou, “Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization”,
Mathematical Problems in Engineering, Article Number: 823659, 2014.
6. A. Mohapatra, P.R. Bijwe and B.K. Panigrahi, “Efficient sensitivity based assessment of impact of uncertainties in
multi-objective framework”, International Journal of Electrical Power & Energy Systems, Vol. 64, pp. 947–955, January
2015.
7. Ke Li, Qingfu Zhang, Sam Kwong, Miqing Li and Ran Wang, “Stable Matching-Based Selection in Evolutionary Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 6, pp. 909–923, December
2014.
8. Ke Li and Sam Kwong, “A general framework for evolutionary multiobjective optimization via manifold learning”,
Neurocomputing, Vol. 146, pp. 65–74, December 25, 2014.
9. Payel Das, Zaid Chalabi, Benjamin Jones, James Milner, Clive Shrubsole, Michael Davies, Ian Hamilton, Ian Ridley
and Paul Wilkinson, “Multi-objective methods for determining optimal ventilation rates in dwellings”, Building and
Environment, Vol. 66, pp. 72–81, August 2013.
10. Chao Qian, Yang Yu and Zhi-Hua Zhou, “An analysis on recombination in multi-objective evolutionary optimization”,
Artificial Intelligence, Vol. 204, pp. 99–119, November 2013.
• Oliver Schuetze, Marco Laumanns, Emilia Tantar, Carlos A. Coello Coello and El-Ghazali Talbi, “Computing
gap-free Pareto front approximations with stochastic search algorithms”, Evolutionary Computation, Vol. 18,
No. 1, pp. 65–96, Spring 2010.
1. Hu Xia, Jian Zhuang and Dehong Yu, “Multi-objective unsupervised feature selection algorithm utilizing redundancy
measure and negative epsilon-dominance for fault diagnosis”, Neurocomputing, Vol. 146, pp. 113–124, December 25,
2014.
2. Mengqi Hu, Jeffery D. Weir and Teresa Wu, “An augmented multi-objective particle swarm optimizer for building cluster
operation decisions”, Applied Soft Computing, Vol. 25, pp. 347–359, December 2014.
68
3. William Carvajal-Carreno, Asuncion P. Cucala and Antonio Fernandez-Cardador, “Optimal design of energy-efficient
ATO CBTC driving for metro lines based on NSGA-II with fuzzy parameters”, Engineering Applications of Artificial
Intelligence, Vol. 36, pp. 164–177, November 2014.
4. Federico Zuiani and Massimiliano Vasile, “Multi Agent Collaborative Search based on Tchebycheff decomposition”,
Computational Optimization and Applications, Vol. 56, No. 1, pp. 189–208, September 2013.
5. Rudolf Berghammer, Tobias Friedrich and Frank Neumann, “Convergence of set-based multi-objective optimization,
indicators and deteriorative cycles”, Theoretical Computer Science, Vol. 456, pp. 2–17, October 19, 2012.
• Carlos Soza Canales, Ricardo Landa Becerra, Marı́a Cristina Riff and Carlos Coello Coello, “Solving Timetabling
Problems using a Cultural Algorithm”, Applied Soft Computing, Vol. 11, No. 1, pp. 337–344, January 2011.
1. Jiesheng Wang and Qiuping Guo, “Kernel Principal Component Analysis: Radial Basis Function Neural Networksbased
Soft-Sensor Modeling of Polymerizing Process Optimized by Cultural Differential Evolution Algorithm”, Instrumentation
Science & Technology, Vol. 41, No. 1, pp. 18–36, January 1, 2013.
2. Rui Zhang, Jianzhong Zhou, Li Mo, Shuo Ouyang and Xiang Liao, “Economic environmental dispatch using an enhanced
multi-objective cultural algorithm”, Electric Power Systems Research, Vol. 99, pp. 18–29, June 2013.
3. Jianmin Xu, Minjie Zhang and Yanguang Cai, “Cultural Ant Algorithm for Continuous Optimization Problems”, Applied
Mathematics & Information Sciences, Vol. 7, No. 2, pp. 705–710, June 2013.
4. Oliviu Matei, Petricia C. Pop, Jozsef Laszlo Sas and Camelia Chira, “An improved immigration memetic algorithm for
solving the heterogeneous fixed fleet vehicle routing problem”, Neurocomputing, Vol. 150, pp. 58–66, February 20, 2015.
5. Wei Wang, Yuling Song, Yanbing Xue, Hongling Jin, Juncai Hou and Minglei Zhao, “An optimal vibration control
strategy for a vehicle’s active suspension based on improved cultural algorithm”, Applied Soft Computing, Vol. 28, pp.
167–174, March 2015.
6. Matej Crepinsek, Shih-Hsi Liu and Marjan Mernik, “Exploration and Exploitation in Evolutionary Algorithms: A
Survey”, ACM Computing Surveys, Vol. 45, No. 3, Article Number: 35, June 2013.
7. Luis de-Marcos, Antonio Garcia-Cabot and Eva Garcia, “Evolutionary algorithms to solve loosely constrained permutCSPs: A practitioners approach”, International Journal of Innovative Computing Information and Control, Vol. 8, No.
7A, pp. 4771–4796, July 2012.
8. Rui Zhang, Jianzhong Zhou and Yongqiang Wang, “Multi-objective optimization of hydrothermal energy system considering economic and environmental aspects”, International Journal of Electrical Power & Energy Systems, Vol. 42,
No. 1, pp. 384–395, November 2012.
9. Wei Xu, Raofen Wang, Lingbo Zhang and Xingsheng Gu, “A multi-population cultural algorithm with adaptive diversity
preservation and its application in ammonia synthesis process”, Neural Computing & Applications, Vol. 21, No. 6, pp.
1129–1140, September 2012.
• Luis Martı́, Jesús Garcı́a, Antonio Berlanga, Carlos A. Coello Coello and José M. Molina, “MB-GNG:
Addressing Drawbacks in Multi-Objective Optimization Estimation of Distribution Algorithms”, Operations
Research Letters, Vol. 39, No. 2, pp. 150–154, March 2011.
1. Xiangtao Li, Jianan Wang and Minghao Yin, “Enhancing the performance of cuckoo search algorithm using orthogonal
learning method”, Neural Computing & Applications, Vol. 24, No. 6, pp. 1233–1247, May 2014.
2. Xiangtao Li and Minghao Yin, “Self-adaptive constrained artificial bee colony for constrained numerical optimization”,
Neural Computing & Applications, Vol. 24, Nos. 3-4, pp. 723–734, March 2014.
3. Qingyang Xu, Chengjin Zhang and Li Zhang, “A Fast Elitism Gaussian Estimation of Distribution Algorithm and
Application for PID Optimization”, Scientific World Journal, Article Number: 597278, 2014.
4. Hossein Karshenas, Roberto Santana, Concha Bielza and Pedro Larrañaga, “Multiobjective Estimation of Distribution
Algorithm Based on Joint Modeling of Objectives and Variables”, IEEE Transactions on Evolutionary Computation,
Vol. 18, No. 4, pp. 519–542, August 2014.
5. Xiangtao Li and Minghao Yin, “Hybrid Artificial Bee Colony and Biogeography Based Optimization for Global Numerical
Optimization”, Journal of Computational and Theoretical Nanoscience, Vol. 10, No. 5, pp. 1156–1163, May 2013.
6. Pedro Larrañaga, Hossein Karshenas, Concha Bielza and Roberto Santana, “A review on probabilistic graphical models
in evolutionary computation”, Journal of Heuristics, Vol. 18, No. 5, pp. 795–819, October 2012.
• J.J. Durillo, A.J. Nebro, C.A. Coello Coello, J. Garcı́a-Nieto, F. Luna and E. Alba, “A Study of MultiObjective Metaheuristics when Solving Parameter Scalable Problems”, IEEE Transactions on Evolutionary
Computation, Vol. 14, No. 4, pp. 618–635, August 2010.
1. Yu Zhang, Sanbo Hu, Jinglai Wu, Yunqing Zhang and Liping Chen, “Multi-objective optimization of double suction
centrifugal pump using Kriging metamodels”, Advances in Engineering Software, Vol. 74, pp. 16–26, August 2014.
69
2. Y.Z. Li, Q.H. Wu, M.S. Li and J.P. Zhan, “Mean-variance model for power system economic dispatch with wind power
integrated”, Energy, Vol. 72, pp. 510–520, August 1, 2014.
3. Riccardo Amirante, Luciano Andrea Catalano, Carlo Poloni and Paolo Tamburrano, “Fluid-dynamic design optimization
of hydraulic proportional directional valves”, Engineering Optimization, Vol. 46, No. 10, pp. 1295–1314, October 2014.
4. H.L. Liao, Q.H. Wu, Y.Z. Li and L. Jiang, “Economic emission dispatching with variations of wind power and loads
using multi-objective optimization by learning automata”, Energy Conversion and Management, Vol. 87, pp. 990–999,
November 2014.
5. Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano and Alexandre Claudio Botazzo Delbem, “General Subpopulation Framework and Taming the Conflict Inside Populations”, Evolutionary Computation, Vol. 23, No. 1, pp.
1–36, 2015.
6. Alfredo Nunez, Cristian E. Cortes, Doris Saez, Bart De Schutter and Michel Gendreau, “Multiobjective model predictive
control for dynamic pickup and delivery problems”, Control Engineering Practice, Vol. 32, pp. 73–86, November 2014.
7. Aimun Malik, Zheming Zhang and Ramesh K. Agarwal, “Extraction of battery parameters using a multi-objective
genetic algorithm with a non-linear circuit model”, Journal of Power Sources, Vol. 259, pp. 76–86, August 1, 2014.
8. Aris Lanaridis and Andreas Stafylopatis, “An artificial immune network for multiobjective optimization problems”,
Engineering Optimization, Vol. 46, No. 8, pp. 1008–1031, August 3, 2014.
9. Jonathan Brand, Zheming Zhang and Ramesh K. Agarwal, “Extraction of battery parameters of the equivalent circuit
model using a multi-objective genetic algorithm”, Journal of Power Sources, Vol. 247, pp. 729–737, February 1, 2014.
10. Vui Ann Shim, Kay Chen Tan, Chun Yew Cheong and Jun Yong Chia, “Enhancing the scalability of multi-objective
optimization via restricted Boltzmann machine-based estimation of distribution algorithm”, Information Sciences, Vol.
248, pp. 191–213, November 1, 2013.
11. David Greiner and Prabhat Hajela, “Truss topology optimization for mass and reliability considerations-co-evolutionary
multiobjective formulations”, Structural and Multidisciplinary Optimization, Vol. 45, No. 4, pp. 589–613, April 2012.
12. Zhou Wu and Tommy W.S. Chow, “A local multiobjective optimization algorithm using neighborhood field”, Structural
and Multidisciplinary Optimization, Vol. 46, No. 6, pp. 853–870, December 2012.
• Carlos A. Coello Coello, “Evolutionary Multi-Objective Optimization”, Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery, Vol. 1, No. 5, pp. 444–447, September/October 2011.
1. Feifei Dong, Yong Liu, Han Su, Rui Zou and Huaicheng Guo, “Reliability-oriented multi-objective optimal decisionmaking approach for uncertainty-based watershed load reduction”, Science of the Total Environment, Vol 515, pp.
39–48, May 15, 2015.
2. Miqing Li, Shengxiang Yang, Jinhua Zheng and Xiaohui Liu, “ETEA: A Euclidean Minimum Spanning Tree-Based
Evolutionary Algorithm for Multi-Objective Optimization”, Evolutionary Computation, Vol. 22, No. 2, pp. 189–230,
Summer 2014.
3. Miqing Li, Shengxiang Yang and Xiaohui Liu, “ Shift-Based Density Estimation for Pareto-Based Algorithms in ManyObjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 348–365, June 2014.
4. Thomas Weise, Raymond Chiong and Ke Tang, “Evolutionary Optimization: Pitfalls and Booby Traps”, Journal of
Computer Science and Technology, Vol. 27, No. 5, pp. 907–936, September 2012.
5. Guanghui Wang, Jie Chen, Tao Cai and Bin Xin, “Decomposition-based multi-objective differential evolution particle
swarm optimization for the design of a tubular permanent magnet linear synchronous motor”, Engineering Optimization,
Vol. 45, No. 9, pp. 1107–1127, September 1, 2013.
• Efrén Mezura-Montes and Carlos A. Coello Coello, “Constraint-Handling in Nature-Inspired Numerical
Optimization: Past, Present and Future”, Swarm and Evolutionary Computation, Vol. 1, No. 4, pp. 173–
194, December 2011.
1. Minggang Dong, Ning Wang, Xiaohui Cheng and Chuanxian Jiang, “Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems”, Mathematical Problems in Engineering, Article Number:
617905, 2014.
2. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics inspired optimization
(OIO)”, Computer-Aided Design, Vol. 63, pp. 52–71, June 2015.
3. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Adaptive Ranking Mutation Operator Based Differential Evolution for
Constrained Optimization”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 716–727, April 2015.
4. Rommel G. Regis, “Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization
Using Radial Basis Functions”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 326–347, June
2014.
70
5. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
6. Rodrigo Ribeiro de Lucena, Juliana Souza Baioco, Beatriz Souza Leite Pires de Lima, Carl Horst Albrecht and Breno
Pinheiro Jacob, “Optimal design of submarine pipeline routes by genetic algorithm with different constraint handling
techniques”, Advances in Engineering Software, Vol. 76, pp. 110–124, October 2014.
7. Zhenzhou Hu, Xinye Cai and Zhun Fan, “An improved memetic algorithm using ring neighborhood topology for constrained optimization”, Soft Computing, Vol. 18, No. 10, pp. 2023–2041, October 2014.
8. Jinn-Tsong Tsai, “Improved differential evolution algorithm for nonlinear programming and engineering design problems”, Neurocomputing, Vol. 148, pp. 628–640, January 19, 2015.
9. Kaustuv Nag, Tandra Pal and Nikhil R. Pal, “ASMiGA: An Archive-Based Steady-State Micro Genetic Algorithm”,
IEEE Transactions on Cybernetics, Vol. 45, No. 1, pp. 40–52, January 2015.
10. Mario Garza-Fabre, Eduardo Rodriguez-Tello and Gregorio Toscano-Pulido, “Constraint-handling through multi-objective
optimization: The hydrophobic-polar model for protein structure prediction”, Computers & Operations Research, Vol.
53, pp. 128–153, January 2015.
11. Ning Dong and Yuping Wang, “An Unbiased Bi-Objective Optimization Model and Algorithm for Constrained Optimization”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 28, No. 8, Article Number:
1459008, December 2014.
12. Hongwei Dai, Yu Yang, Hui Li and Cunhua Li, “Bi-direction quantum crossover-based clonal selection algorithm and its
applications”, Expert Systems with Applications, Vol. 41, No. 16, pp. 7248–7258, November 15, 2014.
13. Milan Tuba and Nebojsa Bacanin, “Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems”, Neurocomputing, Vol. 143, pp. 197–207, November 2, 2014.
14. Alfredo Nunez, Cristian E. Cortes, Doris Saez, Bart De Schutter and Michel Gendreau, “Multiobjective model predictive
control for dynamic pickup and delivery problems”, Control Engineering Practice, Vol. 32, pp. 73–86, November 2014.
15. Milan Tuba and Nebojsa Bacanin, “Artificial Bee Colony Algorithm Hybridized with Firefly Algorithm for Cardinality
Constrained Mean-Variance Portfolio Selection Problem”, Applied Mathematics & Information Sciences, Vol. 8, No. 6,
pp. 2831–2844, November 2014.
16. Baehyun Min, Joe M. Kang, Sunghoon Chung, Changhyup Park and Ilsik Jang, “Pareto-based multi-objective history
matching with respect to individual production performance in a heterogeneous reservoir”, Journal of Petroleum Science
and Engineering, Vol. 122, pp. 551–566, October 2014.
17. Ruhul A. Sarker, Saber M. Elsayed and Tapabrata Ray, “Differential Evolution With Dynamic Parameters Selection for
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 5, pp. 689–707, October 2014.
18. Rommel G. Regis, “Constrained optimization by radial basis function interpolation for high-dimensional expensive blackbox problems with infeasible initial points”, Engineering Optimization, Vol. 46, No. 2, pp. 218–243, February 1, 2014.
19. Zhuhong Zhang, Shigang Yue, Min Liao and Fei Long, “Danger theory based artificial immune system solving dynamic
constrained single-objective optimization”, Soft Computing, Vol. 18, No. 1, pp. 185–206, January 2014.
20. Paul Pitiot, Michel Aldanondo and Elise Vareilles, “Concurrent product configuration and process planning: Some
optimization experimental results”, Computers in Industry, Vol. 65, No. 4, pp. 610–621, May 2014.
21. J.M. Herrero, G. Reynoso-Meza, M. Martinez, X. Blasco and J. Sanchis, “A Smart-Distributed Pareto Front Using the
ev-MO GA Evolutionary Algorithm”, International Journal on Artificial Intelligence Tools, Vol. 23, No. 2, Article
Number: 1450002, April 2014.
22. Andrea Maesani, Pradeep Ruben Fernando and Dario Floreano, “Artificial Evolution by Viability Rather than Competition”, Plos One, Vol. 9, No. 1, Article Number: e86831, January 29, 2014.
23. Weiwei Zhang, Gary G. Yen and Zhongshi He, “Constrained Optimization via Artificial Immune System”, IEEE Transactions on Cybernetics, Vol. 44, No. 2, pp. 185–198, February 2014.
24. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Engineering optimization by means of an improved constrained differential evolution”, Computer Methods in Applied Mechanics and Engineering, Vol. 268, pp. 884–904, January 1,
2014.
25. Nikos D. Lagaros, “An efficient dynamic load balancing algorithm”, Computational Mechanics, Vol. 53, No. 1, pp.
59–76, January 2014.
26. Xinye Cai, Zhenzhou Hu and Zhun Fan, “A novel memetic algorithm based on invasive weed optimization and differential
evolution for constrained optimization”, Soft Computing, Vol. 17, No. 10, pp. 1893–1910, October 2013.
27. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “ Adaptive Configuration of evolutionary algorithms for
constrained optimization”, Applied Mathematics and Computation, Vol. 222, pp. 680–711, October 1, 2013.
28. A. Villagra, D. Pandolfi and G. Leguizamon, “ Handling constraints with an evolutionary tool for scheduling oil wells
maintenance visits”, Engineering Optimization, Vol. 45, No. 8, pp. 963–981, July-September, 2013.
71
29. Gilberto Reynoso-Meza, Sergio Garcia-Nieto, Javier Sanchis and F. Xavier Blasco, “Controller Tuning by Means of MultiObjective Optimization Algorithms: A Global Tuning Framework”, IEEE Transactions on Control Systems Technology,
Vol. 21, No. 2, pp. 445–458, March 2013.
30. Kalyanmoy Deb and Rituparna Datta, “A bi-objective constrained optimization algorithm using a hybrid evolutionary
and penalty function approach”, Engineering Optimization, Vol. 45, No. 5, pp. 503–527, May 1, 2013.
31. M.M. Ali and W.X. Zhu, “A penalty function-based differential evolution algorithm for constrained global optimization”,
Computational Optimization and Applications, Vol. 54, No. 3, pp. 707–739, April 2013.
32. Blaze Gjorgiev and Marko Cepin, “A multi-objective optimization based solution for the combined economic-environmental
power dispatch problem”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 1, pp. 417–429, January 2013.
33. Jun-fang Li, Bu-han Zhang, Yi-fang Liu, Kui Wang and Xiao-shan Wu, “Spatial evolution character of multi-objective
evolutionary algorithm based on self-organized criticality theory”, Physica A–Statistical Mechanics and its Applications,
Vol. 391, No. 22, pp. 5490–5499, November 15, 2012.
34. Nebojsa Bacanin and Milan Tuba, “Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved
with Genetic Operators”, Studies in Informatics and Control, Vol. 21, No. 2, pp. 137–146, June 2012.
• Victoria S. Aragón, Susana C. Esquivel and Carlos A. Coello Coello, “A modified version of a T-Cell Algorithm
for constrained optimization problems”, International Journal for Numerical Methods in Engineering, Vol.
84, No. 3, pp. 351–378, 15 October 2010.
1. Mostafa Z. Ali and Noor H. Awad, “A novel class of niche hybrid Cultural Algorithms for continuous engineering
optimization”, Information Sciences, Vol. 267, pp. 158–190, May 20, 2014.
2. Selim Yilmaz and Ecir U. Kucuksille, “A new modification approach on bat algorithm for solving optimization problems”,
Applied Soft Computing, Vol. 28, pp. 259–275, March 2015.
3. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.
53, No. 4, pp. 1168–1183, July 2014.
4. Weiwei Zhang, Gary G. Yen and Zhongshi He, “Constrained Optimization via Artificial Immune System”, IEEE Transactions on Cybernetics, Vol. 44, No. 2, pp. 185–198, February 2014.
5. Xiangtao Li and Minghao Yin, “Self-adaptive constrained artificial bee colony for constrained numerical optimization”,
Neural Computing & Applications, Vol. 24, Nos. 3-4, pp. 723–734, March 2014.
6. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
7. Issam Mazhoud, Khaled Hadj-Hamou, Jean Bigeon and Patrice Joyeux, “Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism”, Engineering Applications of Artificial Intelligence, Vol. 26,
No. 4, pp. 1263–1273, April 2013.
8. Amir Hossein Gandomi, Xin-She Yang, Siamak Talatahari and Suash Deb, “Coupled eagle strategy and differential
evolution for unconstrained and constrained global optimization”, Computers & Mathematics with Applications, Vol.
63, No. 1, pp. 191–200, January 2012.
• J.E. Mendoza, M.E. López, C.A. Coello Coello and E.A. López, “Microgenetic multiobjective reconfiguration
algorithm considering power losses and reliability indices for medium voltage distribution network”, IET
Generation, Transmission & Distribution, Vol. 3, No. 9, pp. 825-840, September 2009.
1. Zahra Moravej, Farhad Adelnia and Fazel Abbasi, “Optimal coordination of directional overcurrent relays using NSGAII”, Electric Power Systems Research, Vol. 119, pp. 228–236, February 2015.
2. Deepak Kumar and S.R. Samantaray, “Design of an advanced electric power distribution systems using seeker optimization algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 63, pp. 196–217, December
2014.
3. Andrea Mazza, Gianfranco Chicco and Angela Russo, “Optimal multi-objective distribution system reconfiguration
with multi criteria decision making-based solution ranking and enhanced genetic operators”, International Journal of
Electrical Power & Energy Systems, Vol. 54, pp. 255–267, January 2014.
4. Nikhil Gupta, Anil Swarnkar and K.R. Niazi, “Distribution network reconfiguration for power quality and reliability
improvement using Genetic Algorithms”, International Journal of Electrical Power & Energy Systems, Vol. 54, pp.
664–671, January 2014.
5. Bogdan Tomoiaga, Mircea Chindris, Andreas Sumper, Antoni Sudria-Andreu and Roberto Villafafila-Robles, “Pareto
Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II”, Energies, Vol.
6, No. 3, pp. 1439–1455, March 2013.
72
6. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
7. Lucas S.M. Guedes, Adriano C. Lisboa, Douglas A.G. Vieira and Rodney R. Saldanha, “A Multiobjective Heuristic for
Reconfiguration of the Electrical Radial Network”, IEEE Transactions on Power Delivery, Vol. 28, No. 1, pp. 311–319,
January 2013.
8. H. Nasiraghdam and S. Jadid, “Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multiobjective artificial bee colony (MOABC) algorithm”, Solar Energy, Vol. 86, No. 10, pp. 3057–3071, October 2012.
9. Peng Zhang, Wenyuan Li and Shouxiang Wang, “Reliability-oriented distribution network reconfiguration considering
uncertainties of data by interval analysis”, International Journal of Electrical Power & Energy Systems, Vol. 34, No. 1,
pp. 138–144, January 2012.
• Xiaolin Hu, Carlos A. Coello Coello and Zhangcan Huan, “A new multi-objective evolutionary algorithm:
neighbourhood exploring evolution strategy”, Engineering Optimization, Vol. 37, No. 4, pp. 351–379, June
2005.
1. Everardo Gutierrez and Carlos Brizuela, “An Enhanced MOGWW for the bi-objective Quadratic Assignment Problem”,
International Journal of Computational Intelligence Systems, Vol. 4, No. 4, pp. 530–549, June-August 2011.
• Alfredo G. Hernández-Dı́az, Luis V. Santana-Quintero, Carlos A. Coello Coello, Julián Molina and Rafael
Caballero, “Improving the efficiency of -dominance based grids”, Information Sciences, Vol. 181, No. 15,
pp. 3101–3129, 1 August 2011.
1. M.J. Mahmoodabadi, M. Taherkhorsandi and A. Bagheri, “Optimal robust sliding mode tracking control of a biped
robot based on ingenious multi-objective PSO”, Neurocomputing, Vol. 124, pp. 194–209, January 26, 2014.
2. M.J. Mahmoodabadi, S. Arabani Mostaghim, A. Bagheri and N. Nariman-zadeh, “Pareto optimal design of the decoupled sliding mode controller for an inverted pendulum system and its stability simulation via Java programming”,
Mathematical and Computer Modelling, Vol. 57, Nos. 5-6, pp. 1070–1082, March 2013.
3. Ke Li, Sam Kwong, Jingjing Cao, Miqing Li, Jinhua Zheng and Ruimin Shen, “Achieving balance between proximity
and diversity in multi-objective evolutionary algorithm”, Information Sciences, Vol. 182, No. 1, pp. 220–242, January
1, 2012.
• Antonin Ponsich and Carlos A. Coello Coello, “Differential Evolution performances for the solution of mixed
integer constrained Process Engineering problems”, Applied Soft Computing, Vol. 11, No. 1, pp. 399–409,
January 2011.
1. Yu Chen, Weicheng Xie and Xiufen Zou, “A binary differential evolution algorithm learning from explored solutions”,
Neurocomputing, Vol. 149, Part B, pp. 1038–1047, February 3, 2015.
2. Xiangyin Zhang and Haibin Duan, “An improved constrained differential evolution algorithm for unmanned aerial vehicle
global route planning”, Applied Soft Computing, Vol. 26, pp. 270–284, January 2015.
3. George Piliounis and Nikos D. Lagaros, “Reliability analysis of geostructures based on metaheuristic optimization”,
Applied Soft Computing, Vol. 22, pp. 544–565, September 2014.
4. Adam P. Piotrowski, “Differential Evolution algorithms applied to Neural Network training suffer from stagnation”,
Applied Soft Computing, Vol. 21, pp. 382–406, August 2014.
5. Min-Yuan Cheng and Nhat-Duc Hoang, “Groutability Estimation of Grouting Processes with Microfine Cements Using
an Evolutionary Instance-Based Learning Approach”, Journal of Computing in Civil Engineering, Vol. 28, No. 4, Article
Number: 04014014, July 2014.
6. Nikos D. Lagaros, “A general purpose real-world structural design optimization computing platform”, Structural and
Multidisciplinary Optimization, Vol. 49, No. 6, pp. 1047–1066, June 2014.
7. Jian Wang, Xiaolong Wang, Aipeng Jiang, Jiangzhou Shu and Pin Li, “Operational Optimization of Large-Scale ParallelUnit SWRO Desalination Plant Using Differential Evolution Algorithm”, Scientific World Journal, Article Number:
584068, 2014.
8. Shih-Hsi Liu, Marjan Mernik, Dejan Hrncic and Matej Crepinsek, “A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova’s mass transfer model”,
Applied Soft Computing, Vol. 13, No. 9, pp. 3792–3805, September 2013.
9. D. Iranshahi, M.R. Rahimpour, K. Paymooni and E. Pourazadi, “Utilizing DE optimization approach to boost hydrogen
and octane number, through a combination of radial-flow spherical and tubular membrane reactors in catalytic naphtha
reformers”, Fuel, Vol. 111, pp. 1–11, September 2013.
73
10. Zhen Yang, Qingni Yu, Wenping Dong, Xingsheng Gu, Wenming Qiao and Xiaoyi Liang, “Structure control classification
and optimization model of hollow carbon nanosphere core polymer particle based on improved differential evolution
support vector machine”, Applied Mathematical Modelling, Vol. 37, Nos. 12-13, pp. 7442–7451, July 1, 2013.
11. Nikos D. Lagaros, “An efficient dynamic load balancing algorithm”, Computational Mechanics, Vol. 53, No. 1, pp.
59–76, January 2014.
12. Ling Wang, Xiping Fu, Yunfei Mao, Muhammad Ilyas Menhas and Minrui Fei, “A novel modified binary differential
evolution algorithm and its applications”, Neurocomputing, Vol. 98, pp. 55–75, December 3, 2012.
13. E. Zio, L.R. Golea and G. Sansavini, “Optimizing protections against cascades in network systems: A modified binary
differential evolution algorithm”, Reliability Engineering & System Safety, Vol. 103, pp. 72–83, July 2012.
14. D. Iranshahi, E. Pourazadi, K. Paymooni and M.R. Rahimpour, “Utilizing DE optimization approach to boost hydrogen
and octane number in a novel radial-flow assisted membrane naphtha reactor”, Chemical Engineering Science, Vol. 68,
No. 1, pp. 236–249, January 22, 2012.
15. Xianhui Zeng, Wai-Keung Wong and Sunney Yung-Sun Leung, “An operator allocation optimization model for balancing control of the hybrid assembly lines using Pareto utility discrete differential evolution algorithm”, Computers &
Operations Research, Vol. 39, No. 5, pp. 1145–1159, May 2012.
16. Leandro dos Santos Coelho and Marcelo Wicthoff Pessoa, “A tuning strategy for multivariable PI and PID controllers
using differential evolution combined with chaotic Zaslavskii map”, Expert Systems with Applications, Vol. 38, No. 11,
pp. 13694–13701, October 2011.
17. D. Iranshahi, A.M. Bahmanpour, K. Paymooni, M.R. Rahimpour and A. Shariati, “Simultaneous hydrogen and aromatics
enhancement by obtaining optimum temperature profile and hydrogen removal in naphtha reforming process; a novel
theoretical study”, International Journal of Hydrogen Energy, Vol. 36, No. 14, pp. 8316–8326, July 2011.
• E. Mezura-Montes, C. A. Coello Coello, J. Velázquez-Reyes and L. Muñoz-Dávila, “Multiple trial vectors in
differential evolution for engineering design”, Engineering Optimization, Vol. 39, No. 5, pp. 567-589, July
2007.
1. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Adaptive Ranking Mutation Operator Based Differential Evolution for
Constrained Optimization”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 716–727, April 2015.
2. Selim Yilmaz and Ecir U. Kucuksille, “A new modification approach on bat algorithm for solving optimization problems”,
Applied Soft Computing, Vol. 28, pp. 259–275, March 2015.
3. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Engineering optimization by means of an improved constrained differential evolution”, Computer Methods in Applied Mechanics and Engineering, Vol. 268, pp. 884–904, January 1,
2014.
4. Harish Garg, “Solving Structural Engineering Design Optimization Problems using an Artificial Bee Colony Algorithm”,
Journal of Industrial and Management Optimization, Vol. 10, No. 3, pp. 777–794, July 2014.
5. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
6. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Cuckoo search algorithm: a metaheuristic approach to
solve structural optimization problems”, Engineering with Computers, Vol. 29, No. 1, pp. 17–35, January 2013.
7. Adam Slowik, “Application of an Adaptive Differential Evolution Algorithm With Multiple Trial Vectors to Artificial
Neural Network Training”, IEEE Transactions on Industrial Electronics, Vol. 58, No. 8, pp. 3160–3167, August 2011.
• Eduardo Fernández, Edy López, Sergio Bernal, Carlos A. Coello Coello and Jorge Navarro, “Evolutionary
multiobjective optimization using an outranking-based dominance generalization”, Computers & Operations
Research, Vol. 37, No. 2, pp. 390–395, February 2010.
1. Na Chen and Zeshui Xu, “Hesitant fuzzy ELECTRE II approach: A new way to handle multi-criteria decision making
problems”, Information Sciences, Vol. 292, pp. 175–197, January 20, 2015.
2. Kaveh Khalili-Damghani, Bahram Aminzadeh-Goharrizi, Saeed Rastegar and Babak Aminzadeh-Goharrizi, “Solving
land-use suitability analysis and planning problem by a hybrid meta-heuristic algorithm”, International Journal of
Geographical Information Science, Vol. 28, No. 12, pp. 2390–2416, December 2, 2014.
3. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
4. S.A. Torabi, N. Sahebjamnia, S.A. Mansouri and M. Aramon Bajestani, “A particle swarm optimization for a fuzzy multiobjective unrelated parallel machines scheduling problem”, Applied Soft Computing, Vol. 13, No. 12, pp. 4750–4762,
December 2013.
74
5. Eunice Oliveira, Carlos Henggeler Antunes and Alvaro Gomes, “A comparative study of different approaches using an
outranking relation in a multi-objective evolutionary algorithm”, Computers & Operations Research, Vol. 40, No. 6, pp.
1602–1615, June 2013.
6. Milosz Kadzinski, Salvatore Greco and Roman Slowinski, “Selection of a representative set of parameters for robust
ordinal regression outranking methods”, Computers & Operations Research, Vol. 39, No. 11, pp. 2500–2519, November
2012.
7. Esra Bas, “Surrogate relaxation of a fuzzy multidimensional 0-1 knapsack model by surrogate constraint normalization
rules and a methodology for multi-attribute project portfolio selection”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 5, pp. 958–970, August 2012.
8. Ozgur Kabak and Da Ruan, “A comparison study of fuzzy MADM methods in nuclear safeguards evaluation”, Journal
of Global Optimization, Vol. 51, No. 2, pp. 209–226, October 2011.
• Mario Villalobos-Arias, Carlos A. Coello Coello and Onésimo Hernández-Lerma, “Asymptotic Convergence of
Metaheuristics for Multiobjective Optimization Problems”, Soft Computing, Vol. 10, No. 11, pp. 1001–1005,
September 2006.
1. Bhupendra Kumar Pathak and Sanjay Srivastava, “Integrated Fuzzy-HMH for project uncertainties in time-cost tradeoff
problem”, Applied Soft Computing, Vol. 21, pp. 320–329, August 2014.
2. Yu Chen, Xiufen Zou and Weicheng Xie, “Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front”, Information Sciences, Vol. 181, No. 16, pp. 3336–3355, August 15,
2011.
• Oliver Schütze, Carlos A. Coello Coello, Sanaz Mostaghim, El-Ghazali Talbi and Michael Dellnitz, “Hybridizing Evolutionary Strategies with Continuation Methods for Solving Multi-Objective Problems”, Engineering
Optimization, Vol. 40, No. 5, pp. 383–402, May 2008.
1. Miqing Li, Shengxiang Yang, Ke Li and Xiaohui Liu, “Evolutionary Algorithms with Segment-Based Search for Multiobjective Optimization Problems”, IEEE Transactions on Cybernetics, Vol. 44, No. 8, pp. 1295–1313, August 2014.
2. Benjamin Martin, Alexandre Goldsztejn, Laurent Granvilliers and Christophe Jermann, “Certified Parallelotope Continuation for One-Manifolds”, SIAM Journal on Numerical Analysis, Vol. 51, No. 6, pp. 3373–3401, 2013.
3. Guangyong Sun, Guangyao Li, Zhihui Gong, Guanqiang He and Qing Li, “Radial basis functional model for multiobjective sheet metal forming optimization”, Engineering Optimization, Vol. 43, No. 12, pp. 1351–1366, 2011.
4. Ahmad Nourbakhsh, Hamed Safikhani and Shahram Derakhshan, “The comparison of multi-objective particle swarm
optimization and NSGA II algorithm: applications in centrifugal pumps”, Engineering Optimization, Vol. 43, No. 10,
pp. 1095–1113, 2011.
5. Peter A. N. Bosman, “On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization”,
IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 51–69, February 2012.
6. Yu Chen, Xiufen Zou and Weicheng Xie, “Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front”, Information Sciences, Vol. 181, No. 16, pp. 3336–3355, August 15,
2011.
• Victoria S. Aragón, Susana C. Esquivel and Carlos A. Coello Coello, “Optimizing Constrained Problems
through a T-Cell Artificial Immune System”, Journal of Computer Science & Technology, Vol. 8, No. 3, pp.
158–165, 2008.
1. Zhuhong Zhang, Shigang Yue, Min Liao and Fei Long, “Danger theory based artificial immune system solving dynamic
constrained single-objective optimization”, Soft Computing, Vol. 18, No. 1, pp. 185–206, January 2014.
2. Zhuhong Zhang and Shuqu Qian, “Artificial immune system in dynamic environments solving time-varying non-linear
constrained multi-objective problems”, Soft Computing, Vol. 15, No. 7, pp. 1333–1349, July 2011.
• Carlos A. Coello Coello, “Evolutionary Multi-Objective Optimization: Some Current Research Trends and
Topics that Remain to be Explored”, Frontiers of Computer Science in China, Vol. 3, No. 1, pp. 18–30,
2009.
1. Ruby L.V. Moritz, Enrico Reich, Maik Schwarz, Matthias Bernt and Martin Middendorf, “Refined ranking relations for
selection of solutions in multi objective metaheuristics”, European Journal of Operational Research, Vol. 243, No. 2, pp.
454–464, June 1, 2015.
2. Swaantje Casjens, Holger Schwender, Thomas Bruning and Katja Ickstadt, “A novel crossover operator based on variable
importance for evolutionary multi-objective optimization with tree representation”, Journal of Heuristics, Vol. 21, No.
1, pp. 1–24, February 2015.
75
3. Christian von Lücken, Benjamı́n Barán and Carlos Brizuela, “A survey on multi-objective evolutionary algorithms for
many-objective problems”, Computational Optimization and Applications, Vol. 58, No. 3, pp. 707–756, July 2004.
4. Yu Chen and Xiufen Zou, “Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts”, Information Sciences, Vol. 262, pp. 62–77, March 20, 2014.
5. Carolina P. Almeida, Richard A. Goncalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, “An
experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem”, Annals of Operations
Research, Vol. 199, No. 1, pp. 305–341, October 2012.
6. Chin Wei Bong, Hong Yoong Lam, Ahamad Tajudin Khader and Hamzah Kamarulzaman, “Adaptive multi-objective
archive-based hybrid scatter search for segmentation in lung computed tomography imaging”, Engineering Optimization,
Vol. 44, No. 3, pp. 327–350, 2012.
7. C.W. Bong and M. Rajeswari, “Multiobjective clustering with metaheuristic: current trends and methods in image
segmentation”, IET Image Processing, Vol. 6, No. 1, pp. 1–10, February 2012.
8. Romanas Puisa and Heinrich Streckwall, “Prudent constraint-handling technique for multiobjective propeller optimisation”, Optimization and Engineering, Vol. 12, No. 4, pp. 657–680, December 2011.
9. Chi Zhang, Jose Emmanuel Ramirez-Marquez and Claudio M. Rocco Sanseverino, “A holistic method for reliability
performance assessment and critical components detection in complex networks”, IIE Transactions, Vol. 43, No. 9, pp.
661–675, 2011.
10. Claudio M. Rocco, Jose Emmanuel Ramirez-Marquez, Daniel E. Salazar and Cesar Yajure, “Assessing the Vulnerability
of a Power System Through a Multiple Objective Contingency Screening Approach”, IEEE Transactions on Reliability,
Vol. 60, No. 2, pp. 394–403, June 2011.
11. Chin-Wei Bong and Mandava Rajeswari, “Multi-objective nature-inspired clustering and classification techniques for
image segmentation”, Applied Soft Computing, Vol. 11, No. 4, pp. 3271–3282, June 2011.
12. James Bekker and Chris Aldrich, “The cross-entropy method in multi-objective optimisation: An assessment”, European
Journal of Operational Research, Vol. 211, No. 1, pp. 112–121, May 16, 2011.
13. S.I. Sulaiman, T.K.A. Rahman and I. Musirin, “Multi-Objective Evolutionary Programming for Optimal Grid-Connected
Photovoltaic System Design”, International Review of Electrical Engineering–IREE, Part B, Vol. 5, No. 6, pp. 2936–
2944, November-December 2010.
• Luis V. Santana-Quintero, Alfredo G. Hernández-Dı́az, Julián Molina, Carlos A. Coello Coello and Rafael
Caballero, “DEMORS: A hybrid Multi-Objective Optimization Algorithm using Differential Evolution and
Rough Sets for Constrained Problems”, Computers & Operations Research, Vol. 37, No. 3, pp. 470–480,
March 2010.
1. Minggang Dong, Ning Wang, Xiaohui Cheng and Chuanxian Jiang, “Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems”, Mathematical Problems in Engineering, Article Number:
617905, 2014.
2. Yu Xue, Yi Zhuang, Tianquan Ni, Siru Ni and Xuezhi Wen, “Self-adaptive learning based discrete differential evolution
algorithm for solving CJWTA problem”, Journal of Systems Engineering and Electronics, Vol. 25, No. 1, pp. 59–68,
February 2014.
3. Bili Chen, Wenhua Zeng, Yangbin Lin and Defu Zhang, “A New Local Search-Based Multiobjective Optimization
Algorithm”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 50–73, February 2015.
4. Rommel G. Regis, “Constrained optimization by radial basis function interpolation for high-dimensional expensive blackbox problems with infeasible initial points”, Engineering Optimization, Vol. 46, No. 2, pp. 218–243, February 1, 2014.
5. Xianpeng Wang and Lixin Tang, “Multiobjective Operation Optimization of Naphtha Pyrolysis Process Using Parallel
Differential Evolution”, Industrial & Engineering Chemistry Research, Vol. 52, No. 40, pp. 14415–14428, October 9,
2013.
6. Xiang Li and Gang Du, “BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems”,
Computers & Operations Research, Vol. 40, No. 1, pp. 282–302, January 2013.
7. Feng Qian, Bing Xu, Rongbin Qi and Huaglory Tianfield, “Self-adaptive differential evolution algorithm with αconstrained-domination principle for constrained multi-objective optimization”, Soft Computing, Vol. 16, No. 8, pp.
1353–1372, August 2012.
8. Manuel Chica, Oscar Cordon and Sergio Damas, “An advanced multiobjective genetic algorithm design for the time and
space assembly line balancing problem”, Computers & Industrial Engineering, Vol. 61, No. 1, pp. 103–117, August
2011.
9. Swagatam Das and Ponnuthurai Nagaratnam Suganthan, “Differential Evolution: A Survey of the State-of-the-Art”,
IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 27–54, February 2011.
76
• Nareli Cruz Cortés, Francisco Rodrı́guez-Henrı́quez and Carlos A. Coello Coello, “An Artificial Immune
System Heuristic for Generating Short Addition Chains”, IEEE Transactions on Evolutionary Computation,
Vol. 12, No. 1, pp. 1–24, February 2008.
1. Saul Dominguez-Isidro, Efren Mezura-Montes and Luis-Guillermo Osorio-Hernandez, “Evolutionary programming for
the length minimization of addition chains”, Engineering Applications of Artificial Intelligence, Vol. 37, pp. 125–134,
January 2015.
2. Yin Li, Gong-Liang Chen, Yi-Yang Chen and Jian-Hua Li, “An improvement of the TyT algorithm for GF(2(M)) Based
on Reusing Intermediate Computation Results”, Communications in Mathematical Sciences, Vol. 9, No. 1, pp. 277–287,
March 2011.
• Guillermo Leguizamón and Carlos A. Coello Coello, “Boundary Search for Constrained Numerical Optimization Problems with an Algorithm Inspired on the Ant Colony Metaphor”, IEEE Transactions on Evolutionary
Computation, Vol. 13, No. 2, pp. 350–368, April 2009.
1. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics inspired optimization
(OIO)”, Computer-Aided Design, Vol. 63, pp. 52–71, June 2015.
2. Xiangtao Li and Minghao Yin, “Self-adaptive constrained artificial bee colony for constrained numerical optimization”,
Neural Computing & Applications, Vol. 24, Nos. 3-4, pp. 723–734, March 2014.
3. Yue-Jiao Gong, Jun Zhang, Henry Shu-Hung Chung, Wei-Neng Chen, Zhi-Hui Zhan, Yun Li and Yu-Hui Shi, “An
Efficient Resource Allocation Scheme Using Particle Swarm Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 6, pp. 801–816, December 2012.
4. Abu S.S.M. Barkat Ullah, Ruhul Sarker and Chris Lokan, “Handling equality constraints in evolutionary optimization”,
European Journal of Operational Research, Vol. 221, No. 3, pp. 480–490, September 16, 2012.
5. Haibo Zhang and G.P. Rangaiah, “An efficient constraint handling method with integrated differential evolution for
numerical and engineering optimization”, Computers & Chemical Engineering, Vol. 37, pp. 74–88, February 10, 2012.
6. Chih-Ming Hsu, “Applying genetic programming and ant colony optimisation to improve the geometric design of a
reflector”, International Journal of Systems Science, Vol. 43, No. 5, pp. 972–986, 2012.
7. Ali Husseinzadeh Kashan, “An efficient algorithm for constrained global optimization and application to mechanical
engineering design: League championship algorithm (LCA)”, Computer-Aided Design, Vol. 43, No. 12, pp. 1769–1792,
December 2011.
8. David B. Fogel, “Revisiting Overlooked Foundations of Evolutionary Computation: Part I”, Cybernetics and Systems,
Vol. 41, No. 5, pp. 343–358, 2010.
9. Zhongliang Pan,Ling Chen and Guangzhao Zhang, “A Relevance Feedback Method Based on Ant Colony Algorithm
with Chaos for Image Retrieval Dependencies”, Journal of Computational Information Systems, Vol. 5, No. 6, pp.
1767–1774, 2009.
• Eduardo Fernández González, Edy López, Fernando López and Carlos A. Coello Coello, “Increasing Selective
Pressure Towards the Best Compromise in Evolutionary Multiobjective Optimization: The Extended NOSGA
Method”, Information Sciences, Vol. 181, pp. 44–56, 2011.
1. Jiancheng Long, W.Y. Szeto and Hai-Jun Huang, “A bi-objective turning restriction design problem in urban road
networks”, European Journal of Operational Research, Vol. 237, No. 2, pp. 426–439, September 1, 2014.
2. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Sergio Garcia-Nieto, “Physical programming for preference
driven evolutionary multi-objective optimization”, Applied Soft Computing, Vol. 24, pp. 341–362, November 2014.
3. Sultan Nomal Qasem, Siti Mariyam Shamsuddin, Siti Zaiton Mohd Hashim, Maslina Darus and Eiman Al-Shammari,
“Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems”,
Information Sciences, Vol. 239, pp. 165–190, August 1, 2013.
4. Huanlai Xing and Rong Qu, “A nondominated sorting genetic algorithm for bi-objective network coding based multicast
routing problems”, Information Sciences, Vol. 233, pp. 36–53, June 1, 2013.
5. Dunwei Gong, Jing Sun and Xinfang Ji, “Evolutionary algorithms with preference polyhedron for interval multi-objective
optimization problems”, Information Sciences, Vol. 233, pp. 141–161, June 1, 2013.
6. Gilberto Reynoso-Meza, Xavier Blasco, Javier Sanchis and Juan M. Herrero, “Comparison of design concepts in multicriteria decision-making using level diagrams”, Information Sciences, Vol. 221, pp. 124–141, February 1, 2013.
7. Kaveh Khalili-Damghani, Soheil Sadi-Nezhad, Farhad Hosseinzadeh Lotfi and Madjid Tavana, “A hybrid fuzzy rulebased multi-criteria framework for sustainable project portfolio selection”, Information Sciences, Vol. 220, pp. 442–462,
January 20, 2013.
77
8. Liang Huang, Il Hong Suh and Ajith Abraham, “Dynamic multi-objective optimization based on membrane computing
for control of time-varying unstable plants”, Information Sciences, Vol. 181, No. 11, pp. 2370–2391, June 1, 2011.
9. Sultan Noman Qasem and Siti Mariyam Shamsuddin, “Memetic Elitist Pareto Differential Evolution algorithm based
Radial Basis Function Networks for classification problems”, Applied Soft Computing, Vol. 11, No. 8, pp. 5565–5581,
December 2011.
10. Jun Wang, Hong Peng and Peng Shi, “An optimal image watermarking approach based on a multi-objective genetic
algorithm”, Information Sciences, Vol. 181, No. 24, pp. 5501–5514, December 15, 2011.
11. Rodrigo C. Barros, Duncan D. Ruiz and Marcio P. Basgalupp, “Evolutionary model trees for handling continuous classes
in machine learning”, Information Sciences, Vol. 181, No. 5, pp. 954–971, March 1, 2011.
• Enrique Alba, Gabriel Luque, Carlos A. Coello Coello and Erika Hernández Luna, “A Comparative Study
of Serial and Parallel Heuristics Used to Design Combinational Logic Circuits”, Optimization Methods and
Software, Vol. 22, No. 3, pp. 485–509, June 2007.
1. Ioannis C. Kampolis and Kyriakos C. Giannakoglou, “Synergetic use of different evaluation, parameterization and search
tools within a multilevel optimization platform”, Applied Soft Computing, Vol. 11, No. 1, pp. 645–651, January 2011.
• Daniel Cortés Rivera, Ricardo Landa Becerra and Carlos A. Coello Coello, “Cultural Algorithms, an Alternative Heuristic to Solve the Job Shop Scheduling Problem”, Engineering Optimization, Vol. 39, No. 1, pp.
69–85, January 2007.
1. Ilhern Boussaid, Julien Lepagnot and Patrick Siarry, “A survey on optimization metaheuristics”, Information Sciences,
Vol. 237, pp. 82–117, July 10, 2013.
2. Binchao Chen and Timothy I. Matis, “A flexible dispatching rule for minimizing tardiness in job shop scheduling”,
International Journal of Production Economics, Vol. 141, No. 1, pp. 360–365, January 2013.
3. Virginia Yannibelli and Analia Amandi, “Project scheduling: A multi-objective evolutionary algorithm that optimizes
the effectiveness of human resources and the project makespan”, Engineering Optimization, Vol. 45, No. 1, pp. 45–65,
2013.
4. Weiling Wang and Tieke Li, “Improved Cultural Algorithms for Job Shop Scheduling Problem”, International Journal
of Industrial Engineering–Theory Applications and Practice, Vol. 18, No. 4, pp. 162–168, 2011.
5. Rui Zhang and Cheng Wu, “A divide-and-conquer strategy with particle swarm optimization for the job shop scheduling
problem”, Engineering Optimization, Vol. 42, No. 7, pp. 641–670, 2010.
• Pablo E. Oñate Yumbla, Juan M. Ramirez and Carlos A. Coello Coello, “Optimal power flow subject to
security constraints solved with a particle swarm optimizer”, IEEE Transactions on Power Systems, Vol. 23,
No. 1, pp. 33–40, February 2008.
1. Yu-Cheng Chang, Tsung-Ying Lee, Chu-Lung Chen and Rong-Mow Jan, “Optimal power flow of a wind-thermal generation system”, International Journal of Electrical Power & Energy Systems, Vol. 55, pp. 312–320, February 2014.
2. Yan Xu, Zhao Yang Dong, Rui Zhang, Kit Po Wong and Mingyong Lai, “Solving Preventive-Corrective SCOPF by a
Hybrid Computational Strategy”, IEEE Transactions on Power Systems, Vol. 29, No. 3, pp. 1345–1355, May 2014.
3. Nima Amjady and Mohammad Reza Ansari, “Non-convex security constrained optimal power flow by a new solution
method composed of Benders decomposition and special ordered sets”, International Transactions on Electrical Energy
Systems, Vol. 24, No. 6, pp. 842–857, June 2014.
4. Rui Zhang, Zhao Yang Dong, Yan Xu, Kit Po Wong and Mingyong Lai, “Hybrid computation of corrective securityconstrained optimal power flow problems”, IET Generation Transmission & Distribution, Vol. 8, No. 6, pp. 995–1006,
June 2014.
5. Marian G. Marcovecchio, Augusto Q. Novais and Ignacio E. Grossmann, “Deterministic optimization of the thermal Unit
Commitment problem: A Branch and Cut search”, Computers & Chemical Engineering, Vol. 67, pp. 53–68, August 4,
2014.
6. A. Ananthi Christy and P. Ajay D. Vimal Raj, “Adaptive biogeography based predator-prey optimization technique for
optimal power flow”, International Journal of Electrical Power & Energy Systems, Vol. 62, pp. 344–352, November
2014.
7. Mahmood Joorabian and Ehsan Afzalan, “Optimal power flow under both normal and contingent operation conditions
using the hybrid fuzzy particle swarm optimisation and Nelder-Mead algorithm (HFPSO-NM)”, Applied Soft Computing,
Vol. 14, pp. 623–633, Part C, January 2014.
8. Mohammad Sadegh Jahan and Nima Amjady, “ Solution of large-scale security constrained optimal power flow by a
new bi-level optimisation approach based on enhanced gravitational search algorithm”, IET Generation Transmission
& Distribution, Vol. 7, No. 12, pp. 1481–1491, December 2013.
78
9. Pierluigi Siano and Geev Mokryani, “Assessing Wind Turbines Placement in a Distribution Market Environment by
Using Particle Swarm Optimization”, IEEE Transactions on Power Systems, Vol. 28, No. 4, pp. 3852–3864, November
2013.
10. Pierluigi Siano and Geev Mokryani, “Probabilistic Assessment of the Impact of Wind Energy Integration Into Distribution Networks”, IEEE Transactions on Power Systems, Vol. 28, No. 4, pp. 4209–4217, November 2013.
11. Geev Mokryani and Pieluigi Siano, “Optimal wind turbines placement within a distribution market environment”,
Applied Soft Computing, Vol. 13, No. 10, pp. 4038–4046, October 2013.
12. Qin Wang, James D. McCalley, Tongxin Zheng and Eugene Litvinov, “A Computational Strategy to Solve Preventive
Risk-Based Security-Constrained OPF”, IEEE Transactions on Power Systems, Vol. 28, No. 2, pp. 1666–1675, May
2013.
13. S. Conti, R. Nicolosi, S.A. Rizzo and H.H. Zeineldin, “Optimal Dispatching of Distributed Generators and Storage
Systems for MV Islanded Microgrids”, IEEE Transactions on Power Delivery, Vol. 27, No. 3, pp. 1243–1251, July 2012.
14. Nima Amjady, Hamzeh Fatemi and Hamidreza Zareipour, “Solution of Optimal Power Flow Subject to Security Constraints by a New Improved Bacterial Foraging Method”, IEEE Transactions on Power Systems, Vol. 27, No. 3, pp.
1311–1323, August 2012.
15. A. Bhattacharya and P.K. Roy, “Solution of multi-objective optimal power flow using gravitational search algorithm”,
IET Generation, Transmission & Distribution, Vol. 6, No. 8, pp. 751–763, August 2012.
16. Jingrui Zhang, Jian Wang and Chaoyuan Yue, “Small Population-Based Particle Swarm Optimization for Short-Term
Hydrothermal Scheduling”, IEEE Transactions on Power Systems, Vol. 27, No. 1, pp. 142–152, February 2012.
17. A.F. Zobaa and A. Lecci, “Particle swarm optimisation of resonant controller parameters for power converters”, IET
Power Electronics, Vol. 4, No. 2, pp. 235–241, 2011.
18. Ruey-Hsun Liang, Sheng-Ren Tsai, Yie-Tone Chen and Wan-Tsun Tseng, “Optimal power flow by a fuzzy based hybrid
particle swarm optimization approach”, Electric Power Systems Research, Vol. 81, No. 7, pp. 1466–1474, July 2011.
19. Nima Amjady and Hossein Sharifzadeh, “Security constrained optimal power flow considering detailed generator model
by a new robust differential evolution algorithm”, Electric Power Systems Research, Vol. 81, No. 2, pp. 740–749,
February 2011.
20. N.B. Muthuselvan, M. Devesh Raj and P. Somasundaram, “Cauchy - Gaussian Infused Particle Swarm Optimization for
Economic Dispatch with Wind Power Generation”, International Review of Electrical Engineering–IREE, Part B, Vol.
6, No. 1, pp. 387–395, January-February 2011.
21. A. Lashkar Ara, A. Kazemi and S.A. Nabavi Niaki, “Optimal location of Hybrid Flow Controller considering modified
steady-state model”, Applied Energy, Vol. 88, No. 5, pp. 1578–1585, May 2011.
22. A. Lashkar Ara, A. Kazemi and S.A. Nabavi Niaki, “Modelling of Optimal Unified Power Flow Controller (OUPFC)
for optimal steady-state performance of power systems”, Energy Conversion and Management, Vol. 52, No. 2, pp.
1325–1333, February 2011.
23. A. Bhattacharya and P.K. Chattopadhyay, “Application of biogeography-based optimisation to solve different optimal
power flow problems”, IET Generation Transmission & Distribution, Vol. 5, No. 1, pp. 70–80, January 2011.
24. D.C. Secui, I. Felea, S. Dzitac and L. Popper, “A Swarm Intelligence Approach to the Power Dispatch Problem”,
International Journal of Computers Communications & Control, Vol. 5, No. 3, pp. 375–384, September 2010.
25. Jie Xing, Chen Chen and Peng Wu, “Optimal Active Power Dispatch with Small-signal Stability Constraints”, Electric
Power Components and Systems, Vol. 38, No. 9, pp. 1097–1110, 2010.
26. A.Y. Abdelaziz, F.M. Mohammed, S.F. Mekhamer and M.A.L. Badr, “Distribution Systems Reconfiguration using a
modified particle swarm optimization algorithm”, Electric Power Systems Research, Vol. 79, No. 11, pp. 1521–1530,
November 2009.
• Oliver Schütze, Marco Laumanns, Carlos A. Coello Coello, Michael Dellnitz and El-ghazali Talbi, “Convergence of Stochastic Search Algorithms to Finite Size Pareto Set Approximations”, Journal of Global
Optimization, Vol. 41, No. 4, pp. 559–577, August 2008.
1. Joseph Y.J. Chow and Amelia C. Regan, “A surrogate-based multiobjective metaheuristic and network degradation
simulation model for robust toll pricing”, Optimization and Engineering, Vol. 15, No. 1, pp. 137–165, March 2014.
2. Mengqi Hu, Jeffery D. Weir and Teresa Wu, “An augmented multi-objective particle swarm optimizer for building cluster
operation decisions”, Applied Soft Computing, Vol. 25, pp. 347–359, December 2014.
3. Yu Chen and Xiufen Zou, “Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts”, Information Sciences, Vol. 262, pp. 62–77, March 20, 2014.
4. Rudolf Berghammer, Tobias Friedrich and Frank Neumann, “Convergence of set-based multi-objective optimization,
indicators and deteriorative cycles”, Theoretical Computer Science, Vol. 456, pp. 2–17, October 19, 2012.
79
5. Douglas A.G. Vieira, Ricardo H.C. Takahashi and Rodney R. Saldanha, “Multicriteria optimization with a multiobjective
golden section line search”, Mathematical Programming, Vol. 131, Nos. 1-2, pp. 131–161, February 2012.
6. Yu Chen, Xiufen Zou and Weicheng Xie, “Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front”, Information Sciences, Vol. 181, No. 16, pp. 3336–3355, August 15,
2011.
7. Z. Tang, J. Periaux, G. Bugeda and E. Onate, “Lift maximization with uncertainties for the optimization of high-lift
devices”, International Journal for Numerical Methods in Fluids, Vol. 64, No. 2, pp. 119–135, September 20, 2010.
• Leticia C. Cagnina, Susana C. Esquivel and Carlos A. Coello Coello, “Solving Engineering Optimization
Problems with the Simple Constrained Particle Swarm Optimizer”, Informatica, Vol. 32, pp. 319–326, 2008.
1. Hamid Salimi, “Stochastic Fractal Search: A powerful metaheuristic algorithm”, Knowledge-based Systems, Vol. 75, pp.
1–18, February 2015.
2. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics inspired optimization
(OIO)”, Computer-Aided Design, Vol. 63, pp. 52–71, June 2015.
3. Selim Yilmaz and Ecir U. Kucuksille, “A new modification approach on bat algorithm for solving optimization problems”,
Applied Soft Computing, Vol. 28, pp. 259–275, March 2015.
4. Zhenzhou Hu, Xinye Cai and Zhun Fan, “An improved memetic algorithm using ring neighborhood topology for constrained optimization”, Soft Computing, Vol. 18, No. 10, pp. 2023–2041, October 2014.
5. Xin-She Yang, Mehmet Karamanoglu and Xingshi He, “Flower pollination algorithm: A novel approach for multiobjective
optimization”, Engineering Optimization, Vol. 46, No. 9, pp. 1222–1237, September 2, 2014.
6. Xinye Cai, Zhenzhou Hu and Zhun Fan, “A novel memetic algorithm based on invasive weed optimization and differential
evolution for constrained optimization”, Soft Computing, Vol. 17, No. 10, pp. 1893–1910, October 2013.
7. Xin-She Yang, Suash Deb, Martin Loomes and Mehmet Karamanoglu, “A framework for self-tuning optimization algorithm”, Neural Computing & Applications, Vol. 23, Nos. 7-8, pp. 2051–2057, December 2013.
8. Harish Garg, “Solving Structural Engineering Design Optimization Problems using an Artificial Bee Colony Algorithm”,
Journal of Industrial and Management Optimization, Vol. 10, No. 3, pp. 777–794, July 2014.
9. Erik Cuevas and Miguel Cienfuegos, “A new algorithm inspired in the behavior of the social-spider for constrained
optimization”, Expert Systems with Applications, Vol. 41, No. 2, pp. 412–425, February 1, 2014.
10. Xin-She Yang, “Firefly algorithm, stochastic test functions and design optimisation”, International Journal of Bioinspired Computation, Vol. 2, No. 2, pp. 78–84, 2010.
11. Xin-She Yang and Suash Deb, “Multiobjective cuckoo search for design optimization”, Computers & Operations Research,
Vol. 40, No. 6, pp. 1616–1624, June 2013.
12. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
13. Xin-She Yang, “Multiobjective firefly algorithm for continuous optimization”, Engineering with Computers, Vol. 29, No.
2, pp. 175–184, April 2013.
14. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Cuckoo search algorithm: a metaheuristic approach to
solve structural optimization problems”, Engineering with Computers, Vol. 29, No. 1, pp. 17–35, January 2013.
15. Vivek Kumar Mehta and Bhaskar Dasgupta, “A constrained optimization algorithm based on the simplex search
method”, Engineering Optimization, Vol. 44, No. 5, pp. 537–550, 2012.
16. Ahmad Mozaffari, Mofid Gorji-Bandpy and Tahereh B. Gorji, “Optimal design of constraint engineering systems: application of mutable smart bee algorithm”, International Journal of Bio-Inspired Computation, Vol. 4, No. 3, pp. 167–180,
2012.
17. Xin-She Yang and Suash Deb, “Two-stage eagle strategy with differential evolution”, International Journal of BioInspired Computation, Vol. 4, No. 1, pp. 1–5, 2012.
18. Adil Baykasoglu, “Design optimization with chaos embedded great deluge algorithm”, Applied Soft Computing, Vol. 12,
No. 3, pp. 1055–1067, March 2012.
19. Musrrat Ali, Millie Pant, Ajith Abraham and Chang Wook Ahn, “Swarm Directions Embedded Differential Evolution
for Faster Convergence of Global Optimization Problems”, International Journal on Artificial Intelligence Tools, Vol.
21, No. 3, Article Number: 1240013, June 2012.
20. S. Talatahari, A. Kaveh and R. Sheikholeslami, “Engineering design optimization using chaotic enhanced charged system
search algorithms”, Acta Mechanica, Vol. 223, No. 10, pp. 2269–2285, October 2012.
21. Sanghoun Oh, Chang Wook Ahn and Moongu Jeon, “Effective Constraints Based Evolutionary Algorithm for Constrained Optimization Problems”, International Journal of Innovative Computing Information and Control, Vol. 8, No.
6, pp. 3997–4014, June 2012.
80
22. Ali Husseinzadeh Kashan, “An efficient algorithm for constrained global optimization and application to mechanical
engineering design: League championship algorithm (LCA)”, Computer-Aided Design, Vol. 43, No. 12, pp. 1769–1792,
December 2011.
23. Giordano Tomassetti, “A cost-effective algorithm for the solution of engineering problems with particle swarm optimization”, Engineering Optimization, Vol. 42, No. 5, pp. 471–495, 2010.
• Victoria S. Aragón, Susana C. Esquivel and Carlos A. Coello Coello, “Artificial Immune System for Solving
Constrained Optimization Problems”, Revista Iberoamericana de Inteligencia Artificial, Vol. 11, No. 35, pp.
55–66, 2007.
1. Weiwei Zhang, Gary G. Yen and Zhongshi He, “Constrained Optimization via Artificial Immune System”, IEEE Transactions on Cybernetics, Vol. 44, No. 2, pp. 185–198, February 2014.
2. Hong He, Feng Qian and Wenli Du, “A chaotic immune algorithm with fuzzy adaptive parameters”, Asia-Pacific Journal
of Chemical Engineering, Vol. 3, No. 6, pp. 695–705, November-December 2008.
3. Jianyong Chen, Qiuzhen Lin and LinLin Shen, “An Immune-Inspired Evolution Strategy for Constrained Optimization
Problems”, International Journal on Artificial Intelligence Tools, Vol. 20, No. 3, pp. 549–561, June 2011.
4. Qiaoling Wang, Xiao-Zhi Gao and Changhong Wang, “An Adaptive Bacterial Foraging Algorithm for Constrained
Optimization”, International Journal of Innovative Computing Information and Control, Vol. 6, No. 8, pp. 3585–3593,
August 2010.
• Adriana Lara, Gustavo Sanchez, Carlos A. Coello Coello and Oliver Schütze, “HCS: A New Local Search
Strategy for Memetic Multi-Objective Evolutionary Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 1, pp. 112–132, February 2010.
1. Xiaoliang Ma, Fang Liu, Yutao Qi, Lingling Li, Licheng Jiao, Meiyun Liu and Jianshe Wu, “MOEA/D with Baldwinian
learning inspired by the regularity property of continuous multiobjective problem”, Neurocomputing, Vol. 145, pp.
336–352, December 5, 2014.
2. Aliasghar Arab and Alireza Alfi, “An adaptive gradient descent-based local search in memetic algorithm applied to
optimal controller design”, Information Sciences, Vol. 299, pp. 117–142, April 1, 2015.
3. Proteek Chandan Roy, Md. Monirul Islam, Kazuyuki Murase and Xin Yao, “Evolutionary Path Control Strategy for
Solving Many-Objective Optimization Problem”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 702–715, April
2015.
4. Gang Xu, Yu-qun Yang, Bin-Bin Liu, Yi-hong Xu and Ai-jun Wu, “An efficient hybrid multi-objective particle swarm
optimization with a multi-objective dichotomy line search”, Journal of Computational and Applied Mathematics, Vol.
280, pp. 310–326, May 15, 2015.
5. Bili Chen, Wenhua Zeng, Yangbin Lin and Defu Zhang, “A New Local Search-Based Multiobjective Optimization
Algorithm”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 50–73, February 2015.
6. Miqing Li, Shengxiang Yang, Ke Li and Xiaohui Liu, “Evolutionary Algorithms with Segment-Based Search for Multiobjective Optimization Problems”, IEEE Transactions on Cybernetics, Vol. 44, No. 8, pp. 1295–1313, August 2014.
7. Hyoungjin Kim and Meng-Sing Liou, “Adaptive directional local search strategy for hybrid evolutionary multiobjective
optimization”, Applied Soft Computing, Vol. 19, pp. 290–311, June 2014.
8. Zhi-Hui Zhan, Jingjing Li, Jiannong Cao, Jun Zhang, Henry Shu-Hung Chung and Yu-Hui Shi, “Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems”, IEEE
Transactions on Cybernetics, Vol. 43, No. 2, pp. 445–463, April 2013.
9. Honggang Wang, “Zigzag Search for Continuous Multiobjective Optimization”, INFORMS Journal on Computing, Vol.
25, No. 4, pp. 654–665, Fall 2013.
10. Yukun Bao, Zhongyi Hu and Tao Xiong, “A PSO and pattern search based memetic algorithm for SVMs parameters
optimization”, Neurocomputing, Vol. 117, pp. 98–106, October 6, 2013.
11. Hu Xia, Jian Zhuang and Dehong Yu, “Combining Crowding Estimation in Objective and Decision Space With Multiple
Selection and Search Strategies for Multi-Objective Evolutionary Optimization”, IEEE Transactions on Cybernetics,
Vol. 44, No. 3, pp. 378–393, March 2014.
12. D. Martin, A. Rosete, J. Alcala-Fdez and F. Herrera, “QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm
to mine quantitative association rules”, Information Sciences, Vol. 258, pp. 1–28, February 10, 2014.
13. Karthik Sindhya, Kaisa Miettinen and Kalyanmoy Deb, “A Hybrid Framework for Evolutionary Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 4, pp. 495–511, August 2013.
14. Federico Zuiani and Massimiliano Vasile, “Multi Agent Collaborative Search based on Tchebycheff decomposition”,
Computational Optimization and Applications, Vol. 56, No. 1, pp. 189–208, September 2013.
81
15. L.C. Jiao, Handing Wang, R.H. Shang and F. Liu, “A co-evolutionary multi-objective optimization algorithm based on
direction vectors”, Information Sciences, Vol. 228, pp. 90–112, April 10, 2013.
16. Yutao Qi, Fang Liu, Meiyun Liu, Maoguo Gong and Licheng Jiao, “Multi-objective immune algorithm with Baldwinian
learning”, Applied Soft Computing, Vol. 12, No. 8, pp. 2654–2674, August 2012.
17. Zhou Wu and Tommy W.S. Chow, “A local multiobjective optimization algorithm using neighborhood field”, Structural
and Multidisciplinary Optimization, Vol. 46, No. 6, pp. 853–870, December 2012.
18. Soumyadip Sengupta, Swagatam Das, Md Nasir, Athanasios V. Vasilakos and Witold Pedrycz, “An Evolutionary Multiobjective Sleep-Scheduling Scheme for Differentiated Coverage in Wireless Sensor Networks”, IEEE Transactions on
Systems, Man and Cybernetics Part C–Applications and Reviews, Vol. 42, No. 6, pp. 1093–1102, November 2012.
19. Kaiquan Cai, Jun Zhang, Chi Zhou, Xianbin Cao and Ke Tang, “Using computational intelligence for large scale air
route networks design”, Applied Soft Computing, Vol. 12, No. 9, pp. 2790–2800, September 2012.
20. Chunhua Peng, Huijuan Sun, Jianfeng Guo and Gang Liu, “Multi-objective optimal strategy for generating and bidding
in the power market”, Energy Conversion and Management, Vol. 57, pp. 13–22, May 2012.
21. Peter A. N. Bosman, “On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization”,
IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 51–69, February 2012.
22. M. Vasile and F. Zuiani, “Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design”, Proceedings of the Institution of Mechanical Engineers Part G–Journal of
Aerospace Engineering, Vol. 225, No. G11, pp. 1211–1227, November 2011.
23. Xianshun Chen, Yew-Soon Ong, Meng-Hiot Lim and Kay Chen Tan, “A Multi-Facet Survey on Memetic Computation”,
IEEE Transactions on Evolutionary Computation, Vol. 15, No. 5, pp. 591–607, October 2011.
24. Karthik Sindhya, Sauli Ruuska, Tomi Haanpää and Kaisa Miettinen, “A new hybrid mutation operator for multiobjective
optimization with differential evolution”, Soft Computing, Vol. 15, No. 10, pp. 2041–2055, October 2011.
25. Jun Huang, Xiaohong Huang, Yan Ma and Yanbing Liu, “High-dimensional objective optimizer: An evolutionary algorithm and its nonlinear analysis”, Expert Systems with Applications, Vol. 38, No. 7, pp. 8921–8928, July 2011.
26. Guofu Zhang, Jianguo Jiang, Zhaopin Su, Meibin Qi and Hua Fang, “Searching for overlapping coalitions in multiple
virtual organizations”, Information Sciences, Vol. 180, No. 17, pp. 3140–3156, September 1, 2010.
27. Nguyen Binh Ta Duong, Suiping Zhou, Wentong Cai, Xueyan Tang and Rassul Ayani, “Multi-objective zone mapping
in large-scale distributed virtual environments”, Journal of Network and Computer Applications, Vol. 34, No. 2, pp.
551–561, March 2011.
• Julián Molina, Luis V. Santana, Alfredo G. Hernández-Dı́az, Carlos A. Coello Coello and Rafael Caballero,
“g-dominance: Reference point based dominance for MultiObjective Metaheuristics”, European Journal of
Operational Research, Vol. 197, No. 2, pp. 685–692, September 2009.
1. A. Mahmodinejad and D. Foroutannia, “Piecewise equitable efficiency in multiobjective programming”, Operations
Research Letters, Vol. 42, No. 8, pp. 522–526, December 2014.
2. Lei Chen, Jiali Qiu, Guoyuan Wei and Zhenyao Shen, “A preference-based multi-objective model for the optimization
of best management practices”, Journal of Hydrology, Vol. 520, pp. 356–366, January 2015.
3. Javier Rubio-Loyola, Gregorio Toscano-Pulido, Marinos Charalambides, Marisol Magana-Aguilar, Joan Serrat-Fernandez,
George Pavlou and Hiram Galeana-Zapien, “Business-driven policy optimization for service management”, International
Journal of Network Management, Vol. 25, No. 2, pp. 113–140, March-April 2015.
4. Alan R.R. de Freitas, Peter J. Fleming and Federico G. Guimaraes, “Aggregation Trees for visualization and dimension
reduction in many-objective optimization”, Information Sciences, Vol. 298, pp. 288–314, March 20, 2015.
5. Ana Belen Ruiz, Ruben Saborido and Mariano Luque, “A preference-based evolutionary algorithm for multiobjective
optimization: the weighting achievement scalarizing function genetic algorithm”, Journal of Global Optimization, Vol.
62, No. 1, pp. 101–129, May 2015.
6. Rui Wang, Robin C. Purshouse, Ioannis Giagkiozis and Peter J. Fleming, “The iPICEA-g: a new hybrid evolutionary
multi-criteria decision making approach using the brushing technique”, European Journal of Operational Research, Vol.
243, No. 2, pp. 442–453, June 1, 2015.
7. Sanghamitra Bandyopadhyay, Rudrasis Chakraborty and Ujjwal Maulik, “Priority based epsilon dominance: A new
measure in multiobjective optimization”, Information Sciences, Vol. 305, pp. 97–109, June 1, 2015.
8. Ernestas Filatovas, Olga Kurasova and Karthik Sendhya, “Synchronous R-NSGA-II: An Extended Preference-Based
Evolutionary Algorithm for Multi-Objective Optimization”, Informatica, Vol. 26, No. 1, pp. 33–50, 2015.
9. Kenneth Sorensen and Johan Springael, “Progressive Multi-Objective Optimization”, International Journal of Information Technology & Decision Making, Vol. 13, No. 5, pp. 917–936, September 2014.
82
10. Laura Cruz, Eduardo Fernandez, Claudia Gomez, Gilberto Rivera and Fatima Perez, “Many-Objective Portfolio Optimization of Interdependent Projects with ’a priori’ Incorporation of Decision-Maker Preferences”, Applied Mathematics
& Information Sciences, Vol. 8, No. 4, pp. 1517–1531, July 2014.
11. Ruochen Liu, Chenlin Ma, Fei He, Wenping Ma and Licheng Jiao, “Reference direction based immune clone algorithm
for many-objective optimization”, Frontiers of Computer Science, Vol. 8, No. 4, pp. 642–655, August 2014.
12. Dunwei Gong, Xinfang Ji, Jing Sun and Xiaoyan Sun, “Interactive evolutionary algorithms with decision-maker’s preferences for solving interval multi-objective optimization problems”, Neurocomputing, Vol. 137, pp. 241–251, August 5,
2014.
13. D. Greiner, J.M. Emperador, B. Galvan, M. Mendez and G. Winter, “Engineering Knowledge-Based Variance-Reduction
Simulation and G-Dominance for Structural Frame Robust Optimization”, Advances in Mechanical Engineering, Article
Number: 680359, 2013.
14. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
15. Eunice Oliveira, Carlos Henggeler Antunes and Alvaro Gomes, “A comparative study of different approaches using an
outranking relation in a multi-objective evolutionary algorithm”, Computers & Operations Research, Vol. 40, No. 6, pp.
1602–1615, June 2013.
16. Ruochen Liu, Xiao Wang, Jing Liu, Lingfen Fang and Licheng Jiao, “A preference multi-objective optimization based
on adaptive rank clone and differential evolution”, Natural Computing, Vol. 12, No. 1, pp. 109–132, March 2013.
17. Carolina P. Almeida, Richard A. Goncalves, Elizabeth F. Goldbarg, Marco C. Goldbarg and Myriam R. Delgado, “An
experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem”, Annals of Operations
Research, Vol. 199, No. 1, pp. 305–341, October 2012.
18. P. Sebastian, Y. Ledoux, A. Collignan and J. Pailhes, “Linking objective and subjective modeling in engineering design
through arc-elastic dominance”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7743–7756, July 2012.
19. Arnaud Liefooghe, Laetitia Jourdan and El-Ghazali Talbi, “A software framework based on a conceptual unified model
for evolutionary multiobjective optimization: ParadisEO-MOEO”, European Journal of Operational Research, Vol. 209,
No. 2, pp. 104–112, March 1, 2011.
20. Jong-Hwan Kim, Ji-Hyeong Han, Ye-Hoon Kim, Seung-Hwan Choi and Eun-Soo Kim, “Preference-Based Solution
Selection Algorithm for Evolutionary Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation,
Vol. 16, No. 1, pp. 20–34, February 2012.
21. Joaquin Izquierdo, Idel Montalvo, Rafael Perez-Garcia and Agustin Matias, “On the Complexities of the Design of Water
Distribution Networks”, Mathematical Problems in Engineering, Vol. Article Number: 947961, 2012.
22. E. Zio and R. Bazzo, “A clustering procedure for reducing the number of representative solutions in the Pareto Front of
multiobjective optimization problems”, European Journal of Operational Research, Vol. 210, No. 3, pp. 624–634, May
1, 2011.
23. E. Zio and R. Bazzo, “Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation”,
Reliability Engineering & System Safety, Vol. 96, No. 5, pp. 569–580, May 2011.
24. Lamjed Ben Said, Slim Bechikh and Khaled Ghedira, “The r-Dominance: A New Dominance Relation for Interactive
Evolutionary Multicriteria Decision Making”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 5, pp.
801–818, October 2010.
25. John W. Fowler, Esma S. Gel, Murat M. Koksalan, Pekka Korhonen, Jon L. Marquis and Jyrki Wallenius, “Interactive evolutionary multi-objective optimization for quasi-concave preference functions”, European Journal of Operational
Research, Vol. 206, No. 2, pp. 417–425, October 16, 2010.
• Susana C. Esquivel and Carlos A. Coello Coello, “Hybrid Particle Swarm Optimizer for a Class of Dynamic
Fitness Landscape”, Engineering Optimization, Vol. 38, No. 8, pp. 873–888, December 2006.
1. Lili Liu, Dingwei Wang and Jiafu Tang, “Composite particle optimization with hyper-reflection scheme in dynamic
environments”, Applied Soft Computing, Vol. 11, No. 8, pp. 4626–4639, December 2011.
2. Carlos Cruz, Juan R. Gonzalez and David A. Pelta, “Optimization in dynamic environments: a survey on problems,
methods and measures”, Soft Computing, Vol. 15, No. 7, pp. 1427–1448, July 2011.
3. Lili Liu, Shengxiang Yang and Dingwei Wang, “Particle Swarm Optimization With Composite Particles in Dynamic
Environments”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 40, No. 6, pp. 1634–
1648, December 2010.
4. Liang Li, Guang-ming Yu, Zu-yu Chen and Xue-song Chu, “Discontinuous flying particle swarm optimization algorithm
and its application to slope stability analysis”, Journal of Central South University of Technology, Vol. 17, No. 4, pp.
852–856, August 2010.
83
5. Xindi Cai, Ganesh K. Venayagamoorthy and Donald C. Wunsch II, “Evolutionary swarm neural network game engine
for Capture Go”, Neural Networks, Vol. 23, No. 2, pp. 295–305, March 2010.
• Carlos A. Coello Coello and Alan D. Christiansen, “Multiobjective Optimization of Trusses using Genetic
Algorithms”, Computers and Structures, Vol. 75, No. 6, pp. 647–660, May 2000.
1. Mohammad Reza Neyshaburi, Hossein Bayat, Kourosh Mohammadi, Nader Nariman-Zadeh and Mahdi Irannejad, “Improvement in estimation of soil water retention using fractal parameters and multiobjective group method of data
handling”, Archives of Agronomy and Soil Science, Vol. 61, No. 2, pp. 257–273, February 1, 2015.
2. Mohammad Reza Ghasemi and Mohammad Farshchin, “Pareto-based optimum seismic design of steel frames”, Proceedings of the Institution of Civil Engineers–Structures and Buildings, Vol. 167, No. 1, pp. 66–74, January 2014.
3. Mir Majid Etghani, Mohammad Hassan Shojaeefard, Abolfazl Khalkhali and Mostafa Akbari, “A hybrid method of
modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel”, Applied
Thermal Engineering, Vol. 59, No. 1-2, pp. 309–315, September 25, 2013.
4. D. Greiner, J.M. Emperador, B. Galvan, M. Mendez and G. Winter, “Engineering Knowledge-Based Variance-Reduction
Simulation and G-Dominance for Structural Frame Robust Optimization”, Advances in Mechanical Engineering, Article
Number: 680359, 2013.
5. Rasoul Azizipanah-Abarghooee, Mohammad Rasoul Narimani, Bahman Bahmani-Firouzi and Taher Niknam, “ Modified
shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices”, Journal of Intelligent &
Fuzzy Systems, Vol. 26, No. 2, pp. 681–692, 2014.
6. N. Fallah and G. Zamiri, “Multi-objective optimal design of sliding base isolation using genetic algorithm”, Scientia
Iranica, Vol. 20, No. 1, pp. 87–96, February 2013.
7. Nayar Cuitlahuac Gutierrez Astudillo, Rebeca del Rocio Peniche Vera, Gilberto Herrera Ruiz, Roberto Alvarado Cardenas and Francisco J. Carrion Viramontes, “A long span bridge and a greenhouse roof truss structure optimized by means
of a consistent genetic algorithm with a natural crossover”, Engineering Computations, Vol. 30, No. 1, pp. 49–73, 2013.
8. A. Jamali, M. Ghamati, B. Ahmadi and N. Nariman-zadeh, “Probability of failure for uncertain control systems using neural networks and multi-objective uniform-diversity genetic algorithms (MUGA)”, Engineering Applications of
Artificial Intelligence, Vol. 26, No. 2, pp. 714–723, February 2013.
9. Mohammad Rasoul Narimani, Rasoul Azizipanah-Abarghooee, Behrouz Zoghdar-Moghadam-Shahrekohne and Kayvan
Gholami, “A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering
generator constraints and multi-fuel type”, Energy, Vol. 49, pp. 119–136, January 1, 2013.
10. M.J. Mahmoodabadi, A. Bagheri, N. Nariman-Zadeh, A. Jamali and R. Abedzadeh Maafi, “Pareto Design of Decoupled
Sliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm”, Journal of Applied
Mathematics, Article Number: 639014, 2012.
11. A. Shokuhi-Rad, A. Jamali, M. Naghashzadegan, N. Nariman-zadeh and A. Hajiloo, “Optimum Pareto design of nonlinear predictive control with multi-design variables for PEM fuel cell”, International Journal of Hydrogen Energy, Vol.
37, No. 15, pp. 11244–11254, August 2012.
12. Mohammad Hasan Shojaeefard, Reza Abdi Behnagh, Mostafa Akbari, Mohammad Kazem Besharati Givi and Foad
Farhani, “Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt
joints using neural network and particle swarm algorithm”, Materials & Design, Vol. 44, pp. 190–198, February 2013.
13. T. Niknam and H. Zeinoddini-Meymand, “Impact of Fuel Cell Power Plants on Multi-objective Optimal Operation
Management of Distribution Network”, Fuel Cells, Vol. 12, No. 3, pp. 487–505, June 2012.
14. Tino Stankovic, Mario Storga and Dorian Marjanovic, “Synthesis of Truss Structure Designs by NSGA-II and NodeSort
Algorithm”, Strojniski Vestnik–Journal of Mechanical Engineering, Vol. 58, No. 3, pp. 203–212, March 2012.
15. A. Kaveh and K. Laknejadi, “A Hybrid Multi-Objective Optimization and Decision Making Procedure for Optimal
Design of Truss Structures”, Iranian Journal of Science and Technology–Transactions of Civil Engineering, Vol. 35, No.
C2, pp. 137–154, August 2011.
16. Taher Niknam, Mohammad Rasoul Narimani, Masoud Jabbari and Admad Reza Malekpour, “A modified shuffle frog
leaping algorithm for multi-objective optimal power flow”, Energy, Vol. 36, No. 11, pp. 6420–6432, November 2011.
17. Tao Xu, Wenjie Zuo, Tianshuang Xu, Guangcai Song and Ruichuan Li, “An adaptive reanalysis method for genetic
algorithm with application to fast truss optimization ”, Acta Mechanica Sinica, Vol. 26, No. 2, pp. 225–234, May 2010.
18. Bahaa I. Kazem, “Multi-Objective Optimization for the Force System of Orthodontic Retraction Spring Using Genetic
Algorithms”, Journal of Medical Devices–Transactions of the ASME, Vol. 3, No. 4, Article Number: 041006, December
2009.
19. Antony W. Iorio and Xiaodong Li, “Improving the performance and scalability of Differential Evolution on problems
exhibiting parameter interactions”, Soft Computing, Vol. 15, No. 9, pp. 1769–1792, September 2011.
84
20. H. Safikhani, A. Khalkhali and M. Farajpoor, “Pareto Based Multi-Objective Optimization of Centrifugal Pumps using
CFD, Neural Networks and Genetic Algorithms”, Engineering Applications of Computational Fluid Mechanics, Vol. 5,
No. 1, pp. 37–48, March 2011.
21. Andrew Odjo, Normal E. Sammons, Jr., Wei Yuan, Antonio Marcilla, Mario R. Eden and Jose A. Caballero, “DisjunctiveGenetic Programming Approach to Synthesis of Process Networks”, Industrial & Engineering Chemistry Research, Vol.
50, No. 10, pp. 6213–6228, May 18, 2011.
22. Wenjie Zuo, Tao Xu, Hao Zhang and Tianshuang Xu, “Fast structural optimization with frequency constraints by genetic
algorithm using adaptive eigenvalue reanalysis methods”, Structural and Multidisciplinary Optimization, Vol. 43, No. 6,
pp. 799–810, June 2011.
23. Abolfazl Khalkhali, Mehdi Farajpoor and Hamed Safikhani, “Modeling and Multi-Objective Optimization of ForwardCurved Blade Centrifugal Fans using CFD and Neural Networks”, Transactions of the Canadian Society for Mechanical
Engineering, Vol. 35, No. 1, pp. 63–79, 2011.
24. Christopher S. Roper, “Multiobjective optimization for design of multifunctional sandwich panel heat pipes with microarchitected truss cores”, International Journal of Heat and Fluid Flow, Vol. 32, No. 1, pp. 239–248, February 2011.
25. H. Bayat, M.R. Neyshabouri, K. Mohammadi and N. Nariman-Zadeh, “Estimating Water Retention with Pedotransfer
Functions Using Multi-Objective Group Method of Data Handling and ANNs”, Pedosphere, Vol. 21, No. 1, pp. 107–114,
February 2011.
26. F. Noori, M. Gorji, A. Kazemi and H. Nemati, “Thermodynamic optimization of ideal turbojet with afterburner engines
using non-dominated sorting genetic algorithm II”, Proceedings of the Institution of Mechanical Engineers Part G–Journal
of Aerospace Engineering, Vol. 224, No. G12, pp. 1285–1296, December 2010.
27. A. Khakhali, Nader Nariman-zadeh, A. Darvizeh, A. Masoumi and B. Notghi, “Reliability-based robust multi-objective
crashworthiness optimisation of S-shaped box beams with parametric uncertainties”, International Journal of Crashworthiness, Vol. 15, No. 4, pp. 443–456, 2010.
28. K. Salmalian, N. Nariman-Zadeh, H. Gharababei, H. Haftchenari and A. Varvani-Farahani, “Multi-objective evolutionary
optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibrereinforced plastics composites”, Proceedings of the Institution of Mechanical Engineers Part L–Journal of MaterialsDesign and Applications, Vol. 224, No. L2, pp. 79–91, 2010.
29. N. Nariman-Zadeh, M. Salehpour, A. Jamali and E. Haghgoo, “Pareto optimization of a five-degree of freedom vehicle
vibration model using a multi-objective uniform-diversity genetic algorithm (MUGA)”, Engineering Applications of
Artificial Intelligence, Vol. 23, No. 4, pp. 543–551, June 2010.
30. Sanghamitra Bandyopadhyay, Sankar K. Pal and B. Aruna, “Multiobjective GAs, Quantitative Indices, and Pattern
Classification”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 34, No. 5, pp.
2088–2099, October 2004.
31. Guan-Chun Luh and Chung-Huei Chueh, “Multi-objective optimal design of truss structure with immune algorithm”,
Computers & Structures, Vol. 82, Nos. 11–12, pp. 829–844, May 2004.
32. P. Sivakumar, A. Rajaraman, G.M.S. Knight and D.S. Ramachandramurthy, “Object-oriented optimization approach
using genetic algorithms for lattice towers”, Journal of Computing in Civil Engineering, Vol. 18, No. 2, pp. 162–171,
April 2004.
33. E.M.R. Fairbairn, M.M. Silvoso, R.D. Toledo, J.L.D. Alves and N.F.F. Ebecken, “Optimization of mass concrete construction using genetic algorithms”, Computers & Structures, Vol. 82, Nos. 2–3, pp. 281–299, January 2004.
34. S.Y. Woon, Q.M. Querin and G.P. Steven, “On improving the GA step-wise shape optimization method through the
application of the Fixed Grid FEA paradigm”, Structural and Multidisciplinary Optimization, Vol. 25, No. 4, pp.
270–278, October 2003.
35. N. Ali, K. Behdinan and Z. Fawaz, “Applicability and viability of a GA based finite element analysis architecture for
structural design optimization”, Computers & Structures, Vol. 81, Nos. 22–23, pp. 2259–2271, September 2003.
36. M. Papadrakakis, N.D. Lagaros and V. Plevris, “Multi-objective optimization of skeletal structures under static and
seismic loading conditions”, Engineering Optimization, Vol. 34, No. 6, pp. 645–669, December 2002.
37. A. Nag, D.R. Mahapatra and S. Gopalakrishnan, “Identification of delamination in composite beams using spectral
estimation and a genetic algorithm”, Smart Materials & Structures, Vol. 11, No. 6, pp. 899–908, December 2002.
38. L. Blasi, L. Iuspa and G. Del Core, “Speed-sensitivity analysis by a genetic multiobjective optimization technique”,
Journal of Aircraft, Vol. 39, No. 6, pp. 1076–1079, November-December 2002.
39. V.S. Summanwar, V.K. Jayaraman, B.D. Kulkarni, H.S. Kusumakar, K. Gupta, and J. Rajesh, “Solution of constrained
optimization problems by multi-objective genetic algorithm”, Computers and Chemical Engineering, Vol. 26, No. 10,
pp. 1481–1492, October 15, 2002.
85
40. S. Ranji Ranjithan, S. Kishan Chetan and Harish K. Dakshina, “Constraint Method-Based Evolutionary Algorithm
(CMEA) for Multiobjective Optimization”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello
& David Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag,
Zurich, Suiza, pp. 299–313, Marzo de 2001.
41. Ignacio Paya, Victor Yepes, Fernando Gonzalez-Vidosa and Antonio Hospitaler, “Multiobjective optimization of concrete
frames by simulated annealing”, Computer-Aided Civil and Infrastructure Engineering, Vol. 23, No. 8, pp. 596–610,
November 2008.
42. A. Kaveh and M. Shahrouzi, “Optimal structural design family by genetic search and ant colony approach”, Engineering
Computations, Vol. 25, Nos. 3–4, pp. 268–288, 2008.
43. Vedat Togan and Ayse T. Daloglu, “An improved genetic algorithm with initial population strategy and self-adaptive
member grouping”, Computers & Structures, Vol. 86, Nos. 11–12, pp. 1204–1218, June 2008.
44. S. Pourzeynali and M. Zarif, “Multi-objective optimization of seismically isolated high-rise building structures using
genetic algorithms”, Journal of Sound and Vibration, Vol. 311, Nos. 3–5, pp. 1141–1160, April 8, 2008.
45. N. Amanifard, N. Nariman-Zadeh, M. Borji, A. Khalkhali and A. Habibdoust, “Modelling and Pareto optimization of
heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms”, Energy
Conversion and Management, Vol. 49, No. 2, pp. 311–325, February 2008.
46. N. Nariman-zadeh, A. Jamali and A. Hajiloo, “Frequency-based reliability Pareto optimum design of proportionalintegral-derivative controllers for systems with probabilistic uncertainty”, Proceedings of the Institution of Mechanical
Engineers Part I–Journal of Systems and Control Engineering, Vol. 221, No. I8, pp. 1061–1075, December 2007.
47. X. K. Zou, C.M. Chan, G. Li and Q. Wang, “Multiobjective optimization for performance-based design of reinforced
concrete frames”, Journal of Structural Engineering–ASCE, Vol. 133, No. 10, pp. 1462–1474, October 2007.
48. Samya Elaoud, Taicir Loukil and Jacques Teghem, “The Pareto fitness genetic algorithm: Test function study”, European
Journal of Operational Research, Vol. 177, No. 3, pp. 1703–1719, March 16, 2007.
49. C.J.K. Lee, T. Furukawa and S. Yoshimura, “A human-like numerical technique for design of engineering systems”,
International Journal for Numerical Methods in Engineering, Vol. 64, No. 14, pp. 1915–1943, December 14, 2005.
50. K. Atashkari, N. Nariman-Zadeh, A. Pilechi, A. Jamali and X. Yao, “Thermodynamic Pareto optimization of turbojet
engines using multi-objective genetic algorithms”, International Journal of Thermal Sciences, Vol. 44, No. 11, pp.
1061–1071, November 2005.
51. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
52. N. Nariman-Zadeh, K. Atashkari, A. Jamali, A. Pilechi and X. Yao, “Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms”, Engineering
Optimization, Vol. 37, No. 5, pp. 437–462, July 2005.
53. J. Martin, C. Bielza and D.R. Insua, “Approximating nondominated sets in continuous multiobjective optimization
problems”, Naval Research Logistics, Vol. 52, No. 5, pp. 469–480, August 2005.
54. David Greiner, Gabriel Winter, José M. Emperador and Blas Galván, “Gray Coding in Evolutionary Multicriteria
Optimization: Application in Frame Structural Optimum Design”, in Carlos A. Coello Coello, Arturo Hernández Aguirre
and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005,
pp. 576–591, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
55. M. Ali-Tavoli, N. Nariman-Zadeh, A. Khakhali and M. Mehran, “Multi-objective optimization of abrasive flow machining
processes using polynomial neural networks and genetic algorithms”, Machining Science and Technology, Vol. 10, No.
4, pp. 491–510, October-December 2006.
56. S.F. Hwang and R.S. He, “Engineering optimization using a real-parameter genetic-algorithm-based hybrid method”,
Engineering Optimization, Vol. 38, No. 7, pp. 833–852, October 2006.
57. H.W. Chen and N.B. Chang, “Decision support for allocation of watershed pollution load using grey fuzzy multiobjective
programming”, Journal of the American Water Resources Association, Vol. 42, No. 3, pp. 725–745, June 2006.
58. H.Z. Huang, Y.K. Gu and X.P. Du, “An interactive fuzzy multi-objective optimization method for engineering design”,
Engineering Applications of Artificial Intelligence, Vol. 19, No. 5, pp. 451–460, August 2006.
59. N. Nariman-Zadeh, A. Darvizeh and A. Jamali, “Pareto optimization of energy absorption of square aluminium columns
using multi-objective genetic algorithms”, Proceedings of the Institution of Mechanical Engineers Part B–Journal of
Engineering Manufacture, Vol. 220, No. 2, pp. 213–224, February 2006.
60. P.A. Makris, C.G. Provatidis and D.T. Venetsanos, “Structural optimization of thin-walled tubular trusses using a virtual
strain energy density approach”, Thin-Walled Structures, Vol. 44, No. 2, pp. 235–246, February 2006.
61. P. Agarwal and A.M. Raich, “Design and optimization of steel trusses using genetic algorithms, parallel computing, and
human-computer interaction”, Structural Engineering and Mechanics, Vol. 23, No. 4, pp. 325–337, July 10, 2006.
86
62. K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali and A. Jamali, “Modelling and multi-objective optimization
of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms”, Energy
Conversion and Management, Vol. 48, No. 3, pp. 1029–1041, March 2007.
63. K. Atashkari, N. Nariman-Zadeh, A. Pilechi, A. Jamali and X. Yao, “Thermodynamic Pareto optimization of turbojet
engines using multi-objective genetic algorithms”, International Journal of Thermal Sciences, Vol. 44, No. 11, pp.
1061–1071, November 2005.
64. Quan Yuan and Feng Qian, “A hybrid genetic algorithm for twice continuously differentiable NLP problems”, Computers
& Chemical Engineering, Vol. 34, No. 1, pp. 36–41, January 11, 2010.
65. A. Jamali, A. Hajiloo and N. Nariman-zadeh, “Reliability-based robust Pareto design of linear state feedback controllers
using a multi-objective uniform-diversity genetic algorithm (MUGA)”, Expert Systems with Applications, Vol. 37, No.
1, pp. 401–413, January 2010.
66. M. Pouraghaie, K. Atashkari, S.M. Besarati and N. Nariman-Zadeh, “Thermodynamic performance optimization of a
combined power/cooling cycle”, Energy Conversion and Management, Vol. 51, No. 1, pp. 204–211, January 2010.
67. A. Jamali, N. Nariman-zadeh, A. Darvizeh, A. Masoumi and S. Hamrang, “Multi-objective evolutionary optimization
of polynomial neural networks for modelling and prediction of explosive cutting process”, Engineering Applications of
Artificial Intelligence, Vol. 22, Nos. 4-5, pp. 676–687, June 2009.
68. L.V.R. Arruda, M.C.S. Swiech, M.R.B. Delgado and F. Neves, Jr., “PID control of MIMO process based on rank niching
genetic algorithm”, Applied Intelligence, Vol. 29, No. 3, pp. 290–305, December 2008.
69. Luca Lanzi, Alessandro Airoldi and Clive Chirwa, “Application of an iterative global approximation technique to structural optimizations”, Optimization and Engineering, Vol. 10, No. 1, pp. 109–132, March 2009.
• Carlos A. Coello Coello, “Constraint-handling using an evolutionary multiobjective optimization technique”,
Civil Engineering and Environmental Systems, Vol. 17, pp. 319–346, 2000.
1. Seyedali Mirjalili and Andrew Lewis, “Adaptive gbest-guided gravitational search algorithm”, Neural Computing &
Applications, Vol. 25, Nos. 7-8, December 2014.
2. Xiangtong Kong, Haibin Ouyang and Xiaoxue Piao, “A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization”, Soft Computing, Vol. 17, No. 12, pp. 2293–2309, December 2013.
3. Syeda Darakhshan Jabeen, “Split and Discard Strategy: A New Approach for Constrained Global Optimization”,
International Journal of Artificial Intelligence Tools, Vol. 22, No. 4, Article Number: 1350023, August 2013.
4. Santosh Mungle, Lyes Benyoucef, Young-Jun Son and M.K. Tiwari, “A fuzzy clustering-based genetic algorithm approach
for time-cost-quality trade-off problems: A case study of highway construction project”, Engineering Applications of
Artificial Intelligence, Vol. 26, No. 8, pp. 1953–1966, September 2013.
5. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
6. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
7. Sylvain Koos, Jean-Baptiste Mouret and Stephane Doncieux, “The Transferability Approach: Crossing the Reality Gap
in Evolutionary Robotics”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 122–145, February
2013.
8. Guanbo Jia, Yong Wang, Zixing Cai and Yaochu Jin, “An improved (µ + λ)-constrained differential evolution for
constrained optimization”, Information Sciences, Vol. 222, pp. 302–322, February 10, 2013.
9. Jazmin Cortez-Gonzalez, Juan Gabriel Segovia-Hernandez, Salvador Hernandez, Claudia Gutierrez-Antonio, Abel BrionesRamirez and Ben-Guang Rong, “Optimal design of distillation systems with less than N-1 columns for a class of four
component mixtures”, Chemical Engineering Research & Design, Vol. 90, No. 10, pp. 1425–1447, October 2012.
10. Rafael S. Parpinelli, Fabio R. Teodoro, Heitor S. Lopes, “A comparison of swarm intelligence algorithms for structural
engineering optimization”, International Journal for Numerical Methods in Engineering, Vol. 91, No. 6, pp. 666–684,
August 10, 2012.
11. A. Kaveh and M. Ahangaran, “Social Harmony Search Algorithm for Continuous Optimization”, Iranian Journal of
Science and Technology-Transactions of Civil Engineering, Vol. 36, No. C2, pp. 121–137, August 2012.
12. Xiao-Zhi Gao, Xiaolei Wang, Tapani Jokinen, Seppo Jari Ovaska, Antero Arkkio and Kai Zenger, “A Hybrid Optimization
Method for Wind Generator Design”, International Journal of Innovative Computing Information and Control, Vol. 8,
No. 6, pp. 4347–4373, June 2012.
13. Ali Riza Yildiz, “Hybrid Taguchi-Harmony Search Algorithm for Solving Engineering Optimization Problems”, International Journal of Industrial Engineering Theory, Applications and Practice, Vol. 15, No. 3, pp. 286–293, 2008.
87
14. Yannick Rousseau, Igor Men’shov and Yoshiaki Nakamura, “Morphing-based shape optimization in computational fluid
dynamics”, Transactions of the Japan Society for Aeronautical and Space Sciences, Vol. 50, No. 167, pp. 41–47, May
2007.
15. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Cuckoo search algorithm: a metaheuristic approach to
solve structural optimization problems”, Engineering with Computers, Vol. 29, No. 1, pp. 17–35, January 2013.
16. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
17. Vivek Kumar Mehta and Bhaskar Dasgupta, “A constrained optimization algorithm based on the simplex search
method”, Engineering Optimization, Vol. 44, No. 5, pp. 537–550, 2012.
18. Xiangtao Hu, Yong’an Huang, Zhouping Yin and Youlun Xiong, “Optimization-based model of tunneling-induced distributed loads acting on the shield periphery”, Automation in Construction, Vol. 24, pp. 138–148, July 2012.
19. L. Song, C. Luo, J. Li and Z. Feng, “Automated multi-objective and multidisciplinary design optimization of a transonic
turbine stage”, Proceedings of the Institution of Mechanical Engineers Part A–Journal of Power and Energy, Vol. 226,
No. A2, pp. 262–276, 2012.
20. Abu S.S.M. Barkat Ullah, Ruhul Sarker and Chris Lokan, “Handling equality constraints in evolutionary optimization”,
European Journal of Operational Research, Vol. 221, No. 3, pp. 480–490, September 16, 2012.
21. Fernando Israel Gomez-Castro, Mario Alberto Rodriguez-Angeles, Juan Gabriel Segovia-Hernandez, Claudia GutierrezAntonio and Abel Briones-Ramirez, “Optimal Designs of Multiple Dividing Wall Columns”, Chemical Engineering &
Technology, Vol. 34, No. 12, pp. 2051–2058, December 2011.
22. Yong Wang and Zixing Cai, “Combining Multiobjective Optimization with Differential Evolution to Solve Constrained
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 117–134, February
2012.
23. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Mixed variable structural optimization using Firefly
Algorithm”, Computers & Structures, Vol. 89, Nos. 23-24, pp. 2325–2336, December 2011.
24. Xiang Li and Gang Du, “Inequality constraint handling in genetic algorithms using a boundary simulation method”,
Computers & Operations Research, Vol. 39, No. 3, pp. 521–540, March 2012.
25. Gideon Avigad and Erella Eisenstadt Matalon, “The multi-single-objective problem and its solution by way of evolutionary algorithms”, Research in Engineering Design, Vol. 22, No. 2, pp. 87–102, April 2011.
26. Erick Yair Miranda-Galindo, Juan Gabriel Segovia-Hernandez, Salvador Hernandez, Claudia Gutierrez-Antonio and
Abel Briones-Ramirez, “Reactive Thermally Coupled Distillation Sequences: Pareto Front”, Industrial & Engineering
Chemistry Research, Vol. 50, No. 2, pp. 926–938, January 19, 2011.
27. Dilip Datta and Jose Rui Figueira, “A real-integer-discrete-coded particle swarm optimization for design problems”,
Applied Soft Computing, Vol. 11, No. 4, pp. 3625–3633, June 2011.
28. Dexuan Zou, Haikuan Liu, Liqun Gao and Steven Li, “A novel modified differential evolution algorithm for constrained
optimization problems”, Computers & Mathematics with Applications, Vol. 61, No. 6, pp. 1608–1623, March 2011.
29. Dexuan Zou, Haikuan Liu, Liqun Gao and Steven Li, “Directed searching optimization algorithm for constrained optimization problems”, Expert Systems with Applications, Vol. 38, No. 7, pp. 8716–8723, July 2011.
30. Claudia Guterrez-Antonio, Abel Briones-Ramirez and Arturo Jimenez-Gutierrez, “Optimization of Petlyuk sequences
using a multi objective genetic algorithm with constraints”, Computers & Chemical Engineering, Vol. 35, No. 2, pp.
236–244, February 9, 2011.
31. Xiao-Zhi Gao, Xiaolei Wang, Seppo Jari Ovaska and He Xu, “A Modified Harmony Search Method in Constrained
Optimization”, International Journal of Innovative Computing Information and Control, Vol. 6, No. 9, pp. 4235–4247,
September 2010.
32. Fernando I. Gomez-Castro, Juan Gabriel Segovia-Hernandez, Salvador Hernandez, Claudia Gutierrez-Antonio and Abel
Briones-Ramirez, “Dividing wall distillation columns: Optimization and control properties”, Chemical Engineering &
Technology, Vol. 31, No. 9, pp. 1246–1260, September 2008.
33. Jesús Garcı́a Herrero, Antonio Berlanga and José Manuel Molina López, “Effective Evolutionary Algorithms for ManySpecifications Attainment: Application to Air Traffic Control Tracking Filters”, IEEE Transactions on Evolutionary
Computation, Vol. 13, No. 1, pp. 151–168, February 2009.
34. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
35. Shubham Agrawal, B.K. Panigrahi and Manoj Kumar Tiwari, “Multiobjective Particle Swarm Algorithm with Fuzzy
Clustering for Electrical Power Dispatch”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 5, pp.
529–541, October 2008.
88
36. M. Mahdavi, M. Haghir Chehreghani, H. Abolhassani and R. Forsati, “Novel meta-heuristic algorithms for clustering
web documents”, Applied Mathematics and Computation, Vol. 201, Nos. 1–2, pp. 441–451, July 15, 2008.
37. M. Fesanghary, M. Mahdavi, M. Minary-Jolandan and Y. Alizadeh, “Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems”, Computer Methods in Applied Mechanics and
Engineering, Vol. 197, Nos. 33–40, pp. 3080–3091, 2008.
38. Xunxue Cui, Qin Li and Qing Tao, “Genetic algorithm for pareto optimum-based route selection”, Journal of Systems
Engineering and Electronics, Vol. 18, No. 2, pp. 360–368, June 2007.
39. Simone Puzzi and Alberto Carpinteri, “A double-multiplicative dynamic penalty approach for constrained evolutionary
optimization”, Structural and Multidisciplinary Optimization, Vol. 35, No. 5, pp. 431–445, May 2008.
40. Yong Wang, Zixing Cai, Yuren Zhou and Wei Zeng, “An Adaptive Tradeoff Model for Constrained Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 80–92, February 2008.
41. Yong Wang, Hui Liu, Zixing Cai and Yuren Zhou, “An orthogonal design based constrained evolutionary optimization
algorithm”, Engineering Optimization, Vol. 39, No. 6, pp. 715–736, September 2007.
42. Pei Yee Ho and Kazuyuki Shimizu, “Evolutionary constrained optimization using an addition of ranking method and a
percentage-based tolerance value adjustment scheme”, Information Sciences, Vol. 177, No. 14, pp. 2985–3004, July 15,
2007.
43. M. Mahdavi, M. Fesanghary and E. Damangir, “An improved harmony search algorithm for solving optimization problems”, Applied Mathematics and Computation, Vol. 188, No. 2, pp. 1567–1579, May 15, 2007.
44. Akira Oyama, Koji Shimoyama and Kozo Fujii, “New constraint-handling method for multi-objective and multiconstraint evolutionary optimization”, Transactions of the Japan Society for Aeronautical and Space Sciences, Vol.
50, No. 167, pp. 56–62, May 2007.
45. Yong Wang, Zixing Cai, Guanqi Guo and Yuren Zhou, “Multiobjective optimization and hybrid evolutionary algorithm
to solve constrained optimization problems”, IEEE Transactions on Systems, Man and Cybernetics Part B–Cybernetics,
Vol. 37, No. 3, pp. 560–575, June 2007.
46. Sanghamitra Bandyopadhyay, Sankar K. Pal and B. Aruna, “Multiobjective GAs, Quantitative Indices, and Pattern
Classification”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 34, No. 5, pp.
2088–2099, October 2004.
47. Lauren M. Clevenger and William E. Hart, “Convergence Examples of a Filter-Based Evolutionary Algorithm”, in
Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic
and Evolutionary Computation Conference. Part I, Springer-Verlag, Lecture Notes in Computer Science Vol. 3102, pp.
666–677, Seattle, Washington, USA, June 2004.
48. C.X. Yang, L.G. Tham, X. T. Feng, Y.J. Wang and P.K.K. Lee, “Two-stepped evolutionary algorithm and its application
to stability analysis of slopes”, Journal of Computing in Civil Engineering, Vol. 18, No. 2, pp. 145–153, April 2004.
49. J.E. Hurtado, “Reanalysis of linear and nonlinear structures using iterated Shanks transformation”, Computer Methods
in Applied Mechanics and Engineering, Vol. 191, Nos. 37–38, 2002.
50. Yuping Wang, Dalian Liu, and Yiu-Ming Cheung, “Preference Bi-objective Evolutionary Algorithm for Constrained
Optimization”, in Yue Hao et al. (editors), Computational Intelligence and Security. International Conference, CIS
2005, pp. 184–191, Springer, Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December 2005.
51. Lauren Clevenger, Lauren Ferguson and William E. Hart, “Filter-Based Evolutionary Algorithm for Constrained Optimization”, Evolutionary Computation, Vol. 13, No. 3, pp. 329–352, Fall 2005.
52. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
53. Bo Liao and Rein Luus, “Comparison of the Luus-Jaakola optimization procedure and the genetic algorithm”, Engineering Optimization, Vol. 37, No. 4, pp. 381–398, June 2005.
54. Tetsuyuki Takahama, Setsuko Sakai and Noriyuki Iwane, “Constrained optimization by the constrained hybrid algorithm of particle swarm optimization and genetic algorithm”, in S. Zhang and R. Jarvis (editors), AI 2005: Advances
in Artificial Intelligence, Springer-Verlag, pp. 389–400, Lecture Notes in Artificial Intelligence Vol. 3809, 2005.
55. Kathrin Klamroth and Jorgen Tind, “Constrained optimization using multiple objective programming”, Journal of
Global Optimization, Vol. 37, No. 3, pp. 325–355, March 2007.
56. Zhuhong Zhang, “Immune optimization algorithm for constrained nonlinear multiobjective optimization problems”,
Applied Soft Computing, Vol. 7, No. 3, pp. 840–857, June 2007.
57. Zixing Cai and Yong Wang, “A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 658–675, December 2006.
58. George G. Dimopoulos, “Mixed-variable engineering optimization based on evolutionary and social metaphors”, Computer Methods in Applied Mechanics and Engineering, Vol. 196, Nos. 4–6, pp. 803–817, 2007.
89
59. Jingxuan Wei and Yuping Wang, “A Novel Multi-objective PSO Algorithm for Constrained Optimization Problems”, in
T.-D. Wang et al. (editors), Simulated Evolution and Learning (SEAL 2006), pp. 174–180, Springer, Lecture Notes in
Computer Science Vol. 4247, 2006.
60. Min Gan, Hui Peng, Xiaoyan Peng, Xiaohong Chen and Garba Inoussa, “An adaptive decision maker for constrained
evolutionary optimization”, Applied Mathematics and Computation, Vol. 215, No. 12, pp. 4172–4184, February 15,
2010.
61. A. Rama Mohan Rao and P.P. Shyju, “A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of Hybrid
Laminate Composite Structures”, Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 3, pp. 149–170,
April 2010.
62. Jose Antonio Vazquez-Castillo, Josue Addiel Venegas-Sanchez, Juan Gabriel Segovia-Hernandez, Hector HernandezEscoto, Salvador Hernandez, Claudia Gutierrez-Antonio and Abel Briones-Ramirez, “Design and optimization, using
genetic algorithms, of intensified distillation systems for a class of quaternary mixtures”, Computers & Chemical Engineering, Vol. 33, No. 11, pp. 1841–1850, November 12, 2009.
63. Quan Yuan and Feng Qian, “A hybrid genetic algorithm for twice continuously differentiable NLP problems”, Computers
& Chemical Engineering, Vol. 34, No. 1, pp. 36–41, January 11, 2010.
64. I.J. Dotu, J. Garcia, A. Berlanga and J.M. Molina, “A meta-level evolutionary strategy for many-criteria design: Application to improving tracking filters”, Advanced Engineering Informatics, Vol. 23, No. 3, pp. 243–252, July 2009.
65. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique for
constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,
pp. 1501–1534, March 2009.
66. Mehrdad Mahdavi and Hassan Abolhassani, “Harmony K-means algorithm for document clustering”, Data Mining and
Knowledge Discovery, Vol. 18, No. 3, pp. 370–391, June 2009.
67. Abu S. S. M. Barkat Ullah, Ruhul Sarker, David Cornforth and Chris Lokan, “AMA: a new approach for solving
constrained real-valued optimization problems”, Soft Computing, Vol. 13, Nos. 8-9, pp. 741–762, July 2009.
68. Claudia Gutierrez-Antonio and Abel Briones-Ramirez, “Pareto front of ideal Petlyuk sequences using a multiobjective
genetic algorithm with constraints”, Computers & Chemical Engineering, Vol. 33, No. 2, pp. 454–464, February 23,
2009.
• Mario Villalobos-Arias, Carlos A. Coello Coello, Onésimo Hernández-Lerma, “Asymptotic Convergence of
a Simulated Annealing Algorithm for Multiobjective Optimization Problems”, Mathematical Methods of
Operations Research, Vol. 64, No. 2, pp. 353–362, October 2006.
1. A.J. Zaslavski, “Existence of Solutions of a Vector Optimization Problem with a Generic Lower Semicontinuous Objective
Function”, Journal of Optimization Theory and Applications, Vol. 141, No. 1, pp. 217–230, April 2009.
• Carlos A. Coello Coello, “An Updated Survey of GA-Based Multiobjective Optimization Techniques”, ACM
Computing Surveys, Vol. 32, No. 2, pp. 109–143, June 2000.
1. Ali Sadollah, Hadi Eskandar and Joong Hoon Kim, “Water cycle algorithm for solving constrained multi-objective
optimization problems”, Applied Soft Computing, Vol. 27, pp. 279–298, February 2015.
2. Bhupendra Kumar Pathak and Sanjay Srivastava, “Integrated Fuzzy-HMH for project uncertainties in time-cost tradeoff
problem”, Applied Soft Computing, Vol. 21, pp. 320–329, August 2014.
3. Ranjit Kaur, Manjeet Singh Patterh and J.S. Dhillon, “Real Coded Genetic Algorithm for Design of IIR Digital Filter
with Conflicting Objectives”, Applied Mathematics & Information Sciences, Vol. 8, No. 5, pp. 2635–2644, September
2014.
4. Hooi Ling Khoo, Lay Eng Teoh and Qiang Meng, “A bi-objective optimization approach for exclusive bus lane selection
and scheduling design”, Engineering Optimization, Vol. 46, No. 7, pp. 987–1007, July 3, 2014.
5. David Pasquale, Giacomo Persico and Stefano Rebay, “Optimization of Turbomachinery Flow Surfaces Applying a CFDBased Throughflow Method”, Journal of Turbomachinery–Transactions of the ASME, Vol. 136, No. 3, Article Number:
031013, March 2014.
6. Shiyou Yang, S.L. Ho, Yingying Yao, Lie Liu and Lie Wu, “Studies on numerical methodologies for inverse problems
and optimizations in China”, COMPEL–International Journal for Computation and Mathematics in Electrical and
Electronics Engineering, Vol. 33, Nos. 1-2, pp. 56–64, 2014.
7. Nikos D. Lagaros, “An efficient dynamic load balancing algorithm”, Computational Mechanics, Vol. 53, No. 1, pp.
59–76, January 2014.
8. Sultan Nomal Qasem, Siti Mariyam Shamsuddin, Siti Zaiton Mohd Hashim, Maslina Darus and Eiman Al-Shammari,
“Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems”,
Information Sciences, Vol. 239, pp. 165–190, August 1, 2013.
90
9. Indranil Pan and Saptarshi Das, “Frequency domain design of fractional order PID controller for AVR system using
chaotic multi-objective optimization”, International Journal of Electrical Power & Energy Systems, Vol. 51, pp. 106–118,
October 2013.
10. Deogratias Nurwahaa and Xinhou Wang, “Optimization of electrospinning process using intelligent control systems”,
Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 593–600, 2013.
11. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
12. Qiang Lu, Xiao-Yan Xia, Rong Chen, Da-Jun Miao, Sha-Sha Chen, Li-Jun Quan and Hai-Ou Li, “When the Lowest
Energy Does Not Induce Native Structures: Parallel Minimization of Multi-Energy Values by Hybridizing Searching
Intelligences”, PLOS One, Vol. 7, No. 9, Article Number: e44967, September 28, 2012.
13. Sultan Noman Qasem, Siti Mariyam Shamsuddin and Azlan Mohd Zain, “Multi-objective hybrid evolutionary algorithms
for radial basis function neural network design”, Knowledge-based Systems, Vol. 27, pp. 475–497, March 2012.
14. Sudipta Sikdar and Indrajit Mukherjee, “A Holistic Framework for Multiple Response Optimization of Hot Strip Rolling
Process”, Materials and Manufacturing Processes, Vol. 26, No. 11, pp. 1393–1403, 2011.
15. David A. Bennett, Ningchuan Xiao and Marc P. Armstrong, “Exploring the Geographic Consequences of Public Policies
Using Evolutionary Algorithms”, Annals of the Association of American Geographers, Vol. 94, No. 4, pp. 827–847,
2004.
16. Ningchuan Xiao, David A. Bennet and Marc P. Armstrong, “Using evolutionary algorithms to generate alternatives for
multiobjective site-search problems”, Environment and Planning A, Vol. 34, No. 4, pp. 639–656, April 2002.
17. F.R.B. Cruz, G. Kendall, L. While, A.R. Duarte and N.L.C. Brito, “Throughput Maximization of Queueing Networks
with Simultaneous Minimization of Service Rates and Buffers”, Mathematical Problems in Engineering, Article Number:
692593, 2012.
18. Rinku Dewri, Indrajit Ray, Nayat Poolsappasit and Darrell Whitley, “Optimal security hardening on attack tree models
of networks: a cost-benefit analysis”, International Journal of Information Security, Vol. 11, No. 3, pp. 167–188, June
2012.
19. Dimitris G. Fotakis, Epameinondas Sidiropoulos, Dimitrios Myronidis and Kostas Ioannou, “Spatial genetic algorithm
for multi-objective forest planning”, Forest Policy and Economics, Vol. 21, pp. 12–19, August 2012.
20. Mathieu Balesdent, Nicolas Berend, Philippe Depince and Abdelhamid Chriette, “A survey of multidisciplinary design
optimization methods in launch vehicle design”, Structural and Multidisciplinary Optimization, Vol. 45, No. 5, pp.
619–642, May 2012.
21. Mikko Linnala, Elina Madetoja, Henri Ruotsalainen and Jari Hamalainen, “Bi-level optimization for a dynamic multiobjective problem”, Engineering Optimization, Vol. 44, No. 2, pp. 195–207, 2012.
22. Daniele Cavalli and Luca Bechini, “Multi-objective optimisation of a model of the decomposition of animal slurry in soil:
Tradeoffs between simulated C and N dynamics”, Soil Biology & Biochemistry, Vol. 48, pp. 113–124, May 2012.
23. Chen-Shu Wang and Heng-Li Yang, “A recommender mechanism based on case-based reasoning”, Expert Systems with
Applications, Vol. 39, No. 4, pp. 4335–4343, March 2012.
24. Yang Zhang and Peter I. Rockett, “Application of Multiobjective Genetic Programming to the Design of Robot Failure
Recognition Systems”, IEEE Transactions on Automation Science and Engineering, Vol. 6, No. 2, pp. 372–376, April
2009.
25. Kwang Mong Sim and Bo An, “Evolving Best-Response Strategies for Market-Driven Agents Using Aggregative Fitness
GA”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 39, No. 3, pp.
284–298, May 2009.
26. Zhe Xu and Susan Lu, “Multi-objective optimization of sensor array using genetic algorithm”, Sensors and Actuators
B-Chemical, Vol. 160, No. 1, pp. 278–286, December 15, 2011.
27. Abdullah Konak, Sadan Kulturel-Konak and Gregory Levitin, “Multi-objective optimization of linear multi-state multiple sliding window system”, Reliability Engineering & System Safety, Vol. 98, No. 1, pp. 24–34, February 2012.
28. Musrrat. Ali, Patrick Siarry and Millie. Pant, “An efficient Differential Evolution based algorithm for solving multiobjective optimization problems”, European Journal of Operational Research, Vol. 217, No. 2, pp. 404–416, March 1,
2012.
29. A.S. Rocha, C.J.A. Macedo, P.H.S. Palhares and L. C. Brito, “An Improved Multiobjective Search Method Applied to
Single Frequency Networks Planning”, IEEE Latin America Transactions, Vol. 10, No. 1, pp. 1143–1148, January 2012.
30. Ling Wang, Xiang Zhong and Min Liu, “A novel group search optimizer for multi-objective optimization”, Expert Systems
with Applications, Vol. 39, No. 3, pp. 2939–2946, February 15, 2012.
31. Sultan Noman Qasem and Siti Mariyam Shamsuddin, “Memetic Elitist Pareto Differential Evolution algorithm based
Radial Basis Function Networks for classification problems”, Applied Soft Computing, Vol. 11, No. 8, pp. 5565–5581,
December 2011.
91
32. Rasmus K. Ursem and Peter Dueholm Justesen, “Multi-objective Distinct Candidates Optimization: Locating a few
highly different solutions in a circuit component sizing problem”, Applied Soft Computing, Vol. 12, No. 1, pp. 255–265,
January 2012.
33. Tomas Fencl, Pavel Burget and Jan Bilek, “Network topology design”, Control Engineering Practice, Vol. 19, No. 11,
pp. 1287–1296, November 2011.
34. Hans-Friedrich Köhn, “A review of multiobjective programming and its application in quantitative psychology”, Journal
of Mathematical Psychology, Vol. 55, No. 5, pp. 386–396, October 2011.
35. Rinku Dewri, Indrajit Ray, Indrakshi Ray and Darrell Whitley, “κ-Anonymization in the Presence of Publisher Preferences”, IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 11, pp. 1678–1690, November 2011.
36. Vadimas Starikovicius, Raimondas Ciegis and Oleg Iliev, “A Parallel Solver for the Design of Oil Filters”, Mathematical
Modelling and Analysis, Vol. 16, No. 2, pp. 326–341, June 2011.
37. Witold Stankiewicz, Robert Roszak and Marek Morzynski, “Genetic Algorithm-based Calibration of Reduced Order
Galerkin Models”, Mathematical Modelling and Analysis, Vol. 16, No. 2, pp. 233–247, June 2011.
38. Antonio C. Caputo, Pacifico M. Pelagagge and Mario Palumbo, “Economic optimization of industrial safety measures
using genetic algorithms”, Journal of Loss Prevention in the Process Industries, Vol. 24, No. 5, pp. 541–551, September
2011.
39. Javier Sanchez-Monedero, Pedro A. Gutierrez, F. Fernandez-Navarro and C. Hervas-Martinez, “Weighting Efficient
Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers”, Neural Processing Letters, Vol. 34, No. 2, pp.
101–116, October 2011.
40. Lixin Han and Hong Yan, “BSN: An automatic generation algorithm of social network data”, Journal of Systems and
Software, Vol. 84, No. 8, pp. 1261–1269, August 2011.
41. Abdullah Konak and Alice E. Smith, “Efficient Optimization of Reliable Two-Node Connected Networks: A Biobjective
Approach”, INFORMS Journal on Computing, Vol. 23, No. 3, pp. 430–445, Summer 2011.
42. M.P. Cuellar, S. Capel-Cuevas, M.C. Pegalajar, I. de Orbe-Paya and L.F. Capitan-Vallvey, “Minimization of sensing
elements for full-range optical pH device formulation”, New Journal of Chemistry, Vol. 35, No. 5, pp. 1042–1053, 2011.
43. Majid Ramezani, Mandi Bashiri and Anthony C. Atkinson, “A goal programming-TOPSIS approach to multiple response
optimization using the concepts of non-dominated solutions and prediction intervals”, Expert Systems with Applications,
Vol. 38, No. 8, pp. 9557–9563, August 2011.
44. Axel Nordin, Andreas Hopf, Damien Motte, Robert Bjarnemo and Claus-Christian Eckhardt, “An Approach to ConstraintBased and Mass-Customizable Product Design”, Journal of Computing and Information Science in Engineering, Vol.
11, No. 1, Article Number: 011006, March 2011.
45. Vassilis E. Zafeiris and E.A. Giakoumakis, “Optimized traffic flow assignment in multi-homed, multi-radio mobile hosts”,
Computer Networks, Vol. 55, No. 5, pp. 1114–1131, April 1, 2011.
46. Roberto Duran-Novoa, Noel Leon-Rovira, Humberto Aguayo-Tellez and David Said, “Inventive problem solving based
on dialectical negation, using evolutionary algorithms and TRIZ heuristics”, Computers in Industry, Vol. 62, No. 4, pp.
437–445, May 2011.
47. Debarati Kundu, Kaushik Suresh, Sayan Ghosh, Swagatam Das, B.K. Panigrahi and Sanjoy Das, “Multi-objective
optimization with artificial weed colonies”, Information Sciences, Vol. 181, No. 12, pp. 2441–2454, June 15, 2011.
48. Djamel Djenouri and Ilangko Balasingham, “Traffic-Differentiation-Based Modular QoS Localized Routing for Wireless
Sensor Networks”, IEEE Transactions on Mobile Computing, Vol. 10, No. 6, pp. 797–809, June 2011.
49. Fatimah Sham Ismail, Rubiyah Yusof and Marzuki Khalid, “Self Organizing Multi-Objective Optimization Problem”,
International Journal of Innovative Computing Information and Control, Vol. 7, No. 1, pp. 301–314, January 2011.
50. J. Hazra and A.K. Sinha, “A multi-objective optimal power flow using particle swarm optimization”, European Transactions on Electrical Power, Vol. 21, No. 1, pp. 1028–1045, January 2011.
51. C.K. Kwong, X.G. Luo and J.F. Tang, “A Multiobjective Optimization Approach for Product Line Design”, IEEE
Transactions on Engineering Management, Vol. 57, No. 5, pp. 97–108, February 2011.
52. J. Sanchez-Monedero, C. Hervas-Martinez, P.A. Gutierrez, Mariano Carbonero Ruz, M.C. Ramirez Moreno and M. CruzRamirez, “Evaluating the Performance of Evolutionary Extreme Learning Machines by a Combination of Sensitivity and
Accuracy Measures”, Neural Network World, Vol. 20, No. 7, pp. 899–912, 2010.
53. Nikos D. Lagaros, Vagelis Plevris and Manolis Papadrakakis, “Neurocomputing strategies for solving reliability-robust
design optimization problems”, Engineering Computations, Vol. 27, Nos. 7–8, pp. 819–840, 2010.
54. Md Tamjidul Hoque, Madhu Chetty, Andrew Lewis and Abdul Sattar, “Twin Removal in Genetic Algorithms for
Protein Structure Prediction Using Low-Resolution Model”, IEEE-ACM Transactions on Computational Biology and
Bioinformatics, Vol. 8, No. 1, pp. 234–245, January-February 2011.
92
55. Majid Vafaei Jahan and Mohammad-R Akbarzadeh-Totonchi, “From Local Search to Global Conclusions: Migrating
Spin Glass-Based Distributed Portfolio Selection”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 4,
pp. 591–601, August 2010.
56. Joao A. Zeferino, Antonio P. Antunes and Maria C. Cunha, “Multi-objective model for regional wastewater systems
planning”, Civil Engineering and Environmental Systems, Vol. 27, No. 2, pp. 95–106, 2010.
57. Yang Zhang and Peter I. Rockett, “A generic optimising feature extraction method using multiobjective genetic programming”, Applied Soft Computing, Vol. 11, No. 1, pp. 1087–1097, January 2011.
58. Saeid Fallah-Jamshidi, Maghsoud Amiri and Neda Karimi, “Nonlinear continuous multi-response problems: a novel
two-phase hybrid genetic based metaheuristic”, Applied Soft Computing, Vol. 10, No. 4, pp. 1274–1283, September
2010.
59. Sultan Noman Qasem and Siti Mariyam Shamsuddin, “Radial basis function network based on time variant multiobjective particle swarm optimization for medical diseases diagnosis”, Applied Soft Computing, Vol. 11, No. 1, pp.
1427–1438, January 2011.
60. M.N. Neema and A. Ohgai, “Multi-objective location modeling of urban parks and open spaces: Continuous optimization”, Computers Environment and Urban Systems, Vol. 34, No. 5, pp. 359–376, August 2010.
61. Arturo Alarcon-Rodriguez, Graham Ault and Stuart Galloway, “Multi-objective planning of distributed energy resources:
A review of the state-of-the-art”, Renewable & Sustainable Energy Reviews, Vol. 14, No. 5, pp. 1353–1366, June 2010.
62. Yang Zhang and Peter I. Rockett, “Domain-independent feature extraction for multi-classification using multi-objective
genetic programming”, Pattern Analysis and Applications, Vol. 13, No. 3, pp. 273–288, August 2010.
63. Qiang Meng and Hooi Ling Khoo, “A Pareto-optimization approach for a fair ramp metering”, Transportation Research
Part C–Emerging Technologies, Vol. 18, No. 4, pp. 489–506, August 2010.
64. M.T. Yazdani Sabouni, F. Jolai and A. Mansouri, “Heuristics for minimizing total completion time and maximum
lateness on identical parallel machines with setup times”, Journal of Intelligent Manufacturing, Vol. 21, No. 4, pp.
439–449, August 2010.
65. F.R.B. Cruz, T. Van Woensel and J. MacGregor Smith, “Buffer and throughput trade-offs in M/G/1/K queueing
networks: A bi-criteria approach”, International Journal of Production Economics, Vol. 125, No. 2, pp. 224–234, June
2010.
66. Aluizio Fausto Ribeiro Araujo and Cicero Garrozi, “MulRoGA: A Multicast Routing Genetic Algorithm approach
considering multiple objectives”, Applied Intelligence, Vol. 32, No. 3, pp. 330–345, June 2010.
67. S. Uhlig and O. Bonaventure, “Designing BGP-based outbound traffic engineering techniques for stub ASes”, Computer
Communication Review, Estados Unidos, Vol. 34, No. 5, pp. 89–106, October 2004.
68. V.J. Gillet, “Applications of evolutionary computation in drug design”, Structure and Bonding, Vol. 110, pp. 133–152,
2004.
69. Karl Doerner, Walter J. Gutjahr, Richard F. Hartl, Christine Strauss and Christian Stummer, “Pareto Ant Colony
Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection”, Annals of Operations Research, Vol.
131 Nos. 1–4, pp. 79–99, October 2004.
70. E.T. Martin, R.A. Hassan and W.A. Crossley, “Comparing the N-branch genetic algorithm and the multi-objective
genetic algorithm”, AIAA Journal, Vol. 42, No. 7, pp. 1495–1500, July 2004.
71. Edwin D. de Jong and Jordan B. Pollack, “Ideal Evaluation from Coevolution”, Evolutionary Computation, Vol. 12, No.
2, pp. 159–192, Summer 2004.
72. S.Y. Yang, J.R. Cardoso, S.L. Ho, P.H. Ni, J.M. Machado and E.W.C. Lo, “An improved tabu-based vector optimal
algorithm for design optimizations of electromagnetic devices”, IEEE Transactions on Magnetics, Vol. 40, No. 2, pp.
1140–1143, Part 2, March 2004.
73. D.X.M. Zheng, S.T. Ng and M.M. Kumaraswamy, “Applying a genetic algorithm-based multiobjective approach for
time-cost optimization”, Journal of Construction Engineering and Management–ASCE, Vol. 130, No. 2, pp. 168–176,
March-April 2004.
74. Vincenzo Cutello and Giuseppe Nicosia, “An immunological approach to combinatorial optimization problems”, Advances
in Artificial Intelligence—IBERAMIA 2002, Proceedings, pp. 361–370, Springer-Verlag, Lecture Notes in Artificial
Intelligence Vol. 2527, 2002.
75. T.L. Veith, M.L. Wolfe and C.D. Heatwole, “Optimization procedure for cost effective BMP placement at a watershed
scale”, Journal of the American Water Resources Association, Vol. 39, No. 6, pp. 1331–1343, December 2003.
76. John Atkinson-Abutridy, Chris Mellish and Stuart Aitken, “A Semantically Guided and Domain-Independent Evolutionary Model for Knowledge Discovery From Texts”, IEEE Transactions on Evolutionary Computation, Vol. 7, No. 6,
pp. 546–560, December 2003.
93
77. S. Dedieu, L. Pibouleau, C. Azzaro-Pantel and S. Domenech, “Design and retrofit of multiobjective batch plants via a
multicriteria genetic algorithm”, Computers & Chemical Engineering, Vol. 27, No. 12, pp. 1723–1740, December 15,
2003.
78. Ningchuan Xiao and Marc P. Armstrong, “A Specialized Island Model and Its Application in Multiobjective Optimization”, in Erick Cantú-Paz et al. (editors), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part
II, pp. 1530–1540, Springer. Lecture Notes in Computer Science Vol. 2724, July 2003.
79. Christian Blum and Andrea Roli, “Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison”, ACM Computing Surveys, Vol. 35, No. 3, pp. 268–308, September 2003.
80. P. Lacomme, C. Prins and M. Sevaux, “Multiobjective Capacitated Arc Routing Problem”, in Carlos M. Fonseca, Peter
J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization.
Second International Conference, EMO 2003, pp. 550–564, Springer. Lecture Notes in Computer Science. Volume 2632,
Faro, Portugal, April 2003.
81. S.L. Ho and S.Y. Yang, H.C. Wong and G.Z. Ni, “A simulated annealing algorithm for multiobjective optimizations of
electromagnetic devices”, IEEE Transactions on Magnetics, Vol. 39, No. 3, pp. 1285–1288, Part 1, May 2003.
82. O. Nicolotti, V.J. Gillet, P.J. Fleming and D.V.S. Green, “Multiobjective optimization in quantitative structure-activity
relationships: Deriving accurate and interpretable QSARs”, Journal of Medicinal Chemistry, Vol. 45, No. 23, pp.
5069–5080, November 7, 2002.
83. A. Heredia-Langner, D.C. Montgomery, and W.M. Carlyle, “Solving a multistage partial inspection problem using genetic
algorithms”, International Journal of Production Research, Vol. 40, No. 8, pp. 1923–1940, May 2002.
84. V. J. Gillet, W. Khatib, P. Willett, P.J. Fleming, and D.V.S. Green, “Combinatorial library design using a multiobjective
genetic algorithm”, Journal of Chemical Information and Computer Sciences, Vol. 42, No. 2, pp. 375-385 March-April
2002.
85. E.F. Khor, K.C. Tan & T.H. Lee, “Tabu-Based Exploratory Evolutionary Algorithm for Effective Multi-objective Optimization”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First
International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, Lecture Notes in Computer
Science Vol. 1993, Zurich, Suiza, pp. 344–358, Marzo de 2001.
86. Hui Li, Qingfu Zhang, Edward Tsang, and John A. Ford, “Hybrid Estimation of Distribution Algorithm for Multiobjective Knapsack Problem”, in Jens Gottlieb and Günter R. Raidl (editors), Evolutionary Computation in Combinatorial
Optimization, Proceedings of the 4th European Conference, EvoCOP 2004, Springer, pp. 145–154, Lecture Notes in
Computer Science, Vol. 3004, April 2004.
87. F. de Toro, E. Ros, S. Mota and J. Ortega, “Multi-objective optimization evolutionary algorithms applied to paroxysmal
atrial fibrillation diagnosis based on the k-nearest neighbours classifier”, Advances in Artificial Intelligence—IBERAMIA
2002, Proceedings, pp. 313–318, Springer-Verlag, Lecture Notes in Artificial Intelligence Vol. 2527, 2002.
88. K.C. Tan, T.H. Lee & E.F. Khor, “Incrementing Multi-objective Evolutionary Algorithms: Performance Studies and
Comparisons”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First
International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, Lecture Notes in Computer
Science Vol. 1993, Zurich, Suiza, pp. 111–125, Marzo de 2001.
89. Matthias Ehrgott and Xavier Gandibleux, “A Survey and Annotated Bibliography of Multiobjective Combinatorial
Optimization”, OR Spektrum, Vol. 22, pp. 425–460, 2000.
90. Y.H. Wang, S.Y. Yang, G.Z. Ni, P.H. Ni and S.L. Ho, “An emigration genetic algorithm for vector optimizations of
electromagnetic devices”, International Journal of Applied Electromagnetics and Mechanics, Vol. 19, Nos. 1–4, pp.
103–109, 2004.
91. Yuhuai Wang, Shiyou Yang, Guangzheng Ni, S.L. Ho and Z.J. Liu, “An Emigration Genetic Algorithm and Its application
to Multiobjective Optimal Designs of Electromagnetic Devices”, IEEE Transactions on Magnetics, Vol. 40, No. 2, pp.
1240–1243, March 2004.
92. K.C. Tan, T.H. Lee and E.F. Khor, “Automatic design of multi-variable quantitative feedback theory control systems
via evolutionary computation”, Proceedings of the Institution of Mechanical Engineers Part I—Journal of Systems and
Control Engineering, Vol. 215, No. I3, pp. 245–259, 2001.
93. X. Llorà, D.E. Goldberg, I. Traus and E. Bernadó, “Accuracy, parsimony, and generality in evolutionary learning
systems via multiobjective selection”, in Learning Classifier Systems, Lecture Notes in Artificial Intelligence Vol. 2661,
pp. 118–142, 2002.
94. Francisco de Toro, Eduardo Ros, Sonia Mota and Julio Ortega, “Non-invasive Atrial Disease Diagnosis Using Decision
Rules: A Multi-objective Optimization Approach”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy
Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO
2003, pp. 638–647, Springer. Lecture Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.
95. M.L. Hetland and P. Saetrom, “Evolutionary rule mining in time series databases”, Machine Learning, Vol. 58 Nos.
2–3, pp. 107–125, February-March 2005.
94
96. Hui Li and Qingfu Zhang, “Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGAII”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 2, pp. 284–302, April 2009.
97. Yang Zhang and Peter I. Rockett, “A Generic Multi-dimensional Feature Extraction Method Using Multiobjective
Genetic Programming”, Evolutionary Computation, Vol. 17, No. 1, pp. 89–115, Spring 2009.
98. Adernar Muraro, Jr., Angelo Passaro, Nancy Mieko Abe, Airam Jonatas Preto and Stephen Stephany, “Design of
Electrooptic Modulators Using a Multiobjective Optimization Approach”, Journal of Lightwave Technology, Vol. 26,
Nos. 13–16, pp. 2969–2976, July-August 2008.
99. Haldun Aytug and Serpil Sayin, “Using support vector machines to learn the efficient set in multiple objective discrete
optimization”, European Journal of Operational Research, Vol. 193, No. 2, pp. 510–519, March 1, 2009.
100. V. Javier Traver and Filiberto Pla, “Log-polar mapping template design: From task-level requirements to geometry
parameters”, Image and Vision Computing, Vol. 26, No. 10, pp. 1354–1370, October 1, 2008.
101. Aniruddha Sengupta and Anup Upadhyay, “Locating the critical failure surface in a slope stability analysis by genetic
algorithm”, Applied Soft Computing, Vol. 9, No. 1, pp. 387–392, January 2009.
102. A. Kaveh and M. Shahrouzi, “Optimal structural design family by genetic search and ant colony approach”, Engineering
Computations, Vol. 25, Nos. 3–4, pp. 268–288, 2008.
103. Siu-Lau Ho and Shiyou Yang, “A computationally efficient vector optimizer using ant colony optimizations algorithm
for multiobjective designs”, IEEE Transactions on Magnetics, Vol. 44, No. 6, pp. 1034–1037, June 2008.
104. M.M. Ould Sidi, S. Hayat, S. Hammadi and P. Borne, “A novel approach to developing and evaluating regulation
strategies for urban transport disrupted networks”, International Journal of Computer Integrated Manufacturing, Vol.
21, No. 4, pp. 480–493, 2008.
105. Chen-Shu Wang and Ching-Ter Chang, “Integrated genetic algorithm and goal programming for network topology design
problem with multiple objectives and multiple criteria”, IEEE-ACM Transactions on Networking, Vol. 16, No. 3, pp.
680–690, June 2008.
106. Miguel Delgado, Manuel P. Cuellar and Maria Carmen Pegalajar, “Multiobjective hybrid optimization and training of
recurrent neural Networks”, IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol. 38, No.
2, pp. 381–403, April 2008.
107. Marco A. Panduro and Carlos A. Brizuela, “Evolutionary multi-objective design of non-uniform circular phased arrays”,
COMPEL–The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol.
27, No. 2, pp. 551–566, 2008.
108. Knut Bernhardt, “Finding alternatives and reduced formulations for process-based models”, Evolutionary Computation,
Vol. 16, No. 1, pp. 63–88, Spring 2008.
109. Xingdong Zhang and Marc P. Armstrong, “Genetic algorithms and the corridor location problem: multiple objectives
and alternative solutions”, Environment and Planning B–Planning & Design, Vol. 35, No. 1, pp. 148–168, January
2008.
110. B. Huang, P. Fery, L. Xue and Y. Wang, “Seeking the Pareto front for multiobjective spatial optimization problems”,
International Journal of Geographical Information Science, Vol. 22, No. 5, pp. 507–526, 2008.
111. Jose Elias Claudio Arroyo, Pedro Sampaio Vieira and Dalessandro Soares Vianna, “A GRASP algorithm for the multicriteria minimum spanning tree problem”, Annals of Operations Research, Vol. 159, No. 1, pp. 125–133, March 2008.
112. Taylan Ilhan, Seyed M.R. Iravani and Mark S. Daskin, “The orienteering problem with stochastic profits”, IIE Transactions, Vol. 40, No. 4, pp. 406–421, April 2008.
113. Bhupendra Kurnar Pathak, Sanjay Srivastava and Karnal Srivastava, “Neural network embedded multiobjective genetic
algorithm to solve non-linear time-cost tradeoff problems of project scheduling”, Journal of Scientific & Industrial
Research, Vol. 67, No. 2, pp. 124–131, February 2008.
114. Mohamed Mahmoud Ould Sidi, Slim Hammadi, Saied Hayat and Pierre Borne, “Urban transport network regulation
and evaluation: A fuzzy evolutionary approach”, IEEE Transactions on Systems, Man, and Cybernetics Part A–Systems
and Humans, Vol. 38, No. 2, pp. 309–318, March 2008.
115. Ta-Yuan Chou, Tung-Kuan Liu, Chung-Nan Lee and Chi-Ruey Jeng, “Method of inequality-based multiobjective genetic
algorithm for domestic daily aircraft routing”, IEEE Transactions on Systems, Man, and Cybernetics Part A–Systems
and Humans, Vol. 38, No. 2, pp. 299–308, March 2008.
116. Qingfu Zhang and Hui Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition”, IEEE
Transactions on Evolutionary Computation, Vol. 11, No. 6, pp. 712–731, December 2007.
117. Peter J. Fleming and Maksim A. Pashkevich, “Optimal advertising campaign generation for multiple brands using
MOGA”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 37, No. 6, pp.
1190–1201, November 2007.
95
118. Fei Sun, Srivaths Ravi, Arland Raghunathan and Niraj K. Jha, “A synthesis methodology for hybrid custom instruction
and coprocessor generation for extensible processors”, IEEE Transactions on Computer-Aided Design of Integrated
Circuits and Systems, Vol. 26, No. 11, pp. 2035–2045, November 2007.
119. Nikos D. Lagaros and Manolis Papadrakakis, “Seismic design of RC structures: A critical assessment in the framework
of multi-objective optimization”, Earthquake Engineering & Structural Dynamics, Vol. 36, No. 12, pp. 1623–1639,
October 10, 2007.
120. Bilal Alatas, Erhan Akin and Ali Karci, “Modenar: Multi-objective differential evolution algorithm for mining numeric
association rules”, Applied Soft Computing, Vol. 8, No. 1, pp. 646–656, January 2008.
121. Nikos D. Lagaros and Michalis Fragiadakis, “Robust performance-based design optimization of steel moment resisting
frames”, International Journal of Earthquake Engineering, Vol. 11, No. 5, pp. 752–772, September 2007.
122. Adrian Dietz, Catherine Azzaro Pantel, Luc Guy Pibouleau and Serge Domenech, “Ecodesign of batch processes: Optimal
design strategies for economic and ecological bioprocesses”, International Journal of Chemical Reactor Engineering, Vol.
5, Art. No. A34, September 4, 2007.
123. J. Galuski and C.L. Bloebaum, “Multi-objective Pareto concurrent subspace optimization for multidisciplinary design”,
AIAA Journal, Vol. 45, No. 8, pp. 1894–1906, August 2007.
124. A. Kaveh and M. Shahrouai, “A hybrid ant strategy and genetic algorithm to tune the population size for efficient
structural optimization”, Engineering Computations, Vol. 24, Nos. 3–4, pp. 237–254, 2007.
125. Man Nie, Shiyou Yang, Guangzheng Ni, S.L. Ho and Peihong Ni, “An improved vector evolutionary algorithm for
multiobjective designs of electromagnetic devices”, International Journal of Applied Electromagnetics and Mechanics,
Vol. 25, Nos. 1–4, pp. 711–715, 2007.
126. Nikos D. Lagaros and Manolis Papadrakakis, “Robust seismic design optimization of steel structures”, Structural and
Multidisciplinary Optimization, Vol. 33, No. 6, pp. 457–469, June 2007.
127. Ningchuan Xiao, David A. Bennett and Marc P. Armstrong, “Interactive evolutionary approaches to multiobjective
spatial decision making: A synthetic review”, Computers Environment and Urban Systems, Vol. 31, No. 3, pp. 232–252,
May 2007.
128. A. Dietz, C. Azzaro-Pantel, L. Pibouleau and S. Domenech, “Optimal design of batch plants under economic and
ecological considerations: Application to a biochemical batch plant”, Mathematical and Computer Modelling, Vol. 46,
Nos. 1–2, pp. 109–123, July 2007.
129. Q.C. Zhao, Y.C. Ho and Q.S. Jia, “Vector ordinal optimization”, Journal of Optimization Theory and Applications, Vol.
125, No. 2, pp. 259–274, May 2005.
130. E.G. Carrano, L.A.E. Soares, R.H.C. Takahashi, R.R. Saldanha and O.M. Neto, “Electric distribution network multiobjective design using a problem-specific genetic algorithm”, IEEE Transactions on Power Delivery, Vol. 21, No. 2, pp.
995–1005, April 2006.
131. A. Dietz, C. Azzaro-Pantel, L. Pibouleau and S. Domenech, “Multiobjective optimization for multiproduct batch plant
design under economic and environmental considerations”, Computers & Chemical Engineering, Vol. 30, No. 4, pp.
599–613, February 15, 2006.
132. M. Gupta, J. Rees, A. Chaturvedi and J. Chi, “Matching information security vulnerabilities to organizational security
profiles: a genetic algorithm approach”, Decision Support Systems, Vol. 41, No. 3, pp. 592–603, March 2006.
133. Joshua Knowles, “ParEGO: A Hybrid Algorithm With On-Line Landscape Approximation for Expensive Multiobjective
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, pp. 50–66, February 2006.
134. E.G. Talbi and H. Meunier, “Hierarchical parallel approach for GSM mobile network design”, Journal of Parallel and
Distributed Computing, Vol. 66, No. 2, pp. 274–290, February 2006.
135. L.E. Smith, A.R. Swickard, A. Heredia-Langner, G.A. Warren, E.R. Siciliano and S.D. Miller, “Design considerations
for passive gamma-ray spectrometers”, IEEE Transactions on Nuclear Science, Vol. 52, No. 5, pp. 1721–1727, Part 3,
October 2005.
136. J.M. Malard, A. Heredia-Langner, W.R. Cannon, R. Mooney and D.J. Baxter, “Peptide identification via constrained
multi-objective optimization: Pareto-based genetic algorithms”, Concurrency and Computation—Practice & Experience,
Vol. 17, No. 14, pp. 1687–1704, December 10, 2005.
137. P.C.R. Lane and F. Gobet, “Discovering predictive variables when evolving cognitive models”, Pattern Recognition and
Data Mining, Pt 1, Proceedings, Springer, pp. 108–117, Lecture Notes in Computer Science Vol. 3686, 2005.
138. J. Yao, N. Kharma and P. Grogono, “A multi-population genetic algorithm for robust and fast ellipse detection”, Pattern
Analysis and Applications, Vol. 8, Nos. 1–2, pp. 149–162, 2005.
139. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
96
140. M. Lavagna, A. Povoleri and A.E. Finzi, “Interplanetary mission design with aero-assisted manoeuvres multi-objective
evolutive optimization”, Acta Astronautica, Vol. 57, Nos. 2–8, pp. 498–509, July-October 2005.
141. N.D. Lagaros, V. Plevris and M. Papadrakakis, “Multi-objective design optimization using cascade evolutionary computations”, Computer Methods in Applied Mechanics and Engineering, Vol. 194, Nos. 30–33, pp. 3496–3515, 2005.
142. Mario Köppen, Raul Vicente-Garcia and Betram Nickolay, “Fuzzy-Pareto-Dominance and Its Application in Evolutionary
Multi-objective Optimization”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors),
Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 399–412, Springer. Lecture
Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
143. E.-G. Talbi, S. Cahon and N. Melab, “Designing cellular networks using a parallel hybrid metaheuristic on the computational grid”, Computer Communications, Vol. 30, No. 4, pp. 698–713, February 26, 2007.
144. S.L. Ho, S.Y. Yang, G.Z. Ni and K.F. Wong, “An efficient multiobjective optimizer based on genetic algorithm and
approximation techniques for electromagnetic design”, IEEE Transactions on Magnetics, Vol. 43, No. 4, pp. 1605–1608,
April 2007.
145. Michalis Fragiadakis, Nikos D. Lagaros and Manolis Papadrakakis, “Performance-based multiobjective optimum design
of steel structures considering life-cycle cost”, Structural and Multidisciplinary Optimization, Vol. 32, No. 1, pp. 1–11,
July 2006.
146. Naveed Ramzan and Werner Witt, “Multi-objective optimization in distillation unit: a case study”, Canadian Journal
of Chemical Engineering, Vol. 84, No. 5, pp. 604–613, October 2006.
147. Seyed Hamid Reza Pasandideh and Seyed Taghi Akhavan Niaki, “Multi-response simulation optimization using genetic
algorithm within desirability function framework”, Applied Mathematics and Computation, Vol. 175, No. 1, pp. 366–382,
April 1, 2006.
148. S. Singh, A. Payne and R. Kingsland, “Modelling the human visual process by evolving images from noise”, Advances
in Machine Vision, Image Processing, and Pattern Analysis, Springer-Verlag, pp. 251–259, Lecture Notes in Computer
Science Vol. 4153, 2006.
149. M. Arakawa, K. Hasegawa and K. Funatsu, “QSAR study of anti-HIV HEPT analogues based on multi-objective genetic
programming and counter-propagation neural network”, Chemometrics and Intelligent Laboratory Systems, Vol. 83, No.
2, pp. 91–98, September 15, 2006.
150. I.M. Delamer and J.L.M. Lastra, “Evolutionary multi-objective optimization of QoS-Aware Publish/Subscribe Middleware in electronics production”, Engineering Applications of Artificial Intelligence, Vol. 19, No. 6, pp. 593–607,
September 2006.
151. H.W. Ding, L. Benyoucef and X.L. Xie, “A simulation-based multi-objective genetic algorithm approach for networked
enterprises optimization”, Engineering Applications of Artificial Intelligence, Vol. 19, No. 6, pp. 609–623, September
2006.
152. A. Dominguez, I. Stiharu and R. Sedaghati, “Practical design optimization of truss structures using the genetic algorithms”, Research in Engineering Design, Vol. 17, No. 2, pp. 73–84, September 2006.
153. A. Konak, D.W. Coit and A.E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial”, Reliability
Engineering & System Safety, Vol. 91, No. 9, pp. 992–1007, September 2006.
154. Daniel W. Boeringer and Douglas H. Werner, “Bézier representations for the multiobjective, optimization of conformal
array amplitude weights”, IEEE Transactions on Antennas and Propagation, Vol. 54, No. 7, pp. 1964–1970, July 2006.
155. M. Pedro, E. Monteiro and F. Boavida, “An approach to off-line inter-domain QoS-aware resource optimization”, Networking 2006: Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communication Systems, pp. 247–255, Springer, Lecture Notes in Computer Science Vol
3976, 2006.
156. P. Lacomme, C. Prins and M. Sevaux, “A genetic algorithm for a bi-objective capacitated arc routing problem”, Computers & Operations Research, Vol. 33, No. 12, pp. 3473–3493, December 2006.
157. A. Dietz, A. Aguilar-Lasserre, C. Azzaro-Pantel, L. Pibouleau and S. Domenech, “A fuzzy multiobjective algorithm for
multiproduct batch plant: Application to protein production”, Computers & Chemical Engineering, Vol. 32, Nos. 1–2,
pp. 292–306, January-February 2008.
158. Nima Assadian and Seid H. Pourtakdoust, “Multiobjective genetic optimization of Earth-Moon trajectories in the restricted four-body problem”, Advances in Space Research, Vol. 45, No. 3, pp. 398–409, February 1, 2010.
159. Khaled Badran and Peter I. Rockett, “The influence of mutation on population dynamics in multiobjective genetic
programming”, Genetic Programming and Evolvable Machines, Vol. 11, No. 1, pp. 5–33, March 2010.
160. K.P. Anagnostopoulos and G. Mamanis, “A portfolio optimization model with three objectives and discrete variables”,
Computers & Operations Research, Vol. 37, No. 7, pp. 1285–1297, July 2010.
161. S.L. Ho and Shiyou Yang, “Multiobjective Synthesis of Antenna Arrays Using a Vector Tabu Search Algorithm”, IEEE
Antennas and Wireless Propagation Letters, Vol. 8, pp. 947–950, 2009.
97
162. Gavin Paul, Dikai Liu, Nathan Kirchner and Garnini Dissanayake, “An Effective Exploration Approach to Simultaneous
Mapping and Surface Material-Type Identification of Complex Three-Dimensional Environments”, Journal of Field
Robotics, Vol. 26, Nos. 11–12, pp. 915–933, November-December 2009.
163. Vissarion Papadopoulos and Nikos D. Lagaros, “Vulnerability-based robust design optimization of imperfect shell structures”, Structural Safety, Vol. 31, No. 6, pp. 475–482, 2009.
164. M. Shafii and F. De Smedt, “Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic
algorithm”, Hydrology and Earth System Sciences, Vol. 13, No. 11, pp. 2137–2149, 2009.
165. Daniel Mueller-Gritschneder, Helmut Graeb and Ulf Schlichtmann, “A Successive Approach to Compute the Bounded
Pareto Front of Practical Multiobjective Optimization Problems”, SIAM Journal on Optimization, Vol. 20, No. 2, pp.
915–934, 2009.
166. Li-Hua Cheng, Ping-Chung Wu and Junghui Chen, “Numerical Simulation and Optimal Design of AGMD-Based Hollow
Fiber Modules for Desalination”, Industrial & Engineering Chemistry Research, Vol. 48, No. 10, pp. 4948–4959, May
20, 2009.
167. Honglin Li, Hailei Zhang, Mingyue Zheng, Jie Luo, Ling Kang, Xiaofeng Liu, Xicheng Wang and Hualiang Jiang, “An
effective docking strategy for virtual screening based on multi-objective optimization algorithm”, BMC Bioinformatics,
Vol. 10, article number 58, February 11, 2009.
168. A. Albers, N. Leon-Rovira, H. Aguayo and T. Maier, “Development of an engine crankshaft in a framework of computeraided innovation”, Computers in Industry, Vol. 60, No. 8, pp. 604–612, October 2009.
169. Shuguang Zhao, Xinquan Lai and Mingying Zhao, “A uniform-design based multi-objective adaptive genetic algorithm
and its application to automated design of electronic circuits”, Advances in Natural Computation, Part 1, pp. 653–656,
Lecture Notes in Computer Science Vol. 4221, 2006.
170. Catherine Azzaro-Pantel and Pascale Zarate, “Mutual benefits of two multicriteria analysis methodologies: A case study
for batch plant design”, Engineering Applications of Artificial Intelligence, Vol. 22, Nos. 4–5, pp. 546–556, June 2009.
171. Joshua Knowles, “Closed-Loop Evolutionary Multiobjective Optimization”, IEEE Computational Intelligence Magazine,
Vol. 4, No. 3, pp. 77–91, August 2009.
172. Heng-Li Yang and Cheng-Su Wang, “Recommender system for software project planning one application of revised CBR
algorithm”, Expert Systems with Applications, Vol. 36, No. 5, pp. 8938–8945, July 2009.
173. L.V.R. Arruda, M.C.S. Swiech, M.R.B. Delgado and F. Neves, Jr., “PID control of MIMO process based on rank niching
genetic algorithm”, Applied Intelligence, Vol. 29, No. 3, pp. 290–305, December 2008.
174. Xiaofeng Liu, Fang Bai, Sisheng Ouyang, Xicheng Wang, Honglin Li and Hualiang Jiang, “Cyndi: a multi-objective
evolution algorithm based method for bioactive molecular conformational generation”, BMC Bioinformatics, Vol. 10,
article no. 101, March 31, 2009.
• Carlos A. Coello Coello, “Treating Constraints as Objectives for Single-Objective Evolutionary Optimization”, Engineering Optimization, Vol. 32, No. 3, pp. 275–308, February, 2000.
1. Harish Garg, “Solving Structural Engineering Design Optimization Problems using an Artificial Bee Colony Algorithm”,
Journal of Industrial and Management Optimization, Vol. 10, No. 3, pp. 777–794, July 2014.
2. Deepak Sharma, Kalyanmoy Deb and N.N. Kishore, “Customized evolutionary optimization procedure for generating
minimum weight compliant mechanisms”, Engineering Optimization, Vol. 46, No. 1, pp. 39–60, January 2, 2014.
3. David A. Van Veldhuizen and Gary B. Lamont. “Multiobjective Evolutionary Algorithms: Analyzing the State-of-theArt”, Evolutionary Computation, Vol. 8, No. 2, pp. 125–147, Summer 2000.
4. Neculai Andrei, “Nonlinear Optimization Applications Using the GAMS Technology”, Springer, New York, USA, 2013,
ISBN 978-1-4614-6797-7, página 94.
5. Kalyanmoy Deb and Rituparna Datta, “A bi-objective constrained optimization algorithm using a hybrid evolutionary
and penalty function approach”, Engineering Optimization, Vol. 45, No. 5, pp. 503–527, May 1, 2013.
6. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
7. Dhish Saxena, Alessandro Rubino, João A. Duro and Ashutosh Tiwari, “Identifying the redundant, and ranking the
critical, constraints in practical optimization problems”, Engineering Optimization, Vol. 45, Nos. 7-9, pp. 787–809,
July-September, 2013.
8. LiCheng Jiao, Lin Li, RongHua Shang, Fang Liu and Rustam Stolkin, “A novel selection evolutionary strategy for
constrained optimization”, Information Sciences, Vol. 239, pp. 122–141, August 1, 2013.
9. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
98
10. Kalyanmoy Deb and Soumil Srivastava, “A genetic algorithm based augmented Lagrangian method for constrained
optimization”, Computational Optimization and Applications, Vol. 53, No. 3, pp. 869–902, December 2012.
11. Abu S.S.M. Barkat Ullah, Ruhul Sarker and Chris Lokan, “Handling equality constraints in evolutionary optimization”,
European Journal of Operational Research, Vol. 221, No. 3, pp. 480–490, September 16, 2012.
12. Yong Wang and Zixing Cai, “Combining Multiobjective Optimization with Differential Evolution to Solve Constrained
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 117–134, February
2012.
13. R.V. Rao, V.J. Savsani and D.P. Vakharia, “Teaching-learning-based optimization: A novel method for constrained
mechanical design optimization problems”. Computer-Aided Design, Vol. 43, No. 3, pp. 303–315, March 2011.
14. Ruibin Bai, Edmund K. Burke, Graham Kendall, Jingpeng Li and Barry McCollum, “A Hybrid Evolutionary Approach
to the Nurse Rostering Problem”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 4, pp. 580–590,
August 2010.
15. Enrico Zio and Irina Crenguta Popescu, “Recognizing signal trends on-line by a fuzzy-logic-based methodology optimized
via genetic algorithms”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 6, pp. 831–849, September 2007.
16. Cheng-gang Cui, Yan-jun Li and Tie-jun Wu, “A relative feasibility degree based approach for constrained optimization
problems”, Journal of Zhejiang University–Science C–Computers & Electronics, Vol. 11, No. 4, pp. 249–260, April
2010.
17. M. Farina and P. Amato, “Linked interpolation-optimization strategies for multicriteria optimization problems”, Soft
Computing–A Fusion of Foundations, Methodologies and Applications, Springer-Verlag, Vol. 9, No. 1, pp. 54–65,
January 2005.
18. B. Lin and D.C. Miller, “Tabu search algorithm for chemical process optimization”, Computers & Chemical Engineering,
Vol. 28, No. 11, pp. 2287–2306, October 15, 2004.
19. Giuseppe Ascia, Vincenzo Catania and Maurizio Palesi, “A GA-Based Design Space Exploration Framework for Parameterized System-On-A-Chip Platforms”, IEEE Transactions on Evolutionary Computation, Vol. 8, No. 4, pp. 329–346,
August 2004.
20. S. He, E. Prempain and Q.H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems”,
Engineering Optimization, Vol. 36, No. 5, pp. 585–605, October 2004.
21. Raziyeh Farmani and Jonathan A. Wright, “Self-Adaptive Fitness Formulation for Constrained Optimization”, IEEE
Transactions on Evolutionary Computation, Vol. 7, No. 5, pp. 445–455, October 2003.
22. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical design with constraints and preferences: application
to a purge valve”, Computer Methods in Applied Mechanics and Engineering, Vol. 192, Nos. 39–40, pp. 4355–4378,
2003.
23. B.J. Reardon, “Optimizing the hot isostatic pressing process”, Materials and Manufacturing Processes, Vol. 18, No. 3,
pp. 493–508, 2003.
24. D.J. Barrett, “Steady state turnover time of carbon in the Australian terrestrial biosphere”, Global Biogeochemical
Cycles, Vol. 16, No. 4, Art. No. 1108, December 3, 2002.
25. V.S. Summanwar, V.K. Jayaraman, B.D. Kulkarni, H.S. Kusumakar, K. Gupta, and J. Rajesh, “Solution of constrained
optimization problems by multi-objective genetic algorithm”, Computers and Chemical Engineering, Vol. 26, No. 10,
pp. 1481–1492, October 15, 2002.
26. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
27. Haiyan Lu and Weiqi Chen, “Self-adaptive velocity particle swarm optimization for solving constrained optimization
problems”, Journal of Global Optimization, Vol. 41, No. 3, pp. 427–445, July 2008.
28. Marco A. Panduro and Carlos A. Brizuela, “Evolutionary multi-objective design of non-uniform circular phased arrays”,
COMPEL–The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol.
27, No. 2, pp. 551–566, 2008.
29. Yong Wang, Zixing Cai, Yuren Zhou and Wei Zeng, “An Adaptive Tradeoff Model for Constrained Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 80–92, February 2008.
30. Jing Liu, Weicai Zhong and Licheng Hao, “An organizational evolutionary algorithm for numerical optimization”, IEEE
Transactions on Systems, Man and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 1052–1064, August 2007.
31. Yong Wang, Hui Liu, Zixing Cai and Yuren Zhou, “An orthogonal design based constrained evolutionary optimization
algorithm”, Engineering Optimization, Vol. 39, No. 6, pp. 715–736, September 2007.
32. Yong Wang, Zixing Cai, Guanqi Guo and Yuren Zhou, “Multiobjective optimization and hybrid evolutionary algorithm
to solve constrained optimization problems”, IEEE Transactions on Systems, Man and Cybernetics Part B–Cybernetics,
Vol. 37, No. 3, pp. 560–575, June 2007.
99
33. Giuseppe Ascia, Vincenzo Catania and Maurizio Palesi, “A multiobjective genetic approach for system-level exploration
in parameterized systems-on-a-chip”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,
Vol. 24, No. 4, pp. 635–645, April 2005.
34. T.P. Runarsson and X. Yao, “Search biases in constrained evolutionary optimization”, IEEE Transactions on Systems,
Man, and Cybernetics Part C—Applications and Reviews, Vol. 35, No. 2, pp. 233–243, May 2005.
35. D. Naso, B. Turchiano and C. Meloni, “Single and multi-objective evolutionary algorithms for the coordination of serial
manufacturing operations”, Journal of Intelligent Manufacturing, Vol. 17, No. 2, pp. 251–270, April 2006.
36. Yuping Wang, Dalian Liu, and Yiu-Ming Cheung, “Preference Bi-objective Evolutionary Algorithm for Constrained
Optimization”, in Yue Hao et al. (editors), Computational Intelligence and Security. International Conference, CIS
2005, pp. 184–191, Springer, Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December 2005.
37. C.J.K. Lee, T. Furukawa and S. Yoshimura, “A human-like numerical technique for design of engineering systems”,
International Journal for Numerical Methods in Engineering, Vol. 64, No. 14, pp. 1915–1943, December 14, 2005.
38. S.S. Rao and Y. Xiong, “A hybrid genetic algorithm for mixed-discrete design optimization”, Journal of Mechanical
Design, Vol. 127, No. 6, pp. 1100-1112, November 2005.
39. M.S. Osman, M.A. Abo-Sinna and A.A. Mousa, “A combined genetic algorithm-fuzzy logic controller (GA-FLC) in
nonlinear programming”, Applied Mathematics and Computation, Vol. 170, No. 2, pp. 821–840, November 15, 2005.
40. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
41. Sangameswar Venkatraman and Gary G. Yen, “A Generic Framework for Constrained Optimization Using Genetic
Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 4, August 2005
42. Milan Zeleny, “The Evolution of Optimality: De Novo Programming”, in Carlos A. Coello Coello, Arturo Hernández
Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO
2005, pp. 1–13, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
43. Zixing Cai and Yong Wang, “A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 658–675, December 2006.
44. George G. Dimopoulos, “Mixed-variable engineering optimization based on evolutionary and social metaphors”, Computer Methods in Applied Mechanics and Engineering, Vol. 196, Nos. 4–6, pp. 803–817, 2007.
45. Haiyan Lu and Weiqi Chen, “Dynamic-objective particle swarm optimization for constrained optimization problems”,
Journal of Combinatorial Optimization, Vol. 12, No. 4, pp. 409–419, December 2006.
46. S. Favuzza, M.G. Ippolito and E.R. Sanseverino, “Crowded comparison operators for constraints handling in NSGA-II
for optimal design of the compensation system in electrical distribution networks”, Advanced Engineering Informatics,
Vol. 20, No. 2, pp. 201–211, April 2006.
47. G. Ascia, V. Catania and D. Panno, “An evolutionary management scheme in high-performance packet switches”,
IEEE-ACM Transactions on Networking, Vol. 13, No. 2, pp. 262–275, April 2005.
48. A.A. Aguilar-Lasserre, L. Pibouleau, C. Azzaro-Pantel and S. Domenech, “Enhanced genetic algorithm-based fuzzy
multiobjective strategy to multiproduct batch plant design”, Applied Soft Computing, Vol. 9, No. 4, pp. 1321–1330,
September 2009.
49. Mihalis M. Golias, Maria Boile and Sotirios Theofanis, “Berth scheduling by customer service differentiation: A multiobjective approach”, Transportation Research Part E–Logistics and Transportation Review, Vol. 45, No. 6, pp. 878–892,
November 2009.
50. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique for
constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,
pp. 1501–1534, March 2009.
51. Chun’an Liu and Yuping Wang, “Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization
problems”, Journal of Systems Engineering and Electronics, Vol. 20, No. 1, pp. 204–210, February 2009.
• Alfredo G. Hernández-Dı́az, Luis V. Santana-Quintero, Carlos A. Coello Coello and Julián Molina, “Paretoadaptive -dominance”, Evolutionary Computation, Vol. 15, No. 4, pp. 493–517, Winter 2007.
1. Sanghamitra Bandyopadhyay, Rudrasis Chakraborty and Ujjwal Maulik, “Priority based epsilon dominance: A new
measure in multiobjective optimization”, Information Sciences, Vol. 305, pp. 97–109, June 1, 2015.
2. Hu Xia, Jian Zhuang and Dehong Yu, “Multi-objective unsupervised feature selection algorithm utilizing redundancy
measure and negative epsilon-dominance for fault diagnosis”, Neurocomputing, Vol. 146, pp. 113–124, December 25,
2014.
3. Miqing Li, Shengxiang Yang, Jinhua Zheng and Xiaohui Liu, “ETEA: A Euclidean Minimum Spanning Tree-Based
Evolutionary Algorithm for Multi-Objective Optimization”, Evolutionary Computation, Vol. 22, No. 2, pp. 189–230,
Summer 2014.
100
4. Yangyang Li, Xia Xu, Peidao Li and Licheng Jiao, “Improved RM-MEDA with local learning”, Soft Computing, Vol.
18, No. 7, pp. 1383–1397, July 2014.
5. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
6. Ahmed Kafafy, Ahmed Bounekkar and Stéphane Bonnevay, “HEMH2: An Improved Hybrid Evolutionary Metaheuristics
for 0/1 Multiobjective Knapsack Problems”, in Lam Thu Bui, Yew Soon Ong, Nguyen Xuan Hoai, Hisao Ishibuchi and
Ponnuthurai Nagaratnam Suganthan (editors), Simulated Evolution and Learning, 9th International Conference, SEAL
2012, pp. 104–116, Springer. Lecture Notes in Computer Science Vol. 7673, Hanoi, Vietnam, December 16-19, 2012.
7. Jian-Qiu Zhang, Feng Xu and Xia-Wen Fang, “Decomposition of Multi-Objective Evolutionary Algorithm based on
Estimation of Distribution”, Applied Mathematics & Information Sciences, Vol. 8, No. 1, pp. 249–254, January 2014.
8. Shengxiang Yang, Miqing Li, Xiaohui Liu and Jinhua Zheng, “ A Grid-Based Evolutionary Algorithm for Many-Objective
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 5, pp. 721–736, October 2013.
9. Gilberto Reynoso-Meza, Sergio Garcia-Nieto, Javier Sanchis and F. Xavier Blasco, “Controller Tuning by Means of MultiObjective Optimization Algorithms: A Global Tuning Framework”, IEEE Transactions on Control Systems Technology,
Vol. 21, No. 2, pp. 445–458, March 2013.
10. Gilberto Reynoso-Meza, Xavier Blasco, Javier Sanchis and Juan M. Herrero, “Comparison of design concepts in multicriteria decision-making using level diagrams”, Information Sciences, Vol. 221, pp. 124–141, February 1, 2013.
11. Yong Wang, Jian Xiang and Zixing Cai, “A regularity model-based multiobjective estimation of distribution algorithm
with reducing redundant cluster operator”, Applied Soft Computing, Vol. 12, No. 11, pp. 3526–3538, November 2012.
12. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Juan M. Herrero, “Multiobjective evolutionary algorithms
for multivariable PI controller design”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7895–7907, July 2012.
13. Dongdong Yang, Licheng Jiao, Maoguo Gong and Jie Feng, “Adaptive Ranks Clone and k-Nearest Neighbor List-Based
Immune Multi-Objective Optimization”, Computational Intelligence, Vol. 26, No. 4, pp. 359–385, November 2010.
14. J.R. Figueira, A. Liefooghe, E.-G. Talbi and A.P. Wierzbicki, “A parallel multiple reference point approach for multiobjective optimization”, European Journal of Operational Research, Vol. 205, No. 2, pp. 390–400, September 1, 2010.
15. Wenyin Gong, Zhihua Cai and Li Zhu, “An efficient multiobjective differential evolution algorithm for engineering
design”, Structural and Multidisciplinary Optimization, Vol. 38, No. 2, pp. 137–157, April 2009.
16. A. Liefooghe, L. Jourdan and E.-G. Talbi, “Metaheuristics and cooperative approaches for the Bi-objective Ring Star
Problem”, Computers & Operations Research, Vol. 37, No. 6, pp. 1033–1044, June 2010.
17. Wenyin Gong and Zhihua Cai, “An improved multiobjective differential evolution based on Pareto-adaptive epsilondominance and orthogonal design”, European Journal of Operational Research, Vol. 198, No. 2, pp. 576–601, October
16, 2009.
• Carlos A. Coello Coello, “Evolutionary Multiobjective Optimization: A Historical View of the Field”, IEEE
Computational Intelligence Magazine, Vol. 1, No. 1, pp. 28–36, February 2006.
1. Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano and Alexandre Claudio Botazzo Delbem, “General Subpopulation Framework and Taming the Conflict Inside Populations”, Evolutionary Computation, Vol. 23, No. 1, pp.
1–36, 2015.
2. Caihong Mu, Licheng Jiao, Yi Liu and Yangyang Li, “Multiobjective nondominated neighbor coevolutionary algorithm
with elite population”, Soft Computing, Vol. 19, No. 5, pp. 1329–1349, May 2015.
3. Mohammad Mortazavi-Naeini, George Kuczera and Lijie Cui, “Efficient multi-objective optimization methods for computationally intensive urban water resources models”, Journal of Hydroinformatics, Vol. 17, No. 1, pp. 36–55, 2015.
4. Tran Duc-Hoc, Min-Yuan Cheng and Minh-Tu Cao, “Hybrid multiple objective artificial bee colony with differential
evolution for the time-cost-quality tradeoff problem”, Knowledge-Based Systems, Vol. 74, pp. 176–186, January 2015.
5. Khairy Elsayed and Chris Lacor, “ Robust parameter design optimization using Kriging, RBF and RBFNN with gradientbased and evolutionary optimization techniques”, Applied Mathematics and Computation, Vol. 236, pp. 325–344, June
1, 2014.
6. Swaantje Casjens, Holger Schwender, Thomas Bruning and Katja Ickstadt, “A novel crossover operator based on variable
importance for evolutionary multi-objective optimization with tree representation”, Journal of Heuristics, Vol. 21, No.
1, pp. 1–24, February 2015.
7. Siwei Jiang, Yew-Soon Ong, Jie Zhang and Liang Feng, “Consistencies and Contradictions of Performance Metrics in
Multiobjective Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2391–2404, December 2014.
8. Arpita Mondal, T.I. Eldho and V.V.S. Gurunadha Rao, “Multiobjective Groundwater Remediation System Design Using
Coupled Finite-Element Model and Nondominated Sorting Genetic Algorithm II”, Journal of Hydrologic Engineering,
Vol. 15, No. 5, pp. 350–359, May 2010.
101
9. Dongdong Yang, Licheng Jiao, Ruican Niu and Maoguo Gong, “Investigation of Combinational Clustering Indices in
Artificial Immune Multi-Objective Clustering”, Computational Intelligence, Vol. 30, No. 1, pp. 115–144, February 2014.
10. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
11. Kaustuv Nag, Tandra Pal and Nikhil R. Pal, “ASMiGA: An Archive-Based Steady-State Micro Genetic Algorithm”,
IEEE Transactions on Cybernetics, Vol. 45, No. 1, pp. 40–52, January 2015.
12. Jian Xiong, Jing Liu, Yingwu Chen and Hussein A. Abbass, “A Knowledge-Based Evolutionary Multiobjective Approach
for Stochastic Extended Resource Investment Project Scheduling Problems”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 5, pp. 742–763, October 2014.
13. A. Mohapatra, P.R. Bijwe and B.K. Panigrahi, “Efficient sensitivity based assessment of impact of uncertainties in
multi-objective framework”, International Journal of Electrical Power & Energy Systems, Vol. 64, pp. 947–955, January
2015.
14. Yan Xu, Zhao Yang Dong, Ke Meng, Wei Feng Yao, Rui Zhang and Kit Po Wong, “Multi-Objective Dynamic VAR Planning Against Short-Term Voltage Instability Using a Decomposition-Based Evolutionary Algorithm”, IEEE Transactions
on Power Systems, Vol. 29, No. 6, pp. 2813–2822, November 2014.
15. Nguyen Long, Lam T. Bui and Hussein A. Abbass, “DMEA-II: the direction-based multi-objective evolutionary algorithmII”, Soft Computing, Vol. 18, No. 11, pp. 2119–2134, November 2014.
16. Alvaro Rubio-Largo, Miguel A. Vega-Rodriguez and David L. Gonzalez-Alvarez, “Designing a fine-grained parallel differential evolution with Pareto tournaments for solving an optical networking problem”, Concurrency and Computation—
Practice & Experience, Vol. 26, No. 11, pp. 1908–1934, August 10, 2014.
17. Yutao Qi, Xiaoliang Ma, Fang Liu, Licheng Jiao, Jianyong Sun and Jianshe Wu, “MOEA/D with Adaptive Weight
Adjustment”, Evolutionary Computation, Vol. 22, No. 2, pp. 231–264, Summer 2014.
18. Yang Yu, Jiafu Tang, Jun Gong, Yong Yin and Iko Kaku, “Mathematical analysis and solutions for multi-objective
line-cell conversion problem”, European Journal of Operational Research, Vol. 236, No. 2, pp. 774–786, July 16, 2014.
19. J.M. Herrero, G. Reynoso-Meza, M. Martinez, X. Blasco and J. Sanchis, “A Smart-Distributed Pareto Front Using the
ev-MO GA Evolutionary Algorithm”, International Journal on Artificial Intelligence Tools, Vol. 23, No. 2, Article
Number: 1450002, April 2014.
20. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A multi-objective evolutionary algorithm-based ensemble optimizer
for feature selection and classification with neural network models”, Neurocomputing, Vol. 125, pp. 217–228, February
11, 2014.
21. Andrea Maesani, Pradeep Ruben Fernando and Dario Floreano, “Artificial Evolution by Viability Rather than Competition”, Plos One, Vol. 9, No. 1, Article Number: e86831, January 29, 2014.
22. Jiao Shi, Maoguo Gong, Wenping Ma and Licheng Jiao, “A Multipopulation Coevolutionary Strategy for Multiobjective
Immune Algorithm”, Scientific World Journal, Article Number: 539128, 2014.
23. D. Greiner, J.M. Emperador, B. Galvan, M. Mendez and G. Winter, “Engineering Knowledge-Based Variance-Reduction
Simulation and G-Dominance for Structural Frame Robust Optimization”, Advances in Mechanical Engineering, Article
Number: 680359, 2013.
24. Yalin Wang, Xiaofang Chen, Weihua Gui, Chunhua Yang, Lou Caccetta and Honglei Xu, “ A Hybrid Multiobjective
Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification”, Journal of
Applied Mathematics, Vol. Article Number: 841780, 2013.
25. Enriqueta Vercher and Jose D. Bermudez, “A Possibilistic Mean-Downside Risk-Skewness Model for Efficient Portfolio
Selection”, IEEE Transactions on Fuzzy Systems, Vol. 21, No. 3, pp. 585–595, June 2013.
26. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “An effective model of multiple multiobjective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs”,
Applied Soft Computing, Vol. 13, No. 5, pp. 2863–2895, May 2013.
27. Shady Attia, Mohamed Hamdy, William O’Brien and Salvatore Carlucci, “Assessing gaps and needs for integrating
building performance optimization tools in net zero energy buildings design”, Energy and Buildings, Vol. 60, pp. 110–
124, May 2013.
28. Khairy Elsayed and Chris Lacor, “CFD modeling and multi-objective optimization of cyclone geometry using desirability
function, artificial neural networks and genetic algorithms”, Applied Mathematical Modelling, Vol. 37, No. 8, pp. 5680–
5704, April 15, 2013.
29. Beatriz Pontes, Raul Giraldez and Jesus S. Aguilar-Ruiz, “Configurable pattern-based evolutionary biclustering of gene
expression data”, Algorithms for Molecular Biology, Vol. 8, Article Number: UNSP 4, February 23, 2013.
30. Jian Xiong, Xu Tan, Ke-wei Yang and Ying-wu Chen, “Fuzzy Group Decision Making for Multiobjective Problems:
Tradeoff between Consensus and Robustness”, Journal of Applied Mathematics, Article Number: 657978, 2013.
102
31. Tsung-Che Chiang, “Enhancing rule-based scheduling in wafer fabrication facilities by evolutionary algorithms: Review
and opportunity”, Computers & Industrial Engineering, Vol. 64, No. 1, pp. 524–535, January 2013.
32. Michael Georgiopoulos, “Learning in the feed-forward random neural network: A critical review”, Performance Evaluation, Vol. 68, No. 4, pp. 361–384, April 2011.
33. Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay and Ujjwal Maulik, “Multi-Class Clustering of Cancer Subtypes
through SVM Based Ensemble of Pareto-Optimal Solutions for Gene Marker Identification”, Plos One, Vol. 5, No, 11.
Article Number: e13803, November 12, 2010.
34. Anirban Mukhopadhyay and Ujjwal Maulik, “A multiobjective approach to MR brain image segmentation”, Applied
Soft Computing, Vol. 11, No. 1, pp. 872–880, January 2011.
35. Sandra Garcia, David Quintana, Ines M. Galvan and Pedro Isasi, “Multiobjective Algorithms with Resampling for
Portfolio Optimization”, Computing and Informatics, Vol. 32, No. 4, pp. 777–796, 2013.
36. Engin Ufuk Ergul and Ilyas Eminoglu, “DOPGA: a new fitness assignment scheme for multi-objective evolutionary
algorithms”, International Journal of Systems Science, Vol. 45, No. 3, pp. 407–426, March 1, 2014.
37. Rui Wang, Robin C. Purshouse and Peter J. Fleming, “Preference-Inspired Coevolutionary Algorithms for ManyObjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 4, pp. 474–494, August
2013.
38. Vui Ann Shim, Kay Chen Tan, Chun Yew Cheong and Jun Yong Chia, “Enhancing the scalability of multi-objective
optimization via restricted Boltzmann machine-based estimation of distribution algorithm”, Information Sciences, Vol.
248, pp. 191–213, November 1, 2013.
39. Alvaro Rubio-Largo, Miguel A. Vega-Rodriguez, Juan A. Gomez-Pulido and Juan M. Sanchez-Perez, “Multiobjective
Metaheuristics for Traffic Grooming in Optical Networks”, IEEE Transactions on Evolutionary Computation, Vol. 17,
No. 4, pp. 457–473, August 2013.
40. Christiane Regina Soares Brasil, Alexandre Claudio Botazzo Delbem and Fernando Luis Barroso da Silva, “Multiobjective
evolutionary algorithm with many tables for purely ab initio protein structure prediction”, Journal of Computational
Chemistry, Vol. 34, No. 20, pp. 1719–1734, July 30, 2013.
41. Chenye Qiu, Chunlu Wang and Xingquan Zuo, “A novel multi-objective particle swarm optimization with K-means
based global best selection strategy”, International Journal of Computational Intelligence Systems, Vol. 6, No. 5, pp.
822–835, September 2013.
42. Xinye Cai, Ou Wei and Zhiqiu Huang, “Evolutionary Approaches for Multi-Objective Next Release Problem”, Computing
and Informatics, Vol. 31, No. 4, pp. 847–875, 2012.
43. Wanxing Sheng, Ke-yan Liu, Yongmei Liu, Xiaoli Meng and Xiaohui Song, “A New DG Multiobjective Optimization
Method Based on an Improved Evolutionary Algorithm”, Journal of Applied Mathematics, Article Number: 643791,
2013.
44. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
45. Anirban Mukhopadhyay, Sumanta Ray and Moumita De, “Detecting protein complexes in a PPI network: a gene
ontology based multi-objective evolutionary approach”, Molecular Biosystems, Vol. 8, No. 11, pp. 3036–3048, 2012.
46. Jian Xiong, Ying-wu Chen, Ke-wei Yang, Qing-song Zhao and Li-ning Xing, “A Hybrid Multiobjective Genetic Algorithm
for Robust Resource-Constrained Project Scheduling with Stochastic Durations”, Mathematical Problems in Engineering,
Article Number: 786923, 2012.
47. Jacob Shabi and Yoram Reich, “Developing an analytical model for planning systems verification, validation and testing
processes”, Advanced Engineering Informatics, Vol. 26, No. 2, pp. 429–438, April 2012.
48. Huajun Chen, Zhaohui Wu and Philippe Cudre-Mauroux, “Semantic Web Meets Computational Intelligence: State of
the Art and Perspectives”, IEEE Computational Intelligence Magazine, Vol. 7, No. 2, pp. 67–74, May 2012.
49. Jose Emmanuel Ramirez-Marquez and Claudio M. Rocco, “Vulnerability based robust protection strategy selection in
service networks”, Computers & Industrial Engineering, Vol. 63, No. 1, pp. 235–242, August 2012.
50. Yutao Qi, Fang Liu, Meiyun Liu, Maoguo Gong and Licheng Jiao, “Multi-objective immune algorithm with Baldwinian
learning”, Applied Soft Computing, Vol. 12, No. 8, pp. 2654–2674, August 2012.
51. Scott F. Page, Sheng Chen, Chris J. Harris and Neil M. White, “Repeated weighted boosting search for discrete or mixed
search space and multiple-objective optimisation”, Applied Soft Computing, Vol. 12, No. 9, pp. 2740–2755, September
2012.
52. J. Octavio Gutierrez-Garcia and Kwang Mong Sim, “GA-based cloud resource estimation for agent-based execution of
bag-of-tasks applications”, Information Systems Frontiers, Vol. 14, No. 4, pp. 925–951, September 2012.
53. Tomislav Capuder, Matija Zidar and Davor Skrlec, “Evolutionary algorithm with fuzzy numbers for planning active
distribution network”, Electrical Engineering, Vol. 94, No. 3, pp. 135–145, September 2012.
103
54. David Greiner and Prabhat Hajela, “Truss topology optimization for mass and reliability considerations-co-evolutionary
multiobjective formulations”, Structural and Multidisciplinary Optimization, Vol. 45, No. 4, pp. 589–613, April 2012.
55. Bernd Anselment, Veronika Schoemig, Christopher Kesten and Dirk Weuster-Botz, “Statistical vs. Stochastic experimental design: An experimental comparison on the example of protein refolding”, Biotechnology Progress, Vol. 28, No.
6, pp. 1499–1506, November-December 2012.
56. Wanxing Sheng, Yongmei Liu, Xiaoli Meng and Tianshu Zhang, “An Improved Strength Pareto Evolutionary Algorithm
2 with application to the optimization of distributed generations”, Computers & Mathematics with Applications, Vol.
64, No. 5, pp. 944–955, September 2012.
57. Yong Wang, Jian Xiang and Zixing Cai, “A regularity model-based multiobjective estimation of distribution algorithm
with reducing redundant cluster operator”, Applied Soft Computing, Vol. 12, No. 11, pp. 3526–3538, November 2012.
58. Walter J. Gutjahr, “Runtime Analysis of an Evolutionary Algorithm for Stochastic Multi-Objective Combinatorial
Optimization”, Evolutionary Computation, Vol. 20, No. 3, pp. 395–421, Fall 2012.
59. Jian Xiong, Ke-wei Yang, Jing Liu, Qing-song Zhao and Ying-wu Chen, “A two-stage preference-based evolutionary
multi-objective approach for capability planning problems”, Knowledge-Based Systems, Vol. 31, pp. 128–139, July 2012.
60. Lam Thu Bui, Zbigniew Michalewicz, Eddy Parkinson and Manuel Blanco Abello, “Adaptation in Dynamic Environments: A Case Study in Mission Planning”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 2, pp.
190–209, April 2012.
61. K.Y. Fung, C.K. Kwong, K.W.M. Siu and K.M. Yu, “A multi-objective genetic algorithm approach to rule mining for
affective product design”, Expert Systems with Applications, Vol. 39, No. 8, pp. 7411–7419, June 15, 2012.
62. Gui-bing Gao, Guo-jun Zhang, Gang Huang, Hai-ping Zhu and Pei-hua Gu, “Solving material distribution routing
problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm”, Journal of Central
South University of Technology, Vol. 19, No. 2, pp. 433–442, February 2012.
63. Khairy Elsayed and Chris Lacor, “Modeling and Pareto optimization of gas cyclone separator performance using RBF
type artificial neural networks and genetic algorithms”, Poweder Technology, Vol. 217, pp. 84–99, February 2012.
64. Na Luo, Feng Qian, Zhen-Cheng Ye, Hui Cheng and Wei-Min Zhong, “Estimation of Mass-Transfer Efficiency for
Industrial Distillation Columns”, Industrial & Engineering Chemistry Research, Vol. 51, No. 7, pp. 3023–3031, February
22, 2012.
65. Pankaj Joshi, Sameer B. Mulani, Wesley C.H. Slemp and Rakesh K. Kapania, “Vibro-Acoustic Optimization of Turbulent
Boundary Layer Excited Panel with Curvilinear Stiffeners”, Journal of Aircraft, Vol. 49, No. 1, pp. 52–65, JanuaryFebruary 2012.
66. Edmund K. Burke, Jingpeng Li and Rong Qu, “A hybrid model of integer programming and variable neighbourhood
search for highly-constrained nurse rostering problems”, European Journal of Operational Research, Vol. 203, No. 2, pp.
484–493, June 1, 2010.
67. C.A. Garcia Montoya and S. Mendoza Toro, “Implementation of an evolutionary algorithm in planning investment in a
power distribution system”, Revista Ingenierı́a e Investigación, Vol. 31, Supplement: 2, pp. 118–124, 2011.
68. Wei-Mei Chen, Hsien-Kuei Hwang and Tsung-Hsi Tsai, “Maxima-finding algorithms for multidimensional samples: A
two-phase approach”, Computational Geometry–Theory and Applications, Vol. 45, Nos. 1-2, pp. 33–53, JanuaryFebruary 2012.
69. Lam T. Bui, Hussein A. Abbass, Michael Barlow and Axel Bender, “Robustness Against the Decision-Maker’s Attitude
to Risk in Problems With Conflicting Objectives”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1,
pp. 1–19, February 2012.
70. Zai Wang, Ke Tang and Xin Yao, “Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular
Software Systems”, IEEE Transactions on Reliability, Vol. 59, No. 3, pp. 563–575, September 2010.
71. Rocio L. Cecchini, Ignacio Ponzoni and Jessica A. Carballido, “Multi-objective evolutionary approaches for intelligent
design of sensor networks in the petrochemical industry”, Expert Systems with Applications, Vol. 39, No. 3, pp. 2643–
2649, February 15, 2012.
72. De-bao Chen, Feng Zou and Jiang-tao Wang, “A multi-objective endocrine PSO algorithm and application”, Applied
Soft Computing, Vol. 11, No. 8, pp. 4508–4520, December 2011.
73. Bo Liu, Ling Wang, Ying Liu and Shouyang Wang, “A unified framework for population-based metaheuristics”, Annals
of Operations Research, Vol. 186, No. 1, pp. 231–262, June 2011.
74. Vui Ann Shim, Kay Chen Tan, Jun Yong Chia and Jin Kiat Chong, “Evolutionary algorithms for solving multi-objective
travelling salesman problem”, Flexible Services and Manufacturing Journal, Vol. 23, No. 2, pp. 207–241, June 2011.
75. Dongdong Yang, Licheng Jiao, Maoguo Gong and Fang Liu, “Artificial immune multi-objective SAR image segmentation
with fused complementary features”, Information Sciences, Vol. 181, No. 13, pp. 2797–2812, July 1, 2011.
104
76. Bernhard Dieber, Christian Micheloni and Bernhard Rinner, “Resource-Aware Coverage and Task Assignment in Visual
Sensor Networks”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21, No. 10, pp. 1424–1437,
October 2011.
77. Huajin Tang, Vui Ann Shim, Kay Chen Tan and Jun Yong Chia, “Restricted Boltzmann machine based algorithm for
multi-objective optimization”, in 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pp. 3958–3965, IEEE
Press, Barcelona, Spain, July 18–23, 2010.
78. Shu-Hsien Liao, Chia-Lin Hsieh and Yu-Siang Lin, “A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems”, Annals of Operations
Research, Vol. 186, No. 1, pp. 213–229, June 2011.
79. Xiaolan Wu, Alan T. Murray and Ningchuan Xiao, “A multiobjective evolutionary algorithm for optimizing spatial
contiguity in reserve network design”, Landscape Ecology, Vol. 26, No. 3, pp. 425–437, March 2011.
80. J. Samuel Baixauli-Soler, Eva Alfaro-Cid and Matilde O. Fernandez-Blanco, “Mean-VaR Portfolio Selection Under Real
Constraints”, Computational Economics, Vol. 37, No. 2, pp. 113–131, February 2011.
81. Chi Zhang, Jose Emmanuel Ramirez-Marquez and Claudio M. Rocco Sanseverino, “A holistic method for reliability
performance assessment and critical components detection in complex networks”, IIE Transactions, Vol. 43, No. 9, pp.
661–675, 2011.
82. Claudio M. Rocco, Jose Emmanuel Ramirez-Marquez, Daniel E. Salazar and Cesar Yajure, “Assessing the Vulnerability
of a Power System Through a Multiple Objective Contingency Screening Approach”, IEEE Transactions on Reliability,
Vol. 60, No. 2, pp. 394–403, June 2011.
83. X.D. Wang, C. Hirsch, Sh. Kang and C. Lacor, “Multi-objective optimization of turbomachinery using improved
NSGA-II and approximation model”, Computer Methods in Applied Mechanics and Engineering, Vol. 200, Nos. 9-12,
pp. 883–895, 2011.
84. Santosh Tiwari, Georges Fadel and Kalyanmoy Deb, “AMGA2: improving the performance of the archive-based microgenetic algorithm for multi-objective optimization”, Engineering Optimization, Vol. 43, No. 4, pp. 377–401, 2011.
85. Xiangwei Zheng and Hong Liu, “A scalable coevolutionary multi-objective particle swarm optimizer”, International
Journal of Computational Intelligence Systems, Vol. 3, No. 5, pp. 590–600, October 2010.
86. Alan Stone, “An Ontological Approach to Quantifying the Functional Flexibility of Embedded Systems”, IEEE Systems
Journal, Vol. 5, No. 1, pp. 111–120, March 2011.
87. Minqiang Li, Liu Liu and Dan Lin, “A fast steady-state epsilon-dominance multi-objective evolutionary algorithm”,
Computational Optimization and Applications, Vol. 48, No. 1, pp. 109–138, January 2011.
88. Jing Chen, Yan Lin, Junzhou Huo, Mingxia Zhang and Zhuoshang Ji, “Optimization of Ships’ Diagonal Ballast Water
Exchange Sequence Using a Multiobjective Genetic Algorithm”, Journal of Ship Research, Vol. 54, No. 4, pp. 257–267,
December 2010.
89. Xiaolan Wu and Tony H. Grubesic, “Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary
algorithm”, Journal of Geographical Systems, Vol. 12, No. 4, pp. 409–433, December 2010.
90. Dongdong Yang, Licheng Jiao, Maoguo Gong and Jie Feng, “Adaptive Ranks Clone and k-Nearest Neighbor List-Based
Immune Multi-Objective Optimization”, Computational Intelligence, Vol. 26, No. 4, pp. 359–385, November 2010.
91. J. Samuel Baixattli-Soler, Eva Alfaro-Cid and Matilde O. Fernandez-Blanco, “Several risk measures in portfolio selection:
Is it worthwhile?”, Revista Española de Financiación y Contabilidad–Spanish Journal of Finance and Accounting, Vol.
39, No. 147, pp. 421–444, July-September 2010.
92. Guilherme P. Coelho, Ana Estela A. da Silva and Fernando J. Von Zuben, “An immune-inspired multi-objective approach
to the reconstruction of phylogenetic trees”, Neural Computing & Applications, Vol. 19, No. 8, pp. 1103–1132, November
2010.
93. Arpita Mondal, T. I. Eldho and V. V. S. Gurunadha Rao, “Multiobjective Groundwater Remediation System Design Using Coupled Finite-Element Model and Nondominated Sorting Genetic Algorithm II”, Journal of Hydrologic Engineering,
Vol. 15, No. 5, pp. 350–359, May 2010.
94. Jing Chen, Yan Lin, Jun Zhou Huo, Ming Xia Zhang and Zhuo Shang Ji, “Optimal ballast water exchange sequence
design using symmetrical multitank strategy”, Journal of Marine Science and Technology, Vol. 15, No. 3, pp. 280–293,
September 2010.
95. Qingyun Duan and Thomas J. Phillips, “Bayesian estimation of local signal and noise in multimodel simulations of
climate change”, Journal of Geophysical Research–Atmospheres, Vol. 115, Article Number: D18123, September 28,
2010.
96. Siew Chin Neoh, Norhashimah Morad, Chee Peng Lim and Zalina Abdul Aziz, “A GA-PSO Layered Encoding Evolutionary Approach to 0/1 Knapsack Optimization”, International Journal of Innovative Computing Information and
Control, Vol. 6, No. 8, pp. 3489–3505, August 2010.
105
97. L.H. Wu, Y.N. Wang, X.F. Yuan and S.W. Zhou, “Environmental/economic power dispatch problem using multiobjective differential evolution algorithm”, Electric Power Systems Research, Vol. 80, No. 9, pp. 1171–1181, September
2010.
98. Jing Chen, Yan Lin, Jun Zhou Huo, Ming Xia Zhang and Zhuo Shang Ji, “Optimization of ship’s subdivision arrangement
for offshore sequential ballast water exchange using a non-dominated sorting genetic algorithm”, Ocean Engineering, Vol.
37, Nos. 11-12, pp. 978–988, August 2010.
99. J.-L. Liu and T.-F. Lee, “A Modified Non-Dominated Sorting Genetic Algorithm with Fractional Factorial Design for
Multi-Objective Optimization Problems”, Journal of Mechanics, Vol. 26, No. 2, pp. 143–156, June 2010.
100. Ruben Ruiz-Torrubiano and Alberto Suarez, “Hybrid Approaches and Dimensionality Reduction for Portfolio Selection
with Cardinality Constraints”, IEEE Computational Intelligence Magazine, Vol. 5, No. 2, pp. 92–107, May 2010.
101. Banu Soylu and Murat Koksalan, “A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems”,
IEEE Transactions on Evolutionary Computation, Vol. 14, No. 2, pp. 191–205, April 2010.
102. Assem Kaylani, Michael Georgiopoulos, Mansooreh Mollaghasemi, Georgios C. Anagnostopoulos, Christopher Sentelle
and Mingyu Zhong, “An Adaptive Multiobjective Approach to Evolving ART Architectures”, IEEE Transactions on
Neural Networks, Vol. 21, No. 4, pp. 529–550, April 2010.
103. A.C. Torres-Echeverria, S. Martorell and H.A. Thompson, “Modelling and optimization of proof testing policies for
safety instrumented systems”, Reliability Engineering & System Safety, Vol. 94, No. 4, pp. 838–854, April 2009.
104. Wenyin Gong, Zhihua Cai and Li Zhu, “An efficient multiobjective differential evolution algorithm for engineering
design”, Structural and Multidisciplinary Optimization, Vol. 38, No. 2, pp. 137–157, April 2009.
105. Lam T. Bui, Hussein A. Abbass and Daryl Essam, “Local models—an approach to distributed multi-objective optimization”, Computational Optimization and Applications, Vol. 42, No. 1, pp. 105–139, January 2009.
106. Lam Thu Bui, Kalyanmoy Deb, Hussein A. Abbass and Daryl Essam, “Interleaving guidance in evolutionary multiobjective optimization”, Journal of Computer Science and Technology, Vol. 23, No. 1, pp. 44–63, January 2008.
107. Min-Rong Chen and Yong-Zal Lu, “A novel elitist multiobjective optimization algorithm: Multiobjective extremal
optimization”, European Journal of Operational Research, Vol. 188, No. 3, pp. 637–651, August 1, 2008.
108. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjective immune algorithm with nondominated
neighbor-based selection”, Evolutionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.
109. Min-Rong Chen, Yong-zai Lu and Gen-ke Yang, “Multiobjective extremal optimization with applications to engineering
design”, Journal of Zhejiang University Science A, Vol. 8, No. 12, pp. 1905–1911, November 2007.
110. Paolo Di Barba, Maria Evelina Mognaschi and Antonio Savini, “Synthesizing a field source for magnetic stimulation of
peripheral nerves”, IEEE Transactions on Magnetics, Vol. 43, No. 11, pp. 4023–4029, November 2007.
111. C. Dimopoulos, “Explicit consideration of multiple objectives in cellular manufacturing”, Engineering Optimization, Vol.
39, No. 5, pp. 551–565, July 2007.
112. Mike Preuss, Boris Naujoks and Günter Rudolph, “Pareto Set and EMOA Behavior for Simple Multimodal Multiobjective
Functions”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós, L. Darrell Whitley
and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference, pp. 513–522,
Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
113. Huantong Geng, Min Zhang, Linfeng Huang and Xufa Wang, “Infeasible Elitists and Stochastic Ranking Selection in
Constrained Evolutionary Multi-objective Optimization”, in Tzai-Der Wang, Xiaodong Li, Shu-Heng Chen, Xufa Wang,
Hussein Abbass, Hitoshi Iba, Guoliang Chen and Xin Yao (editors), Simulated Evolution and Learning, 6th International
Conference, SEAL 2006, pp. 336–344, Springer. Lecture Notes in Computer Science Vol. 4247, Hefei, China, October
2006.
114. Mario Köppen, Katrin Franke and Raul Vicente-Garcia, “Tiny GAs for image processing applications”, IEEE Computational Intelligence Magazine, Vol. 1, No. 2, pp. 17–26, May 2006.
115. Min Zhang, Huantong Geng, Wenjian Luo, Linfeng Huang and Xufa Wang, “A hybrid of differential evolution and genetic
algorithm for constrained multiobjective optimization problems”, Simulated Evolution and Learning, Proceedings, pp.
318–327, Springer, Lecture Notes in Computer Science Vol. 4247, 2006.
116. Pietro Ducange, Beatrice Lazzerini and Francesco Marcelloni, “Multi-objective genetic fuzzy classifiers for imbalanced
and cost-sensitive datasets”, Soft Computing, Vol. 14, No. 7, pp. 713–728, May 2010.
117. Seppo J. Ovaska, Bernhard Sick and Alden H. Wright, “Periodical switching between related goals for improving evolvability to a fixed goal in multi-objective problems”, Information Sciences, Vol. 179, No. 23, pp. 4046–4056, November
25, 2009.
118. David Coulot, Arnaud Pollet, Xavier Collilieux and Philippe Berio, “Global optimization of core station networks for
space geodesy: application to the referencing of the SLR EOP with respect to ITRF”, Journal of Geodesy, Vol. 84, No.
1, pp. 31–50, January 2010.
106
119. David Greiner, Juan J. Aznarez, Orlando Maeso and Gabriel Winter, “Single- and multi-objective shape design of Ynoise barriers using evolutionary computation and boundary elements”, Advances in Engineering Software, Vol. 41, No.
2, pp. 368–378, February 2010.
120. Xu Bin, Chen Nan and Che Huajun, “An integrated method of multi-objective optimization for complex mechanical
structure”, Advances in Engineering Software, Vol. 41, No. 2, pp. 277–285, February 2010.
121. Axel Soto, Rocio L. Cecchini, Gustavo E. Vazquez and Ignacio Ponzoni, “Multi-Objective Feature Selection in QSAR
Using a Machine Learning Approach”, QSAR & Combinatorial Science, Vol. 28, Nos. 11–12, pp. 1509–1523, December
2009.
122. K.P. Anagnostopoulos and G. Mamanis, “A portfolio optimization model with three objectives and discrete variables”,
Computers & Operations Research, Vol. 37, No. 7, pp. 1285–1297, July 2010.
123. Jan Braun, Johannes Krettek, Frank Hoffmann and Torsten Bertram, “Multi-Objective Optimization with Controlled
Model Assisted Evolution Strategies”, Evolutionary Computation, Vol. 17, No. 4, pp. 577–593, Winter 2009.
124. Jan Braun, Frank Hoffmann, Johannes Krettek and Torsten Bertram, “Model Assisted Multiobjective Optimization
with lambda-Control”, AT-Automatisierungstechnik, Vol. 57, No. 3, pp. 115–128, 2009.
125. Chuan Shi, Zhenyu Yan, Zhongzhi Shi and Lei Zhang, “A fast multi-objective evolutionary algorithm based on a tree
structure”, Applied Soft Computing, Vol. 10, No. 2, pp. 468–480, March 2010.
126. Bilal Alatas and Erhan Akin, “Multi-objective rule mining using a chaotic particle swarm optimization algorithm”,
Knowledge-Based Systems, Vol. 22, No. 6, pp. 455–460, August 2009.
127. Anthony Finkelstein, Mark Harman, S. Afshin Mansouri, Jian Ren, Yuanyuan Zhang, “A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making”, Requirements Engineering,
Vol. 14, No. 4, pp. 231–245, December 2009.
128. Yao-Nan Wang, Liang-Hong Wu and Xiao-Fang Yuan, “Multi-objective self-adaptive differential evolution with elitist
archive and crowding entropy-based diversity measure”, Soft Computing, Vol. 14, No. 3, pp. 193–209, February 2010.
129. K. Tesch, M.A. Atherton, T.G. Karayiannis, M.W. Collins and P. Edwards, “Determining heat transfer coefficients using
evolutionary algorithms”, Engineering Optimization, Vol. 41, No. 9, pp. 855–870, September 2009.
130. Hussein A. Abbass, Sameer Alam and Axel Bender, “MEBRA: Multiobjective Evolutionary-Based Risk Assessment”,
IEEE Computational Intelligence Magazine, Vol. 4, No. 3, pp. 29–36, August 2009.
131. Chuan Shi, Zhenyu Yan, Kevin Lu, Zhingzhi Shi and Bai Wang, “A dominance tree and its application in evolutionary
multi-objective optimization”, Information Sciences, Vol. 179, No. 20, pp. 3540–3560, September 29, 2009.
132. Mahmoud R. Halfawy, Leila Dridi and Samar Baker, “Integrated Decision Support System for Optimal Renewal Planning
of Sewer Networks”, Journal of Computing in Civil Engineering, Vol. 22, No. 6, pp. 360–372, November-December
2008.
133. Joshua Knowles, “Closed-Loop Evolutionary Multiobjective Optimization”, IEEE Computational Intelligence Magazine,
Vol. 4, No. 3, pp. 77–91, August 2009.
134. Anirban Mukhopadhyay and Ujjwal Maulik, “Unsupervised Pixel Classification in Satellite Imagery Using Multiobjective
Fuzzy Clustering Combined With SVM Classifier”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No.
4, pp. 1132–1138, April 2009.
135. E. Alfaro-Cid, E.W. McGookin, D.J. Murray-Smith, “A comparative study of genetic operators for controller parameter
optimisation”, Control Engineering Practice, Vol. 17, No. 1, pp. 185–197, January 2009.
136. Yonas Gebre Woldesenbet, Gary G. Yen and Biruk G. Tessema, “Constraint Handling in Multiobjective Evolutionary
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 514–525, June 2009.
137. Wenyin Gong and Zhihua Cai, “An improved multiobjective differential evolution based on Pareto-adaptive epsilondominance and orthogonal design”, European Journal of Operational Research, Vol. 198, No. 2, pp. 576–601, October
16, 2009.
138. Xiangwei Zheng and Hong Liu, “A hybrid vertical mutation and self-adaptation based MOPSO”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 2030–2038, June 2009.
139. Ujjwal Maulik, Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay, “Combining Pareto-optimal clusters using
supervised learning for identifying co-expressed genes”, BMC Bioinformatics, Vol. 10, No. 27, pp. 1–16, January 20,
2009.
140. Dongdong Yang, Licheng Jiao and Maoguo Gong, “Adaptive Multi-Objective Optimization Based on Nondominated
Solutions”, Computational Intelligence, Vol. 25, No. 2, pp. 84–108, May 2009.
• Luis Vicente Santana-Quintero and Carlos A. Coello Coello, “An Algorithm Based on Differential Evolution
for Multi-Objective Problems”, International Journal of Computational Intelligence Research, Vol. 1, No.
2, pp. 151–169, 2005, ISSN 0973-1873.
107
1. Jinn-Tsong Tsai, Ching-I. Yang and Jyh-Horng Chou, “Hybrid sliding level Taguchi-based particle swarm optimization
for flowshop scheduling problems”, Applied Soft Computing, Vol. 15, pp. 177–192, February 2014.
2. Shuo Cheng, Jianhua Zhou and Mian Li, “A New Hybrid Algorithm for Multi-Objective Robust Optimization With
Interval Uncertainty”, Journal of Mechanical Design, Vol. 138, No. 2, Article Number: 021401, February 2015.
3. Ali Sadollah, Hadi Eskandar and Joong Hoon Kim, “Water cycle algorithm for solving constrained multi-objective
optimization problems”, Applied Soft Computing, Vol. 27, pp. 279–298, February 2015.
4. Xiang Li and Gang Du, “BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems”,
Computers & Operations Research, Vol. 40, No. 1, pp. 282–302, January 2013.
5. B.Y. Qu and P.N. Suganthan, “Constrained multi-objective optimization algorithm with an ensemble of constraint
handling methods”, Engineering Optimization, Vol. 43, No. 4, pp. 403–416, 2011.
6. Karthik Sindhya, Sauli Ruuska, Tomi Haanpää and Kaisa Miettinen, “A new hybrid mutation operator for multiobjective
optimization with differential evolution”, Soft Computing, Vol. 15, No. 10, pp. 2041–2055, October 2011.
7. Fred Otieno and Josiah Adeyemo, “Multi-objective cropping pattern in the Vaalharts irrigation scheme”, African Journal
of Agricultural Research, Vol. 6, No. 6, pp. 1286–1294, March 18, 2011.
8. Swagatam Das and Ponnuthurai Nagaratnam Suganthan, “Differential Evolution: A Survey of the State-of-the-Art”,
IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 27–54, February 2011.
9. Jean Robert Pereira Rodrigues, Tonnyfran Xavier de Araujo Sousa, Ricardo Batista de Andrade, Rezende Gomes dos
Santos and Mirian de Lourdes Noronha Motta Mello, “Overheating influence on solidification - thermal variables and
microstructure formation of aluminium alloy”, REM-Revista Escola de Minas, Vol. 62, No. 4, pp. 481–486, OctoberDecember 2009.
10. Wenyin Gong and Zhihua Cai, “An improved multiobjective differential evolution based on Pareto-adaptive epsilondominance and orthogonal design”, European Journal of Operational Research, Vol. 198, No. 2, pp. 576–601, October
16, 2009.
11. Wenyin Gong, Zhihua Cai and Li Zhu, “An efficient multiobjective differential evolution algorithm for engineering
design”, Structural and Multidisciplinary Optimization, Vol. 38, No. 2, pp. 137–157, April 2009.
• Carlos A. Coello Coello and Alan D. Christiansen. “MOSES : A Multiobjective Optimization Tool for
Engineering Design”, Engineering Optimization, Vol. 31, No. 3, pp. 337–368, 1999.
1. Yalin Wang, Xiaofang Chen, Weihua Gui, Chunhua Yang, Lou Caccetta and Honglei Xu, “ A Hybrid Multiobjective
Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification”, Journal of
Applied Mathematics, Vol. Article Number: 841780, 2013.
2. P. Martinez and A.M. Eliceche, “Bi-objective minimization of environmental impact and cost in utility plants”, Computers & Chemical Engineering, Vol. 35, No. 8, pp. 1478–1487, August 10, 2011.
3. Moein Moeini-Aghtaie, Ali Abbaspour and Mahmud Fotuhi-Firuzabad, “Incorporating Large-Scale Distant Wind Farms
in Probabilistic Transmission Expansion Planning-Part I: Theory and Algorithm”, IEEE Transactions on Power Systems,
Vol. 27, No. 3, pp. 1585–1593, August 2012.
4. R. Narmatha Banu and D. Devaraj, “Multi-objective GA with fuzzy decision making for security enhancement in power
system”, Applied Soft Computing, Vol. 12, No. 9, pp. 2756–2764, September 2012.
5. Lixin Han and Hong Yan, “BSN: An automatic generation algorithm of social network data”, Journal of Systems and
Software, Vol. 84, No. 8, pp. 1261–1269, August 2011.
6. S. Dhouib, A. Kharrat and H. Chabchoub, “Goal programming using multiple objective hybrid metaheuristic algorithm”,
Journal of the Operational Research Society, Vol. 62, No. 4, pp. 677–689, April 2011.
7. Souhail Dhouib, Aida Kharrat and Habib Chabchoub, “A multi-start threshold accepting algorithm for multiple objective
continuous optimization problems”, International Journal for Numerical Methods in Engineering, Vol. 83, No. 11, pp.
1498–1517, September 10, 2010.
8. Boguslaw Pytlak, “Multicriteria optimization of hard turning operation of the hardened 18HGT steel”, International
Journal of Advanced Manufacturing Technology, Vol. 49, Nos. 1–4, pp. 305–312, July 2010.
9. Ignacio Paya, Victor Yepes, Fernando Gonzalez-Vidosa and Antonio Hospitaler, “Multiobjective optimization of concrete
frames by simulated annealing”, Computer-Aided Civil and Infrastructure Engineering, Vol. 23, No. 8, pp. 596–610,
November 2008.
10. M.K. Rahman, “An intelligent moving object optimization algorithm for design problems with mixed variables, mixed
constraints and multiple objectives”, Structural and Multidisciplinary Optimization, Vol. 32, No. 1, pp. 40–58, July
2006.
11. M.S. Levin and M.A. Firer, “Hierarchical morphological design of immunoassay technology”, Computers in Biology and
Medicine, Vol. 35, No. 3, pp. 229–245, March 2005.
108
12. D. Sarkar and J.M. Modak, “Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using
nondominated sorting genetic algorithm”, Chemical Engineering Science, Vol. 60, No. 2, pp. 481–492, January 2005.
13. Adil Baykasoǧlu, “Preemptive goal programming using simulated annealing”, Engineering Optimization, Vol. 37, No. 1,
pp. 49–63, January 2005.
14. M.A. Abido, “A novel multiobjective evolutionary algorithm or environmental/economic power dispatch”, Electric Power
Systems Research, Vol. 65, No. 1, pp. 71–81, April 2003.
15. A. Herreros, E. Baeyens and J.R. Peran, “MRCD: A Genetic Algorithm for Multiobjective Robust Control Design”,
Engineering Applications of Artificial Intelligence, Vol. 15, Nos. 3–4, pp. 285–301, June-August 2002.
16. M.A. Abido, “A Niched Pareto Genetic Algorithm for Multiobjective Environmental/Economic Dispatch”, International
Journal of Electrical Power & Energy Systems, Vol. 25, No. 2, pp. 97–105, February 2003.
17. D.F. Jones, S.K. Mirrazavi, and M. Tamiz, “Multi-objective meta-heuristics: An overview of the current state-of-the-art”,
European Journal of Operational Research, Vol. 137, No. 1, pp. 1–9, February 2002.
18. A. Baykasoglu, “Goal programming using multiple objective tabu search”, Journal of the Operational Research Society,
Vol. 52, No. 12, pp. 1359–1369, December 2001.
19. C.J.K. Lee, T. Furukawa and S. Yoshimura, “A human-like numerical technique for design of engineering systems”,
International Journal for Numerical Methods in Engineering, Vol. 64, No. 14, pp. 1915–1943, December 14, 2005.
20. A. Baykasoglu, “Applying multiple objective tabu search to continues optimization problems with a simple neighbourhood
strategy”, International Journal for Numerical Methods in Engineering, Vol. 65, No. 3, pp. 406–424, January 15, 2006.
21. M.A. Abido, “Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem”, IEEE Transactions on
Evolutionary Computation, Vol. 10, No. 3, pp. 315–329, June 2006.
22. Yonas Gebre Woldesenbet, Gary G. Yen and Biruk G. Tessema, “Constraint Handling in Multiobjective Evolutionary
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 3, pp. 514–525, June 2009.
23. All Riza Yildiz, “A Novel Hybrid Immune Algorithm for Global Optimization in Design and Manufacturing”, Robotics
and Computer-Integrated Manufacturing, Vol. 25, No. 2, pp. 261–270, April 2009.
• Carlos A. Coello Coello and Nareli Cruz Cortés, “Hybridizing a Genetic Algorithm with an Artificial Immune
System for Global Optimization”, Engineering Optimization, Vol. 36, No. 5, pp. 607–634, October 2004.
1. Jinn-Tsong Tsai, “Improved differential evolution algorithm for nonlinear programming and engineering design problems”, Neurocomputing, Vol. 148, pp. 628–640, January 19, 2015.
2. Ye Xu, Ling Wang, Shengyao Wang and Min Liu, “An effective hybrid immune algorithm for solving the distributed
permutation flow-shop scheduling problem”, Engineering Optimization, Vol. 46, No. 9, pp. 1269–1283, September 2,
2014.
3. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.
53, No. 4, pp. 1168–1183, July 2014.
4. Gexiang Zhang, Jixiang Cheng, Marian Gheorghe and Qi Meng, “A hybrid approach based on differential evolution
and tissue membrane systems for solving constrained manufacturing parameter optimization problems”, Applied Soft
Computing, Vol. 13, No. 3, pp. 1528–1542, March 2013.
5. Chen-Hao Liu, Wei-Hsiu Huang and Pei-Chann Chang, “A two-stage AIS approach for grid scheduling problems”,
International Journal of Production Research, Vol. 50, No. 10, pp. 2665–2680, 2012.
6. Weiwei Zhang, Gary G. Yen and Zhongshi He, “Constrained Optimization via Artificial Immune System”, IEEE Transactions on Cybernetics, Vol. 44, No. 2, pp. 185–198, February 2014.
7. G. Kanagaraj, S.G. Ponnambalam and N. Jawahar, “A hybrid cuckoo search and genetic algorithm for reliabilityredundancy allocation problems”, Computers & Industrial Engineering, Vol. 66, No. 4, pp. 1115–1124, December
2013.
8. Sanyou Zeng, Yang Yang, Yulong Shi, Xianqiang Yang, Bo Xiao, Song Gao, Danping Yu and Zu Yan, “A micro niche evolutionary algorithm with lower-dimensional-search crossover for optimisation problems with constraints”, International
Journal of Bio-Inspired Computation, Vol. 1, No. 3, pp. 177–185, 2009.
9. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
10. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Cuckoo search algorithm: a metaheuristic approach to
solve structural optimization problems”, Engineering with Computers, Vol. 29, No. 1, pp. 17–35, January 2013.
11. Wenxing Xu, Zhiqiang Geng, Qunxiong Zhu and Xiangbai Gu, “A piecewise linear chaotic map and sequential quadratic
programming based robust hybrid particle swarm optimization”, Information Sciences, Vol. 218, pp. 85–102, January
1, 2013.
109
12. Pei-Chann Chang, Wei-Hsiu Huang and Ching-Jung Ting, “A hybrid genetic-immune algorithm with improved lifespan
and elite antigen for flow-shop scheduling problems”, International Journal of Production Research, Vol. 49, No. 17, pp.
5207–5230, 2011.
13. Jianyong Chen, Qiuzhen Lin and LinLin Shen, “An Immune-Inspired Evolution Strategy for Constrained Optimization
Problems”, International Journal on Artificial Intelligence Tools, Vol. 20, No. 3, pp. 549–561, June 2011.
14. Kuo-Ming Lee, Jinn-Tsong Tsai, Tung-Kuan Liu and Jyh-Horng Chou, “Improved genetic algorithm for mixed-discretecontinuous design optimization problems”, Engineering Optimization, Vol. 42, No. 10, pp. 927–941, October 2010.
15. I-Hong Kuo, Shi-Jinn Horng, Tzong-Wann Kao, Tsung-Lieh Lin, Cheng-Ling Lee, Yuan-Hsin Chen, Y.I. Pan and Takao
Terano, “A hybrid swarm intelligence algorithm for the travelling salesman problem”, Expert Systems, Vol. 27, No. 3,
pp. 166–179, July 2010.
16. K. Vijayalakshmi and S. Radhakrishnan, “A novel hybrid immune-based GA for dynamic routing to multiple destinations
for overlay networks”, Soft Computing, Vol. 14, No. 11, pp. 1227–1239, September 2010.
17. Ali Riza Yildiz, “A novel particle swarm optimization approach for product design and manufacturing”, International
Journal of Advanced Manufacturing Technology, Vol. 40, Nos. 5–6, pp. 617–628, January 2009.
18. Jenn-Ling Liu and Chia-Mei Chen, “Improved intelligent genetic algorithm applied to long-endurance airfoil optimization
design”, Engineering Optimization, Vol. 41, No. 2, pp. 137–154, February 2009.
19. Ali R. Yildiz, Nursel Ozturk, Necmettin Kaya and Ferruh Ozturk, “Hybrid multi-objective shape design optimization
using Taguchi’s method and genetic algorithm”, Structural and Multidisciplinary Optimization, Vol. 34, No. 4, pp.
317–332, October 2007.
20. P. Musilek, A. Lau, M. Reformat and L. Wyard-Scott, “Immune programming”, Information Sciences, Vol. 176, No. 8,
pp. 972–1002, April 22, 2006.
21. Rein Luus, Kelly Sabaliauskas and Ihor Harapyn, “Handling inequality constraints in direct search optimization”, Engineering Optimization, Vol. 38, No. 4, pp. 391–405, June 2006.
22. George G. Dimopoulos, “Mixed-variable engineering optimization based on evolutionary and social metaphors”, Computer Methods in Applied Mechanics and Engineering, Vol. 196, Nos. 4–6, pp. 803–817, 2007.
23. Ali Riza Yildiz, “A new design optimization framework based on immune algorithm and Taguchi’s method”, Computers
in Industry, Vol. 60, No. 8, pp. 613–620, October 2009.
24. Ali Riza Yildiz, “Hybrid immune-simulated annealing algorithm for optimal design and manufacturing”, International
Journal of Materials & Product Technology, Vol. 34, No. 3, pp. 217–226, 2009.
25. Ali Riza Yildiz, “An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing
optimization problems in industry”, Journal of Materials Processing Technology, Vol. 209, No. 6, pp. 2773–2780, March
19, 2009.
26. Ali Riza Yildiz, “A Novel Hybrid Immune Algorithm for Global Optimization in Design and Manufacturing”, Robotics
and Computer-Integrated Manufacturing, Vol. 25, No. 2, pp. 261–270, April 2009.
27. K. Vijayalakshmi and S. Radhakrishnan, “Artificial immune based hybrid GA for QoS based multicast routing in large
scale networks (AISMR)”, Computer Communications, Vol. 31, No. 17, pp. 3984–3994, November 20, 2008.
• Carlos A. Coello Coello, “The EMOO repository: a resource for doing research in evolutionary multiobjective
optimization”, IEEE Computational Intelligence Magazine, Vol. 1, No. 1, pp. 37–45, February 2006.
1. Huajin Tang, Vui Ann Shim, Kay Chen Tan and Jun Yong Chia, “Restricted Boltzmann machine based algorithm for
multi-objective optimization”, in 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pp. 3958–3965, IEEE
Press, Barcelona, Spain, July 18–23, 2010.
2. Philipp Limbourg and Hans-Dieter Kochs, “Multi-objective optimization of generalized reliability design problems using
feature models - A concept for early design stages”, Reliability Engineering & System Safety, Vol. 93, No. 6, pp. 815–828,
June 2008.
• Carlos A. Coello Coello, “A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques”, Knowledge and Information Systems, Vol. 1, No. 3, pp. 269–308, August 1999.
1. Handing Wang, Licheng Jiao, Ronghua Shang, Shan He and Fang Liu, “A Memetic Optimization Strategy Based on
Dimension Reduction in Decision Space”, Evolutionary Computation, Vol. 23, No. 1, pp. 69–10, 2015.
2. Feizi E. Ashtiani, M.H. Niksokhan and M. Ardestani, “Multi-objective Waste Load Allocation in River System by
MOPSO Algorithm”, International Journal of Environmental Research, Vol. 9, No. 1, pp. 69–76, Winter 2015.
3. Feifei Dong, Yong Liu, Han Su, Rui Zou and Huaicheng Guo, “Reliability-oriented multi-objective optimal decisionmaking approach for uncertainty-based watershed load reduction”, Science of the Total Environment, Vol 515, pp.
39–48, May 15, 2015.
110
4. M.A. Abido and N.A. Al-Ali, “Multi-Objective Optimal Power Flow Using Differential Evolution”, Arabain Journal for
Science and Engineering, Vol. 37, No. 4, pp. 991–1005, June 2012.
5. Chintalapudi V. Suresh, S. Sivanagaraju and J. Viswanatha Rao, “Multi-area Multi-fuel Economic-Emission Dispatch
Using a Generalized Unified Power Flow Controller Under Practical Constraints”, Arabian Journal for Science and
Engineering, Vol. 40, No. 2, pp. 531–549, February 2015.
6. Kaustuv Nag, Tandra Pal and Nikhil R. Pal, “ASMiGA: An Archive-Based Steady-State Micro Genetic Algorithm”,
IEEE Transactions on Cybernetics, Vol. 45, No. 1, pp. 40–52, January 2015.
7. Sidhartha Panda and Narendra Kumar Yegireddy, “Automatic generation control of multi-area power system using multiobjective non-dominated sorting genetic algorithm-II”, International Journal of Electrical Power & Energy Systems, Vol.
53, pp. 54–63, December 2013.
8. D. Pirouzan, M. Yahyaei and S. Banisi, “Pareto based optimization of flotation cells configuration using an oriented
genetic algorithm”, International Journal of Mineral Processing, Vol. 126, pp. 107–116, January 10, 2014.
9. Nuri Mehmet Gokhan, Kim LaScola Needy and Bryan A. Norman, “Development of a Simultaneous Design for Supply Chain Process for for the Optimization of the Product Design and Supply Chain Configuration Problem”, EMJ–
Engineering Management Journal, Vol. 22, No. 4, pp. 20–30, December 2010.
10. Guofeng Qin, Jia Li, Nan Jiang, Qiyan Li and Lisheng Wang, “Warehouse Optimization Model Based on Genetic
Algorithm”, Mathematical Problems in Engineering, Article Number: 619029, 2013.
11. Karoon Suksonghong, Kittipong Boonlong and Kim-Leng Goh, “Multi-objective genetic algorithms for solving portfolio
optimization problems in the electricity market”, International Journal of Electrical Power & Energy Systems, Vol. 58,
pp. 150–159, June 2014.
12. Yu-Jun Zheng, Hai-Feng Ling, Jin-Yun Xue and Sheng-Yong Chen, “Population Classification in Fire Evacuation: A
Multiobjective Particle Swarm Optimization Approach”, IEEE Transactions on Evolutionary Computation, Vol. 18, No.
1, pp. 70–81, February 2014.
13. Jose-Oscar H. Sendin, Irene Otero-Muras, Antonio A. Alonso and Julio R. Banga, “Improved optimization methods
for the multiobjective design of bioprocesses”, Industrial & Engineering Chemistry Research, Vol. 45, No. 25, pp.
8594–8603, December 6, 2006.
14. H.-B. Jun, M. Cusin, D. Kiritsis and P. Xirouchakis, “A multi-objective evolutionary algorithm for EOL product recovery
optimization: turbocharger case study”, International Journal of Production Research, Vol. 34, Nos. 18-19, pp. 4573–
4594, 2007.
15. Maria Jose Arbiza, Anna Bonfill, Gonzalo Guillen, Fernando D. Mele, Antonio Espuna and Luis Puigjaner, “Metaheuristic multiobjective optimisation approach for the scheduling of multiproduct batch chemical plants”, Journal of Cleaner
Production, Vol. 16, No. 2, pp. 233–244, 2008.
16. Genci Capi, Masao Yokota and Kazuhisa Mitobe, “Optimal multi-criteria humanoid robot gait synthesis - An evolutionary approach”, International Journal of Innovative Computing Information and Control, Vol. 2, No. 6, pp. 1249–1258,
December 2006.
17. Siang Yew Chong, Peter Tino and Xin Yao, “Relationship Between Generalization and Diversity in Coevolutionary
Learning”, IEEE Transactions on Computational Intelligence and AI in Games, Vol. 1, No. 3, pp. 214–232, September
2009.
18. Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopadhyay and Dariusz Plewczynski, “Unsupervised and Supervised
Learning Approaches Together for Microarray Analysis”, Fundamenta Informaticae, Vol. 106, No. 1, pp. 45–73, 2011.
19. B. Latha Shankar, S. Basavarajappa, Rajeshwar S. Kadadevaramath and Jason C.H. Chen, “A bi-objective optimization
of supply chain design and distribution operations using non-dominated sorting algorithm: A case study”, Expert Systems
with Applications, Vol. 40, No. 14, pp. 5730–5739, October 15, 2013.
20. Marjon G.J. de Vos, Frank J. Poelwijk and Sander J. Tans, “Optimality in evolution: new insights from synthetic
biology”, Current Opinion in Biotechnology, Vol. 24, No. 4, pp. 797–802, August 2013.
21. Yan-Fu Li, Nicola Pedroni and Enrico Zio, “A Memetic Evolutionary Multi-Objective Optimization Method for Environmental Power Unit Commitment”, IEEE Transactions on Power Systems, Vol. 28, No. 3, pp. 2660–2669, August
2013.
22. James N. Richardson, Guy Nordenson, Rebecca Laberenne, Rajan Filomeno Coelho and Sigrid Adriaenssens, “Flexible optimum design of a bracing system for facade design using multiobjective Genetic Algorithms”, Automation in
Construction, Vol. 32, pp. 80–87, July 2013.
23. Asif Ekbal and Sriparna Saha, “Simulated annealing based classifier ensemble techniques: Application to part of speech
tagging”, Information Fusion, Vol. 14, No. 3, pp. 288–300, July 2013.
24. P.S. Suresh, G. Radhakrishnan and K. Shankar, “Optimal trends in Manoeuvre Load Control at subsonic and supersonic
flight points for tailless delta wing aircraft”, Aerospace Science and Technology, Vol. 24, No. 1, pp. 128–135, JanuaryFebruary 2013.
111
25. Jacek Widuch, “A Label Correcting Algorithm for the Bus Routing Problem”, Fundamenta Informaticae, Vol. 118, No.
3, pp. 305–326, 2012.
26. G.V. Prasanna Kumar and H. Raheman, “Identification of Optimum Combination of Proportion of Vermicompost in
the soil-based potting mix and pot volume for the production of paper pot seedlings of vegetables”, Journal of Plant
Nutrition, Vol. 35, No. 9, pp. 1277–1289, 2012.
27. Romanas Puisa and Dracos Vassalos, “Robust analysis of cost-effectiveness in formal safety assessment”, Journal of
Marine Science and Technology, Vol. 17, No. 3, pp. 370–381, September 2012.
28. Steffen Limmer, Dietmar Fey and Johannes Jahn, “GPU implementation of a multiobjective search algorithm”, Positivity,
Vol. 16, No. 3, pp. 397–404, September 2012.
29. Adel Lahsasna, Raja Noor Ainon, Roziati Zainuddin and Awang Bulgiba, “Design of a Fuzzy-based Decision Support
System for Coronary Heart Disease Diagnosis”, Journal of Medical Systems, Vol. 36, No. 5, pp. 3293–3306, October
2012.
30. M.H. Wu, W. Lin and S.Y. Duan, “Investigation of a multi-objective optimization tool for engine calibration”, Proceedings
of the Institution of Mechanical Engineers Part D–Journal of Automobile Engineering, Vol. 222, No. D2, pp. 235–249,
February 2008.
31. Rasoul Azizipanah-Abarghooee, Mohammad Rasoul Narimani, Bahman Bahmani-Firouzi and Taher Niknam, “ Modified
shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices”, Journal of Intelligent &
Fuzzy Systems, Vol. 26, No. 2, pp. 681–692, 2014.
32. Angelo Doglioni, Francesco Fiorillo, Francesco Guadagno and Vincenzo Simeone, “Evolutionary polynomial regression
to alert rainfall-triggered landslide reactivation”, Landslides, Vol. 9, No. 1, pp. 53–62, March 2012.
33. Raja Noor Ainon, Awang M. Bulgiba and Adel Lahsasna, “AMI Screening Using Linguistic Fuzzy Rules”, Journal of
Medical Systems, Vol. 36, No. 2, pp. 463–473, April 2012.
34. Antonio J. Nebro, Enrique Alba and Francisco Luna, “Multi-objective optimization using grid computing”, Soft Computing, Vol. 11, No. 6, pp. 531–540, April 2007.
35. Lamia Belfares, Walid Kibi, Nassirou Lo and Adel Guitouni, “Multi-objectives Tabu Search based algorithm for progressive resource allocation”, European Journal of Operational Research, Vol. 177, No. 3, pp. 1779–1799, March 16,
2007.
36. Gang Quan, Garrison W. Greenwood, Donglin Liu and Sharon Hu, “Searching for multiobjective preventive maintenance
schedules: Combining preferences with evolutionary algorithms”, European Journal of Operational Research, Vol. 177,
No. 3, pp. 1969–1984, March 16, 2007.
37. Richard M. Everson and Jonathan E. Fieldsend, “Multiobjective Optimization of Safety Related Systems: An Application
to Short-Term Conflict Alert”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 2, pp. 187–198, April
2006.
38. Charles V. Camp and Andrew Assadollahi, “CO (2) and cost optimization of reinforced concrete footings using a hybrid
big bang-big crunch algorithm”, Structural and Multidisciplinary Optimization, Vol. 48, No. 2, pp. 411–426, August
2013.
39. Piya Chootinan, Anthony Chen and Hai Yang, “A bi-objective traffic counting location problem for origin-destination
trip table estimation”, Transportmetrica, Vol. 1, No. 1, pp. 65–80, 2005.
40. N.C. Hiremath, Sadananda Sahu and Manoj Kuma Tiwari, “Multi objective outbound logistics network design for a
manufacturing supply chain”, Journal of Intelligent Manufacturing, Vol. 24, No. 6, pp. 1071–1084, December 2013.
41. Engin Ufuk Ergul and Ilyas Eminoglu, “DOPGA: a new fitness assignment scheme for multi-objective evolutionary
algorithms”, International Journal of Systems Science, Vol. 45, No. 3, pp. 407–426, March 1, 2014.
42. David A. Van Veldhuizen and Gary B. Lamont. “Multiobjective Evolutionary Algorithms: Analyzing the State-of-theArt”, Evolutionary Computation, Vol. 8, No. 2, pp. 125–147, Summer 2000.
43. Eckart Zitzler, Kalyanmoy Deb & Lothar Thiele, “Comparison of Multiobjective Evolutionary Algorithms: Empirical
Results”, Evolutionary Computation, Vol. 8, No. 2, pp. 173–195, Summer 2000.
44. S. Chamaani, S. A. Mirtaheri, M. Teshnehlab, M. A. Shoorehdeli and V. Seydi, “Modified Multi-objective Particle
Swarm Optimization for electromagnetic absorber design”, Progress In Electromagnetics Research, PIER, Vol. 79, pp.
353–366, 2008.
45. Anirban Mukhopadhyay and Ujjwal Maulik, “Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy
Clustering Approach”, Fundamenta Informaticae, Vol. 86, No. 4, pp. 411–428, 2008.
46. Sanghamitra Bandyopadhyay, Ujjwal Maulik and Anirban Mukhopadhyay, “Multiobjective genetic clustering for pixel
classification in remote sensing imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 5, pp.
1506–1511, May 2007.
112
47. Siang Yew Chong, Peter Tino and Xin Yao, “Relationship Between Generalization and Diversity in Coevolutionary
Learning”, IEEE Transactions on Computational Intelligence and AI in Games, Vol. 1, No. 3, pp. 213–232, September
2009.
48. B. Palancz and J.L. Awange, “Pareto optimality solution of the multi-objective photogrammetric resection-intersection
problem”, Earth Science Informatics, Vol. 6, No. 1, pp. 1–20, March 2013.
49. Robin Chhabra and M. Reza Emami, “A holistic concurrent design approach to robotics using hardware-in-the-loop
simulation”, Mechatronics, Vol. 23, No. 3, pp. 335–345, April 2013.
50. F. Manzano-Agugliaro, C. San-Antonio-Gomez, S. Lopez, F.G. Montoya and C. Gil, “Pareto-based evolutionary algorithms for the calculation of transformation parameters and accuracy assessment of historical maps”, Computers &
Geosciences, Vol. 57, pp. 124–132, August 2013.
51. Huantong Geng, Haifeng Zhu, Rui Xing and Tingting Wu, “A Novel Hybrid Evolutionary Algorithm for Solving MultiObjective Optimization Problems”, in De-Shuang Huang, Changjun Jiang, Vitoantonio Bevilacqua and Juan Carlos
Figueroa (editors), Intelligent Computing Technology, 8th International Conference, ICIC 2012, pp. 128–136, Springer.
Lecture Notes in Computer Science Vol. 7389, Huangshan, China, July 25-29, 2012.
52. M.A. Abido and N.A. Al-Ali, “Multi-Objective Optimal Power Flow Using Differential Evolution”, Arabian Journal for
Science and Engineering, Vol. 37, No. 4, pp. 991–1005, June 2012.
53. Deogratias Nurwahaa and Xinhou Wang, “Optimization of electrospinning process using intelligent control systems”,
Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 593–600, 2013.
54. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
55. Salman A. Khan and Shafiqur Rehman, “Iterative non-deterministic algorithms in on-shore wind farm design: A brief
survey”, Renewable & Sustainable Energy Reviews, Vol. 19, pp. 370–384, March 2013.
56. Hemant Kumar Singh, Tapabrata Ray and Ruhul Sarker, “Optimum Oil Production Planning Using Infeasibility Driven
Evolutionary Algorithm”, Evolutionary Computation, Vol. 21, No. 1, pp. 65–82, Spring 2013.
57. B. Palancz and J.L. Awange, “Pareto optimality solution of the multi-objective photogrammetric resection-intersection
problem”, Earth Science Informatics, Vol. 6, No. 1, pp. 1–20, March 2013.
58. Nikos Giannopoulos, Vasilis C. Moulianitis and Andreas C. Nearchou, “Multi-objective optimization with fuzzy measures
and its application to flow-shop scheduling”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 7, pp. 1381–
1394, October 2012.
59. Bruce A. Rosa, Ji Zhang, Ian T. Major, Wensheng Qin and Jin Chen, “Optimal timepoint sampling in high-throughput
gene expression experiments”, Bioinformatics, Vol. 28, No. 21, pp. 2773–2781, November 1, 2012.
60. Asif Ekbal and Sriparna Saha, “Combining feature selection and classifier ensemble using a multiobjective simulated
annealing approach: application to named entity recognition”, Soft Computing, Vol. 17, No. 1, pp. 1–16, January 2013.
61. Asif Ekbal and Sriparna Saha, “Multiobjective optimization for classifier ensemble and feature selection: an application
to named entity recognition”, International Journal on Document Analysis and Recognition, Vol. 15, No. 2, pp. 143–166,
June 2012.
62. L.C. Jiao, Handing Wang, R.H. Shang and F. Liu, “A co-evolutionary multi-objective optimization algorithm based on
direction vectors”, Information Sciences, Vol. 228, pp. 90–112, April 10, 2013.
63. Anirban Mukhopadhyay, Sumanta Ray and Moumita De, “Detecting protein complexes in a PPI network: a gene
ontology based multi-objective evolutionary approach”, Molecular Biosystems, Vol. 8, No. 11, pp. 3036–3048, 2012.
64. B. Latha Shankar, S. Basavarajappa, Jason C.H. Chen and Raheshwar S. Kadadevaramath, “Location and allocation
decisions for multi-echelon supply chain network - A multi-objective evolutionary approach”, Expert Systems with Applications, Vol. 40, No. 2, pp. 551–562, February 1, 2013.
65. Houssem Ben Aribia, Nizar Derbel and Hsan Hadj Abdallah, “The active-reactive - Complete dispatch of an electrical
network”, International Journal of Electrical Power & Energy Systems, Vol. 44, No. 1, pp. 236–248, January 2013.
66. A. Garcia-Piquer, A. Fornells, A. Orriols-Puig, G. Corral and E. Golobardes, “Data classification through an evolutionary
approach based on multiple criteria”, Knowledge and Information Systems, Vol. 33, No. 1, pp. 35–56, October 2012.
67. James N. Richardson, Sigrid Adriaenssens, Philippe Bouillard and Rajan Filomeno Coelho, “Multiobjective topology
optimization of truss structures with kinematic stability repair”, Structural and Multidisciplinary Optimization, Vol. 46,
No. 4, pp. 513–532, October 2012.
68. Chin Wei Bong, Hong Yoong Lam, Ahamad Tajudin Khader and Hamzah Kamarulzaman, “Adaptive multi-objective
archive-based hybrid scatter search for segmentation in lung computed tomography imaging”, Engineering Optimization,
Vol. 44, No. 3, pp. 327–350, 2012.
69. Wali Khan Mashwani and Abdellah Salhi, “A decomposition-based hybrid multiobjective evolutionary algorithm with
dynamic resource allocation”, Applied Soft Computing, Vol. 12, No. 9, pp. 2765–2780, September 2012.
113
70. Asif Ekbal and Sriparna Saha, “A multiobjective simulated annealing approach for classifier ensemble: Named entity
recognition in Indian languages as case studies”, Expert Systems with Applications, Vol. 38, No. 12, pp. 14760–14772,
November-December 2011.
71. Zuwairie Ibrahim, Noor Khafifah Khalid, Jameel Abdulla Ahmed Mukred, Salinda Buyamin, Zulkifli Md. Yusof, Muhammad Faiz Mohamed Saaid, N. Mokhtar and Andries R. Engelbrecht, “A DNA Sequence Design for DNA Computation
Based on Binary Vector Evaluated Particle Swarm Optimization”, International Journal of Unconventional Computing,
Vol. 8, No. 2, pp. 119–137, 2012.
72. Rodrigo Coelho Barros, Marcio Porto Basgalupp, Andre C.P.L.F. de Carvalho and Alex A. Freitas, “A Survey of
Evolutionary Algorithms for Decision-Tree Induction”, IEEE Transactions on Systems, Man and Cybernetics Part C–
Applications and Reviews, Vol. 42, No. 3, pp. 291–312, May 2012.
73. Mathieu Balesdent, Nicolas Berend, Philippe Depince and Abdelhamid Chriette, “A survey of multidisciplinary design
optimization methods in launch vehicle design”, Structural and Multidisciplinary Optimization, Vol. 45, No. 5, pp.
619–642, May 2012.
74. Rory Clune, Jerome J. Connor, John A. Ochsendorf and Denis Kelliher, “An object-oriented architecture for extensible
structural design software”, Computers & Structures, Vol. 100, pp. 1–17, June 2012.
75. Davide Bianchi, Simone Genovesi and Agostino Monorchio, “Constrained Pareto Optimization of Wide Band and Steerable Concentric Ring Arrays”, IEEE Transactions on Antennas and Propagation, Vol. 60, No. 7, pp. 3195–3204, July
2012.
76. B. Palancz and J.L. Awange, “Application of Pareto optimality to linear models with errors-in-all-variables”, Journal of
Geodesy, Vol. 86, No. 7, pp. 531–545, July 2012.
77. Bin Huang, Ke Xing, Kazem Abhary and Sead Spuzic, “Optimization of oval-round pass design using genetic algorithm”,
Robotics and Computer-Integrated Manufacturing, Vol. 28, No. 4, pp. 493–499, August 2012.
78. Federico Divina, Beatriz Pontes, Raul Giraldez and Jesus S. Aguilar-Ruiz, “An effective measure for assessing the quality
of biclusters”, Computers in Biology and Medicine, Vol. 42, No. 2, pp. 245–256, February 2012.
79. Soumi Sengupta and Sanghamitra Bandyopadhyay, “De Novo Design of Potential RecA Inhibitors Using MultiObjective
Optimization”, IEEE-ACM Transactions on Computational Biology and Bioinformatics, Vol. 9, No. 4, pp. 1139–1154,
July-August 2012.
80. Helon Vicente Hultmann Ayala and Leandro dos Santos Coelho, “Tuning of PID controller based on a multiobjective
genetic algorithm applied to a robotic manipulator”, Expert Systems with Applications, Vol. 39, No. 10, pp. 8968–8974,
August 2012.
81. Parames Chutima and Palida Chimklai, “Multi-objective two-sided mixed-model assembly line balancing using particle
swarm optimisation with negative knowledge”, Computers & Industrial Engineering, Vol. 62, No. 1, pp. 39–55, February
2012.
82. Juan Jose Valera Garcia, Vicente Garay, Eloy Irigoyen Gordo, Fernando Artaza Fano and Mikel Larrea Sukia, “Intelligent
Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC): Towards the ‘on-line’ optimization of highly complex
control problems”, Expert Systems with Applications, Vol. 39, No. 7, pp. 6527–6540, June 1, 2012.
83. Kent McClymont and Ed Keedwell, “Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting”,
Evolutionary Computation, Vol. 20, No. 1, pp. 1–26, Spring 2012.
84. Katharina Morik, Andreas Kaspari, Michael Wurst and Marcin Skirzynski, “Multi-objective frequent termset clustering”,
Knowledge and Information Systems, Vol. 30, No. 3, pp. 715–738, March 2012.
85. Adel Lahsasna, Raja N. Ainon and Teh Y. Wah, “Enhancement of transparency and accuracy of credit scoring models
through genetic fuzzy classifier”, Maejo International Journal of Science and Technology, Vol. 4, No. 1, pp. 136–158,
January-April 2010.
86. Edward P. Manning, “Using Resource-Limited Nash Memory to Improve an Othello Evaluation Function”, IEEE Transactions on Computational Intelligence and AI in Games, Vol. 2, No. 1, pp. 40–53, March 2010.
87. Fahimeh Jafari, Zhonghai Lu, Axel Jantsch and Mohammad Hossein Yaghmaee, “Buffer Optimization in Network-onChip Through Flow Regulation”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,
Vol. 29, No. 12, pp. 1973–1986, December 2010.
88. Ata-Ul-Waheed and A.R. Baig, “Michigan versus Pittsburg Approach: A Comparison for Market Selection Problem”,
International Journal of Innovative Computing Information and Control, Vol. 8, No. 1A, pp. 13–32, January 2012.
89. M. Mahfouf, M. Jamei, D.A. Linkens and J. Tenner, “Inverse modelling for optimal metal design using fuzzy specified
multi-obective fitness unctions”, Control Engineering Practice, Vol. 16, No. 2, pp. 179–191, February 2008.
90. Vincent Kelner, Florin Capitanescu, Olivier Uonard and Louis Wehenkel, “A hybrid optimization technique coupling an
evolutionary and a local search algorithm”, Journal of Computational and Applied Mathematics, Vol. 215, No. 2, pp.
448–456, June 1, 2008.
114
91. Andrew Kusiak and Filippo A. Salustri, “Computational intelligence in product design engineering: Review and trends”,
IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 37, No. 5, pp. 766–778,
September 2007.
92. C.W. Bong and M. Rajeswari, “Multiobjective clustering with metaheuristic: current trends and methods in image
segmentation”, IET Image Processing, Vol. 6, No. 1, pp. 1–10, February 2012.
93. Ashraf Elazouni and Mohammad Abido, “Multiobjective evolutionary finance-based scheduling: Individual projects
within a portfolio”, Automation in Construction, Vol. 20, No. 7, pp. 755–766, November 2011.
94. Taher Niknam, Mohammad Rasoul Narimani, Masoud Jabbari and Admad Reza Malekpour, “A modified shuffle frog
leaping algorithm for multi-objective optimal power flow”, Energy, Vol. 36, No. 11, pp. 6420–6432, November 2011.
95. Amjad Anvari Moghaddam, Alireza Seifi, Taher Niknam and Mohammad Reza Alizadeh Pahlavani, “Multi-objective
operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power
source”, Energy, Vol. 36, No. 11, pp. 6490–6507, November 2011.
96. Rasmus K. Ursem and Peter Dueholm Justesen, “Multi-objective Distinct Candidates Optimization: Locating a few
highly different solutions in a circuit component sizing problem”, Applied Soft Computing, Vol. 12, No. 1, pp. 255–265,
January 2012.
97. Romanas Puisa and Heinrich Streckwall, “Prudent constraint-handling technique for multiobjective propeller optimisation”, Optimization and Engineering, Vol. 12, No. 4, pp. 657–680, December 2011.
98. Hans-Friedrich Köhn, “A review of multiobjective programming and its application in quantitative psychology”, Journal
of Mathematical Psychology, Vol. 55, No. 5, pp. 386–396, October 2011.
99. Leandro dos Santos Coelho, Helon Vicente Hultmann Ayala and Piergiorgio Alotto, “A Multiobjective Gaussian Particle
Swarm Approach Applied to Electromagnetic Optimization ”, IEEE Transactions on Magnetics, Vol. 46, No. 8, pp.
3289–3292, August 2010.
100. Xiang Shen and Zhonghua Ni, “Multi-Objective Design Optimization of Coronary Stent Mechanical Properties”, Advanced Science Letters, Vol. 4, No. 3, pp. 835–838, March 2011.
101. Chung-Ho Wang and Cheng-Hsiang Li, “Optimization of an established multi-objective delivering problem by an improved hybrid algorithm”, Expert Systems with Applications, Vol. 38, No. 4, pp. 4361–4367, April 2011.
102. Constanta Zoie Radulescu and Magdalena Turek Rahoveanu, “A Multi-Criteria Evaluation Framework for Fish Farms”,
Studies in Informatics and Control, Vol. 20, No. 2, pp. 181–186, June 2011.
103. Sidhartha Panda, “Multi-objective PID controller tuning for a FACTS-based damping stabilizer using Non-dominated
Sorting Genetic Algorithm-II”, International Journal of Electrical Power & Energy Systems, Vol. 33, No. 7, pp. 1296–
1308, September 2011.
104. Abolfazl Khalkhali, Mohamadhosein Sadafi, Javad Rezapour and Hamed Safikhani, “Pareto based Multi-Objective
Optimization of Solar Thermal Energy Storage using Genetic Algorithms”, Transactions of the Canadian Society for
Mechanical Engineering, Vol. 34, Nos. 3–4, pp. 463–474, 2010.
105. M. Khorshidi, M. Soheilypour, M. Peyro, A. Atai and M. Shariat Panahi, “Optimal design of four-bar mechanisms
using a hybrid multi-objective GA with adaptive local search”, Mechanism and Machine Theory, Vol. 46, No. 10, pp.
1453–1465, October 2011.
106. Francisco Reyes, Narciso Cerpa, Alfredo Candia-Vejar and Matthew Bardeen, “The optimization of success probability
for software projects using genetic algorithms”, Journal of Systems and Software, Vol. 84, No. 5, pp. 775–785, May
2011.
107. Jose Elias Claudio Arroyo and Ana Amelia de Souza Pereira, “A GRASP heuristic for the multi-objective permutation
flowshop scheduling problem”, International Journal of Advanced Manufacturing Technology, Vol. 55, Nos. 5-8, pp.
741–753, July 2011.
108. Dennis L.A.G. Grimminck, Suresh K. Vasa, W. Leo Meerts, Arno P.M. Kentgens and Andreas Brinkmann, “EASYGOING DUMBO on-spectrometer optimisation of phase modulated homonuclear decoupling sequences in solid-state
NMR”, Chemical Physics Letters, Vol. 509, Nos. 4-6, pp. 186–191, June 14, 2011.
109. Chi Zhang, Jose Emmanuel Ramirez-Marquez and Claudio M. Rocco Sanseverino, “A holistic method for reliability
performance assessment and critical components detection in complex networks”, IIE Transactions, Vol. 43, No. 9, pp.
661–675, 2011.
110. Zhixiang Fang, Xinlu Zong, Qingquan Li, Qiuping Li and Shengwu Xiong, “Hierarchical multi-objective evacuation
routing in stadium using ant colony optimization approach”, Journal of Transport Geography, Vol. 19, No. 3, pp.
443–451, May 2011.
111. Chin-Wei Bong and Mandava Rajeswari, “Multi-objective nature-inspired clustering and classification techniques for
image segmentation”, Applied Soft Computing, Vol. 11, No. 4, pp. 3271–3282, June 2011.
112. Alireza Behroozsarand and Sirous Shafiei, “Optimal control of distillation column using Non-Dominated Sorting Genetic
Algorithm-II”, Journal of Loss Prevention in the Process Industries, Vol. 24, No. 1, pp. 25–33, January 2011.
115
113. Iain Bate and Usman Khan, “WCET analysis of modern processors using multi-criteria optimisation”, Empirical Software
Engineering, Vol. 16, No. 1, pp. 5–28, February 2011.
114. Shu-Hsien Liao, Chia-Lin Hsieh and Peng-Jen Lai, “An evolutionary approach for multi-objective optimization of the
integrated location-inventory distribution network problem in vendor-managed inventory”, Expert Systems with Applications, Vol. 38, No. 6, pp. 6768–6776, June 2011.
115. Santosh Tiwari, Georges Fadel and Kalyanmoy Deb, “AMGA2: improving the performance of the archive-based microgenetic algorithm for multi-objective optimization”, Engineering Optimization, Vol. 43, No. 4, pp. 377–401, 2011.
116. Indrajit Saha, Ujjwal Maulik and Dariusz Plewczynski, “A new multi-objective technique for differential fuzzy clustering”,
Applied Soft Computing, Vol. 11, No. 2, pp. 2765–2776, March 2011.
117. M. Basu, “Economic environmental dispatch of fixed head hydrothermal power systems using nondominated sorting
genetic algorithm-II”, Applied Soft Computing, Vol. 11, No. 3, pp. 3046–3055, April 2011.
118. Jing Chen, Yan Lin, Junzhou Huo, Mingxia Zhang and Zhuoshang Ji, “Optimization of Ships’ Diagonal Ballast Water
Exchange Sequence Using a Multiobjective Genetic Algorithm”, Journal of Ship Research, Vol. 54, No. 4, pp. 257–267,
December 2010.
119. Hossein Ghiasi, Damiano Pasini and Larry Lessard, “A non-dominated sorting hybrid algorithm for multi-objective
optimization of engineering problems”, Engineering Optimization, Vol. 43, No. 1, pp. 39–59, January 2011.
120. M.A. Abido and Ashraf M. Elazouni, “Multiobjective Evolutionary Finance-Based Scheduling: Entire Projects’ Portfolio”, Journal of Computing in Civil Engineering, Vol. 25, No. 1, pp. 85–97, January-February 2011.
121. A.C. Nearchou, “Mufti-objective balancing of assembly lines by population heuristics”, International Journal of Production Research, Vol. 46, No. 8, pp. 2275–2297, April 15, 2008.
122. Yijun He, Dezhao Chen and Weixiang Zhao, “Integrated method of compromise-based ant colony algorithm and rough
set theory and its application in toxicity mechanism classification”, Chemometrics and Intelligent Laboratory Systems,
Vol. 92, No. 1, pp. 22–32, May 15, 2008.
123. Gisele L. Pappa and Alex A. Freitas, “Evolving rule induction algorithms with multi-objective grammar-based genetic
programming”, Knowledge and Information Systems, Vol. 19, No. 3, pp. 283–309, June 2009.
124. Ujjwal Maulik, Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay, “Finding Multiple Coherent Biclusters in
Microarray Data Using Variable String Length Multiobjective Genetic Algorithm”, IEEE Transactions on Information
Technology in Biomedicine, Vol. 13, No. 6, pp. 969–975, November 2009.
125. Jose Emmanuel Ramirez-Marquez and Claudio M. Rocco, “Evolutionary optimization technique for multi-state twoterminal reliability allocation in multi-objective problems”, IIE Transactions, Vol. 42, No. 8, pp. 539–552, 2010.
126. Jessica A. Carballido, Ignacio Ponzoni and Nelida B. Brignole, “CGD-GA: A graph-based genetic algorithm for sensor
network design”, Information Sciences, Vol. 177, No. 22, pp. 5091–5102, November 15, 2007.
127. Claudio M. Rocco S., Jose Emmanuel Ramirez-Marquez and Daniel E. Salazar A., “Bi and tri-objective optimization in
the deterministic network interdiction problem”, Reliability Engineering & System Safety, Vol. 95, No. 8, pp. 887–896,
August 2010.
128. Claudio M. Rocco S. and Jose Emmanuel Ramirez-Marquez, “A bi-objective approach for shortest-path network interdiction”, Computers & Industrial Engineering, Vol. 59, No. 2, pp. 232–240, September 2010.
129. D. Strnad and N. Guid, “A fuzzy-genetic decision support system for project team formation”, Applied Soft Computing,
Vol. 10, No. 4, pp. 1178–1187, September 2010.
130. Paraskevi S. Georgiadou, Ioannis A. Papazoglou, Chris T. Kiranoudis and Nikolaos C. Markatos, “Multi-objective
evolutionary emergency response optimization for major accidents”, Journal of Hazardous Materials, Vol. 178, Nos. 1-3,
pp. 792–803, June 15, 2010.
131. Srikanth Vadde, Abe Zeid and Sagar V. Kamarthi, “Pricing decisions in a multi-criteria setting for product recovery
facilities”, Omega–International Journal of Management Science, Vol. 39, No. 2, pp. 186–193, April 2011.
132. Guilherme P. Coelho, Ana Estela A. da Silva and Fernando J. Von Zuben, “An immune-inspired multi-objective approach
to the reconstruction of phylogenetic trees”, Neural Computing & Applications, Vol. 19, No. 8, pp. 1103–1132, November
2010.
133. Thiago Quirino, Miroslav Kubat and Nicholas J. Bryan, “Instinct-Based Mating in Genetic Algorithms Applied to the
Tuning of 1-NN Classifiers”, IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 12, pp. 1724–1737,
December 2010.
134. Jing Chen, Yan Lin, Jun Zhou Huo, Ming Xia Zhang and Zhuo Shang Ji, “Optimal ballast water exchange sequence
design using symmetrical multitank strategy”, Journal of Marine Science and Technology, Vol. 15, No. 3, pp. 280–293,
September 2010.
135. Gideon Avigad and Amiram Moshaiov, “Simultaneous concept-based evolutionary multi-objective optimization”, Applied
Soft Computing, Vol. 11, No. 1, pp. 193–207, January 2011.
116
136. M.A. Abido, “Multiobjective particle swarm optimization with nondominated local and global sets”, Natural Computing,
Vol. 9, No. 3, pp. 747–766, September 2010.
137. Giuseppe Carlo Marano, Giuseppe Quaranta and Sara Sgobba, “Fuzzy-entropy based robust optimization criteria for
tuned mass dampers”, Earthquake Engineering and Engineering Vibration, Vol. 9, No. 2, pp. 285–294, June 2010.
138. Angelo Doglioni, Davide Mancarella, Vincenzo Simeone and Orazio Giustolisi, “Inferring groundwater system dynamics
from hydrological time-series data”, Hydrological Sciences Journal–Journal des Sciences Hydrologiques, Vol. 55, No. 4,
pp. 593–608, 2010.
139. P.K. Hota, A.K. Barisal and R. Chakrabarti, “Economic emission load dispatch through fuzzy based bacterial foraging
algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 32, No. 7, pp. 794–803, September 2010.
140. Santosh Tiwari, Georges Fadel and Peter Fenyes, “A Fast and Efficient Compact Packing Algorithm for SAE and ISO
Luggage Packing Problems”, Journal of Computing and Information Science in Engineering, Vol. 10, No. 2, Article
Number 021010, June 2010.
141. Sidhartha Panda, “Application of non-dominated sorting genetic algorithm-II technique for optimal FACTS-based controller design”, Journal of the Franklin Institute–Engineering and Applied Mathematics, Vol. 347, No. 7, pp. 1047–1064,
September 2010.
142. M.T. Yazdani Sabouni, F. Jolai and A. Mansouri, “Heuristics for minimizing total completion time and maximum
lateness on identical parallel machines with setup times”, Journal of Intelligent Manufacturing, Vol. 21, No. 4, pp.
439–449, August 2010.
143. K. Salmalian, N. Nariman-Zadeh, H. Gharababei, H. Haftchenari and A. Varvani-Farahani, “Multi-objective evolutionary
optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibrereinforced plastics composites”, Proceedings of the Institution of Mechanical Engineers Part L–Journal of MaterialsDesign and Applications, Vol. 224, No. L2, pp. 79–91, 2010.
144. M. Basu, “Economic environmental dispatch of hydrothermal power system”, International Journal of Electrical Power
& Energy Systems, Vol. 32, No. 6, pp. 711–720, July 2010.
145. N. Nariman-Zadeh, M. Salehpour, A. Jamali and E. Haghgoo, “Pareto optimization of a five-degree of freedom vehicle
vibration model using a multi-objective uniform-diversity genetic algorithm (MUGA)”, Engineering Applications of
Artificial Intelligence, Vol. 23, No. 4, pp. 543–551, June 2010.
146. Banu Soylu and Murat Koksalan, “A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems”,
IEEE Transactions on Evolutionary Computation, Vol. 14, No. 2, pp. 191–205, April 2010.
147. Jose Oscar H. Sendin, Antonio A. Alonso and Julio R. Banga, “Efficient and robust multi-objective optimization of food
processing: A novel approach with application to thermal sterilization”, Journal of Food Engineering, Vol. 98. No. 3,
pp. 317–324, June 2010.
148. D. Sarkar and J.M. Modak, “Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using
nondominated sorting genetic algorithm”, Chemical Engineering Science, Vol. 60, No. 2, pp. 481–492, January 2005.
149. Talib Hussain, David Montana and Gordon Vidaver, “Evolution-Based Deliberative Planning for Cooperating Unmanned Ground Vehicles in a Dynamic Environment”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary
Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part II, SpringerVerlag, Lecture Notes in Computer Science Vol. 3103, pp. 1017–1029, Seattle, Washington, USA, June 2004.
150. B. Baran, C. von Lucken and A. Sotelo, “Multi-objective pump scheduling optimisation using evolutionary strategies”,
Advances in Engineering Software, Inglaterra, Vol. 36, No. 1, pp. 39–47, January 2005.
151. E.J. Solteiro Pires, J.A. Tenreiro Machado and P.B. de Moura Oliveira, “Robot Trajectory Planning Using Multiobjective
Genetic Algorithm Optimization”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO
2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part I, Springer-Verlag, Lecture Notes in
Computer Science Vol. 3102, pp. 615–626, Seattle, Washington, USA, June.
152. M.A. Abido, J.M. Bakhashwain, “Optimal VAR dispatch using a multiobjective evolutionary algorithm”, International
Journal of Electrical Power & Energy Systems, Vol. 27, No. 1, pp. 13–20, January 2005.
153. Vinı́cius Amaral Armentano and José Elias Claudio, “An Application of a Multi-Objective Tabu Search Algorithm to a
Bicriteria Flowshop Problem”, Journal of Heuristics, Vol. 10, No. 5, pp. 463–481, September 2004.
154. Giuseppe Ascia, Vincenzo Catania and Maurizio Palesi, “A GA-Based Design Space Exploration Framework for Parameterized System-On-A-Chip Platforms”, IEEE Transactions on Evolutionary Computation, Vol. 8, No. 4, pp. 329–346,
August 2004.
155. Ruhul Sarker and Hussein A. Abbass, “Differential evolution for solving multiobjective optimization problems”, AsiaPacific Journal of Operational Research, Vol. 21, No. 2, pp. 225–240, June 2004.
156. I. Alberto and P.M. Mateo, “Representation and management of MOEA populations based on graphs”, European Journal
of Operational Research, Vol. 159, No. 1, pp. 52–65, November 2004.
117
157. V. Kelner and O. Leonard, “Application of genetic algorithms to lubrication pump stacking design”, Journal of Computational and Applied Mathematics, Vol. 168, Nos. 1–2, pp. 255–265, July 1, 2004.
158. A. Ghosh and B. Nath, “Multi-objective rule mining using genetic algorithms”, Information Sciences, Vol. 163, Nos.
1–3, pp. 123–133, June 14, 2004.
159. M. Nemec, D.W. Zingg, T.H. Pulliam, “Multipoint and multi-objective aerodynamic shape optimization”, AIAA Journal,
Vol. 42, No. 6, pp. 1057–1065, June 2004.
160. Eduardo José Solteiro Pires, Paulo B. de Moura Oliveira and José António Tenreiro Machad, “Multi-objective Genetic
Manipulator Trajectory Planner”, in Günther R. Raidl et al. (editors), Applications of Evolutionary Computing. Proceedings of Evoworkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Springer.
Lecture Notes in Computer Science, Volume 3005, pp. 219–229, Coimbra, Portugal, April 2004.
161. G. Papa, “An evolutionary approach to chip design: An empirical evaluation”, Informacije Midem–Journal of Microelectronics electronic components and materials, Vol. 33, No. 3, pp. 142–148, September 2003.
162. M. Solimanpur, P. Vrat and R. Shankar, “A multi-objective genetic algorithm approach to the design of cellular manufacturing systems”, International Journal of Production Research, Vol. 42, No. 7, pp. 1419–1441, April 1, 2004.
163. Eduardo Fernández and Juan Carlos Leyva, “A method based on multiobjective optimization for deriving a ranking
from a fuzzy preference relation”, European Journal of Operational Research, Vol. 154, Issue 1, pp. 110–124, April 2004.
164. F. Viguier and H. Pierreval, “An approach to the design of a hybrid organization of workshops into functional layout
and group technology cells”, International Journal of Computer Integrated Manufacturing, Vol. 17, No. 2, pp. 108–116,
March 2004.
165. M.A. Abido, “Environmental/Economic Power Dispatch using Multiobjective Evolutionary Algorithms”, IEEE Transactions on Power Systems, Vol. 18, No. 4, pp. 1529–1537, November 2003.
166. G.M.B. Oliveira, O.K.N. Asakura and P.P.B. de Oliveira, “Coevolutionary search for one-dimensional cellular automata,
based on parameters related to their dynamic behaviour” Journal of Intelligent & Fuzzy Systems, Vol. 13, Nos. 2–4, pp.
99–110, 2002.
167. Mikkel T. Jensen, “Reducing the Run-Time Complexity of Multiobjective EAs: The NSGA-II and Other Algorithms”,
IEEE Transactions on Evolutionary Computation, Vol. 7, No. 5, pp. 503–515, October 2003.
168. Balram Suman, “Simulated Annealing-Based Multiobjective Algorithms and Their Application for System Reliability”,
Engineering Optimization, Vol. 35, No. 4, pp. 391–416, August 2003.
169. H.A. Abbass, “Speeding up backpropagation using multiobjective evolutionary algorithms”, Neural Computation, Vol.
15, No. 11, pp. 2705–2726, November 2003.
170. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical design with constraints and preferences: application
to a purge valve”, Computer Methods in Applied Mechanics and Engineering, Vol. 192, Nos. 39–40, pp. 4355–4378,
2003.
171. M.P. Sanchez and J.A. Almansa, “A real application example of a control structure selection by means of a multiobjective
genetic algorithm”, in Artificial Neural Nets Problem Solving Methods, Part II, Springer, Lecture Notes in Computer
Science, Volume 2687, pp. 369–376, 2003.
172. Carlos A. Brizuela and Rodrigo Aceves, “Experimental Genetic Operators Analysis for the Multi-objective Permutation
Flowshop”, in Carlos M. Fonseca, Peter J. Fleming, Eckart Zitzler, Kalyanmoy Deb and Lothar Thiele (editors), Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, pp. 578–592, Springer. Lecture
Notes in Computer Science. Volume 2632, Faro, Portugal, April 2003.
173. R.M. Hubley, E. Zitzler and J.C. Roach, “Evolutionary algorithms for the selection of single nucleotide polymorphisms”,
BMC Bioinformatics, Inglaterra, Vol. 4, Art. No. 30, July 23, 2003.
174. Y.L. Abdel-Magid and M.A. Abido, “Optimal multiobjective design of robust power system stabilizers using genetic
algorithms”, IEEE Transactions on Power Systems, Vol. 18, No. 3, pp. 1125–1132, August 2003.
175. Y.H. Kang and Z. Bien, “Introduction of a new concept, age, into the multiobjective evolutionary algorithm in the two
dimensional space”, IEICE Transactions on Information and Systems, Vol. E86D, No. 7, pp. 1304–1309, July 2003.
176. Jonathan E. Fieldsend, Richard M. Everson and Sameer Singh, “Using Unconstrained Elite Archives for Multiobjective
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 7, No. 3, pp. 305–323, June 2003.
177. Peter A.N. Bosman and Dirk Thierens, “The Balance Between Proximity and Diversity in Multiobjective Evolutionary
Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 7, No. 2, pp. 174–188, April 2003.
178. Hisao Ishibuchi, Tadashi Yoshida and Tadahiko Murata, “Balance Between Genetic Search and Local Search in Memetic
Algorithms for Multiobjective Permutation Flowshop Scheduling”, IEEE Transactions on Evolutionary Computation,
Estados Unidos, Vol. 7, No. 2, pp. 204–223, April 2003.
179. Andrés L. Medaglia and Shu-Chern Fang, “A genetic-based framework for solving (multi-criteria) weighted matching
problems”, European Journal of Operational Research, Vol. 149, No. 1, pp. 77–101, August 2003.
118
180. M.A. Abido, “A novel multiobjective evolutionary algorithm or environmental/economic power dispatch”, Electric Power
Systems Research, Vol. 65, No. 1, pp. 71–81, April 2003
181. K.C. Tan, E.F. Khor, T.H. Lee and R. Sathikannan, “An evolutionary algorithm with advanced goal and priority
specification for multi-objective optimization”, Journal of Artificial Intelligence Research, Vol. 18, pp. 183–215, 2003.
182. B.J. Reynolds and S. Azarm, “A multi-objective heuristic-based hybrid genetic algorithm”, Mechanics of Structures and
Machines, Vol. 30, No. 4, pp. 463–491, 2002.
183. P.A.N. Bosman and D. Thierens, “Multi-objective optimization with diversity preserving mixture-based iterated density
estimation evolutionary algorithms”, International Journal of Approximate Reasoning, Vol. 31, No. 3, pp. 259–289,
November 2002.
184. A. Herreros, E. Baeyens and J.R. Peran, “MRCD: A Genetic Algorithm for Multiobjective Robust Control Design”,
Engineering Applications of Artificial Intelligence, Vol. 15, Nos. 3–4, pp. 285–301, June-August 2002.
185. Eduardo Fernández and Jorge Navarro, “A Genetic Search for Exploiting a Fuzzy Preference Model of Portfolio Problems
with Public Projects”, Annals of Operations Research, Vol. 117, Nos. 1–4, pp. 191–213, November 2002.
186. P.J. Fleming and R.C. Purshouse, “Evolutionary algorithms in control systems engineering: a survey”, Control Engineering Practice, Vol. 10, No. 11, pp. 1223–1241, November 2002.
187. M.A. Abido, “A Niched Pareto Genetic Algorithm for Multiobjective Environmental/Economic Dispatch”, International
Journal of Electrical Power & Energy Systems, Vol. 25, No. 2, pp. 97–105, February 2003.
188. V.S. Summanwar, V.K. Jayaraman, B.D. Kulkarni, H.S. Kusumakar, K. Gupta, and J. Rajesh, “Solution of constrained
optimization problems by multi-objective genetic algorithm”, Computers and Chemical Engineering, Vol. 26, No. 10,
pp. 1481–1492, October 15, 2002.
189. Enrique Alba and Marco Tomassini, “Parallelism and Evolutionary Algorithms”, IEEE Transactions on Evolutionary
Computation, Vol. 6, No. 5, pp. 443–462, October 2002.
190. A. Herreros, E. Baeyens and J.R. Peran, “Design of PID-type controllers using multiobjective genetic algorithms”, ISA
Transactions, Vol. 41, No. 4, pp. 457–472, October 2002.
191. Pasanth B. Nair and Andrew J. Keane, ”A Coevolutionary Architecture for Distributed Optimization of Complex
Coupled Systems”, AIAA Journal, Vol. 40, No. 7, pp. 1434–1443, July 2002.
192. K.C. Tan, T.H. Lee and E.F. Khor, “Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons”, Artificial Intelligence Review, Vol. 17, No. 4, pp. 253–290, June 2002.
193. M.S. Levin, “Towards combinatorial analysis, adaptation, and planning of human-computer systems”, Applied Intelligence, Vol. 16, No. 3, pp. 235–247, May-June 2002.
194. Yaochu Jin, Tatsuya Okabe & Bernhard Sendhoff, “Adapting Weighted Aggregation for Multiobjective Evolution Strategies”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, Zurich, Suiza, pp. 96–110, Marzo de
2001.
195. Andrzej Osyczka & Stanislaw Krenich, “Evolutionary Algorithms for Multicriteria Optimization with Selecting a Representative Subset of Pareto Optimal Solutions”, in Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello
& David Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag,
Zurich, Suiza, pp. 141–153, Marzo de 2001.
196. Marco Laumanns, Eckart Zitzler and Lothar Thiele, “On the Effects of Archiving, Elitism, and Density Based Selection in
Evolutionary Multi-objective Optimization”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello
& David Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag,
Zurich, Suiza, pp. 181–196, Marzo de 2001.
197. S. Ranji Ranjithan, S. Kishan Chetan and Harish K. Dakshina, “Constraint Method-Based Evolutionary Algorithm
(CMEA) for Multiobjective Optimization”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello
& David Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag,
Zurich, Suiza, pp. 299–313, Marzo de 2001.
198. Hernán E. Aguirre, Kiyoshi Tanaka, Tatsuo Sugimura & Shinjiro Oshita, “Halftone Image Generation with Improved
Multiobjective Genetic Algorithm”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David
Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, Zurich,
Suiza, pp. 501–515, Marzo de 2001.
199. Ivo F. Sbalzarini, Sibylle Müller & Petros Koumoutsakos, “Microchannel Optimization Using Multiobjective Evolution
Strategies”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First
International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, Zurich, Suiza, pp. 516–530,
Marzo de 2001.
119
200. Ester Bernadó i Mansilla and Josep M. Garrell i Guiu, “MOLeCS: Using Multiobjective Evolutionary Algorithms for
Learning”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First
International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, Zurich, Suiza, pp. 696–710,
Marzo de 2001.
201. Ruhul Sarker, Ko-Hsin Liang & Charles Newton, “A new multiobjective evolutionary algorithm”, European Journal of
Operational Research, Vol. 140, pp. 12–23, 2002.
202. Carlos Mariano and Eduardo Morales, “A New Distributed Reinforcement Learning Algorithm for Multiple Objective
Optimization Problems”, in Maria Carolina Monard and Jaime Simão Sichman (Eds), Advances in Artificial Intelligence.
IBERAMIA-SBIA 2000, pp. 290–299, Springer, Lecture Notes in Artificial Intelligence Vol. 1952, Atibaia, SP, Brazil,
November 2000.
203. Gregor Papa & Jurij Šilc, “Automatic large-scale integrated circuit synthesis using allocation-based scheduling algorithm”, Microprocessors and Microsystems, Vol. 26, No. 3, pp. 139–147, 2002.
204. A.L. Medaglia, S.C. Fang and H.L.W. Nuttle, “Fuzzy Controlled Simulation Optimization”, Fuzzy Sets and Systems,
Vol. 127, No. 1, pp. 65–84, April 2002.
205. B. Fazlollahi and R. Vahidov, “A Method for Generation of Alternatives by Decision Support Systems”, Journal of
Management Information Systems, Vol. 18, No. 2, pp. 229–250, Fall 2001.
206. H. Aguirre, K. Tanaka, T. Sugimura, and S. Oshita, “Simultaneous halftone image generation with improved multiobjective algorithm”, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, Vol.
E84A, No. 8, pp. 1869–1882, August 2001.
207. Tapabrata Ray, Tai Kang and Seow Kian Chye, “Multiobjective Design Optimization by an Evolutionary Algorithm”,
Engineering Optimization, Vol. 33, No. 3, pp. 399–424, 2001.
208. R. Sarker and C. Newton, “Solving a Multiple Objective Linear Program using Simulated Annealing”, Asia-Pacific
Journal of Operational Research, Vol. 18, No. 1, pp. 109–120, May 2001.
209. Lei Shi and Pingjing Yao, “Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis”,
Chinese Journal of Chemical Engineering, Vol. 9, No. 2, pp. 173–178, May 2001.
210. Brent E. Eskridge and Dean F. Hougen, “Memetic Crossover for Genetic Programming: Evolution Through Imitation”,
in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic
and Evolutionary Computation Conference. Part II, Springer-Verlag, Lecture Notes in Computer Science Vol. 3103, pp.
459–470, Seattle, Washington, USA, June 2004.
211. Sanghamitra Bandyopadhyay, Sankar K. Pal and B. Aruna, “Multiobjective GAs, Quantitative Indices, and Pattern
Classification”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 34, No. 5, pp.
2088–2099, October 2004.
212. M.H. Hennessy and A.M. Kelley, “Using real-valued multi-objective genetic algorithms to model molecular absorption
spectra and Raman excitation profiles in solution”, Physical Chemistry Chemical Physics, Vol. 6, No. 6, pp. 1085–1095,
March 21, 2004.
213. B. Rekiek, P. De Lit and A. Delchambre, “Hybrid Assembly Line Design and User’s Preferences”, International Journal
of Production Research, Vol. 40, No. 5, pp. 1095–1111, March 2002.
214. Pierre De Lit, Patrice Latinne, Brahim Rekiek and Alain Delchambre, “Assembly Planning with an Ordering Genetic
Algorithm”, International Journal of Production Research, Vol. 39, No. 16, pp. 3623–3640, November 2001.
215. Brahim Rekiek, Pierre De Lit, Fabrice Pellichero, Thomas L’Englise, Patrick Fouda, Emanuel Falkenauer and Alain
Delchambre, “A Multiple Objective Grouping Genetic Algorithm for Assembly Line Design”, Journal of Intelligent
Manufacturing, Vol. 12, Nos. 5–6, pp. 467–485, 2001.
216. K.C. Tan, T.H. Lee & E. F. Khor, “Evolutionary Algorithms with Dynamic Population Size and Local Exploration for
Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 5, No. 6, pp. 565-588, December
2001.
217. M. Farina and P. Amato, “Linked interpolation-optimization strategies for multicriteria optimization problems”, Soft
Computing–A Fusion of Foundations, Methodologies and Applications, Springer-Verlag, Vol. 9, No. 1, pp. 54–65,
January 2005.
218. Shinn-Ying Ho, Li-Sun Shu and Jian-Hung Chen, “Intelligent Evolutionary Algorithms for Large Parameter Optimization
Problems”, IEEE Transactions on Evolutionary Computation, Vol. 8, No. 6, pp. 522–541, December 2004.
219. Li-Sun Shu, Shinn-Jang Ho, Shinn-Ying Ho, Jian-Hung Chen and Ming-Hao Hung, “A Novel Multi-objective Orthogonal
Simulated Annealing Algorithm for solving Multi-objective Optimization Problems with a Large Number of Parameters”,
in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic
and Evolutionary Computation Conference. Part I, Springer-Verlag, Lecture Notes in Computer Science Vol. 3102, pp.
737–747, Seattle, Washington, USA, June 2004.
120
220. Praveen Koduru, Sanjoy Das, Stephen Welch and Judith L. Roe, “Fuzzy Dominance Based Multi-objective GA-Simplex
Hybrid Algorithms Applied to Gene Network Models”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary
Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part I, SpringerVerlag, Lecture Notes in Computer Science Vol. 3102, pp. 356–367, Seattle, Washington, USA, June 2004.
221. M. Parrilla Sánchez and J. Aranda Almansa, “A Real Application Example of a Control Structure Selection by Means
of a Multiobjective Genetic Algorithm”, in José Mira and José R. Álvarez (Eds.), Artificial Neural Nets Problem Solving
Methods, 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN’2003. Proceedings,
Part II, pp. 369–376, Springer, Lecture Notes in Computer Science, Vol. 2687, Maó, Menorca, Spain, June 3-6, 2003.
222. A. Kurpati, S. Azarm and J. Wu, “Constraint handling improvements for multiobjective genetic algorithms”, Structural
and Multidisciplinary Optimization, Vol. 23, No. 3, pp. 204–213, April 2002.
223. Tomonari Furukawa and Gamini Dissanayake, “Parameter Identification of Autonomous Vehicles using Multi-Objective
Optimisation”, Engineering Optimization, Vol. 34, No. 4, pp. 369–395, 2002.
224. Tomonari Furukawa, “Parameter Identification with Weightless Regularization”, International Journal for Numerical
Methods in Engineering, Vol. 52, No. 3, pp. 219–238, September 2001.
225. K.C. Tan, Tong H. Lee, D. Khoo & E.F. Khor, “A Multiobjective Evolutionary Algorithm Toolbox for Computer-Aided
Multiobjective Optimization”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 31,
No. 4, pp. 537–556, August 2001.
226. W. Matthew Carlyle, Bosun Kim, John W. Fowler & Esma S. Gel, “Comparison of Multiple Objective Genetic Algorithms
for Parallel Machine Scheduling Problems”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello
& David Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag,
Lecture Notes in Computer Science Vol. 1993, Zurich, Suiza, pp. 472–485, Marzo de 2001.
227. C. Brizuela, N. Sannomiya & Y. Zhao, “Multi-objective Flow-Shop: Preliminary Results”, en Eckart Zitzler, Kalyanmoy
Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First International Conference on Evolutionary
Multi-Criterion Optimization, Springer-Verlag, Lecture Notes in Computer Science Vol. 1993, Zurich, Suiza, pp. 443–
457, Marzo de 2001.
228. Jerzy Balicki and Zygmunt Kitowski, “Multicriteria Evolutionary Algorithm with Tabu Search for Task Assignment”,
en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello & David Corne (Eds.), First International
Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, Lecture Notes in Computer Science Vol.
1993, Zurich, Suiza, pp. 373–384, Marzo de 2001.
229. A. Chen, P. Chootinan and S. Pravinvongvuth, “Multiobjective model for locating automatic vehicle identification
readers”, Intelligent Transportation Systems and Vehicle-Highway Automation 2004 Transportation Research Record,
Vol. 1886, pp. 49–58, 2004.
230. J.E. Fieldsend and S. Singh, “Pareto evolutionary neural networks”, IEEE Transactions on Neural Networks, Vol. 16,
No. 2, pp. 338–354, March 2005.
231. Jean-Charles Créput, Abderrafiaa Koukam, Thomas Lissajoux and Alexandre Caminada, “Automatic Mesh Generation
for Mobile Network Dimensioning Using Evolutionary Approach”, IEEE Transactions on Evolutionary Computation,
Vol. 9, No. 1, pp. 18–30, February 2005.
232. Asish Kumar Sharma, Chandramouli Kulshreshtha, Keemin Sohn and Kee-Sun Sohn, “Systematic Control of Experimental Inconsistency in Combinatorial Materials Science”, Journal of Combinatorial Chemistry, Vol. 11, No. 1, pp.
131–137, January-February 2009.
233. R. Saravanan, S. Ramabalan and C. Balamurugan, “Evolutionary multi-criteria trajectory modeling of industrial robots
in the presence of obstacles”, Engineering Applications of Artificial Intelligence, Vol. 22, No. 2, pp. 329–342, March
2009.
234. Feili Yu, Fang Tu, Krishna R. Pattipati, “Integration of a holonic organizational control architecture and multiobjective
evolutionary algorithm for flexible distributed scheduling”, IEEE Transactions on Systems, Man, and Cybernetics Part
A–Systems and Humans, Vol. 38, No. 5, pp. 1001–1017, September 2008.
235. Hongbing Fang, Qian Wang, Yi-Cheng Tu and Mark F. Horstemeyer, “An Efficient Non-dominated Sorting Method for
Evolutionary Algorithms”, Evolutionary Computation, Vol. 16, No. 3, pp. 355–384, Fall 2008.
236. F. Yang, Chung Min Kwan and C.S. Chang, “Multiobjective evolutionary optimization of substation maintenance using
decision-varying Markov model”, IEEE Transactions on Power Systems, Vol. 23, No. 3, pp. 1328–1335, August 2008.
237. Tomonari Furukawa and John G. Michopoulos, “Computational design of multiaxial tests for anisotropic material characterization”, International Journal for Numerical Methods in Engineering, Vol. 74, No. 12, pp. 1872–1895, June 18,
2008.
238. I. Bate, “Systematic approaches to understanding and evaluating design trade-offs”, Journal of Systems and Software,
Vol. 81, No. 8, pp. 1253–1271, August 2008.
121
239. M. Varadarajan and K.S. Sworup, “Solving multi-objective optimal power flow Using differential evolution”, IET Generation Transmission & Distribution, Vol. 2, No. 5, pp. 720–730, September 2008.
240. Gregor Papa and Tomasz Garbolino, “A new approach to optimization of test pattern generator structure”, Informacije
Midem–Journal of Microelectronics electronic components and materials, pp. 26–30, Vol. 38, No. 1, March 2008.
241. Jose L. Risco-Martin, David Atienza, J. Ignacio Hidalgo and Juan Lanchares, “A parallel evolutionary algorithm to
optimize dynamic data types in embedded systems”, Soft Computing, Vol. 12, No. 12, pp. 1157–1167, October 2008.
242. Giuseppe Carlo Marano, “Reliability based multiobjective optimization for design of structures subject to random
vibrations”, Journal of Zhejiang University–Science A, Vol. 9, No. 1, pp. 15–25, January 2008.
243. Praveen Koduru, Sanjoy Das, Stephen M. Welch, Judith L. Roe and Erika Charbit, “A Multiobjective EvolutionarySimplex Hybrid Approach for the Optimization of Differential Equation Models of Gene Networks”, IEEE Transactions
on Evolutionary Computation, Vol. 12, No. 5, pp. 572–590, October 2008.
244. Shubham Agrawal, B.K. Panigrahi and Manoj Kumar Tiwari, “Multiobjective Particle Swarm Algorithm with Fuzzy
Clustering for Electrical Power Dispatch”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 5, pp.
529–541, October 2008.
245. Giuseppe Carlo Marano and Giuseppe Quaranta, “Fuzzy-based robust structural optimization”, International Journal
of Solids and Structures, Vol. 45, Nos. 11–12, pp. 3544–3557, June 15, 2008.
246. Kevin I. Smith, Richard M. Everson, Jonathan E. Fieldsend, Chris Murphy and Rashmi Misra, “Dominance-Based
Multiobjective Simulated Annealing”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 3, pp. 323–342,
June 2008.
247. N. Amanifard, N. Nariman-Zadeh, M. Borji, A. Khalkhali and A. Habibdoust, “Modelling and Pareto optimization of
heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms”, Energy
Conversion and Management, Vol. 49, No. 2, pp. 311–325, February 2008.
248. Bin Qian, Ling Wang, De-Xian Huang and Xiong Wang, “Scheduling multi-objective job shops using a memetic algorithm
based on differential evolution”, International Journal of Advanced Manufacturing Technology, Vol. 35, Nos. 9–10, pp.
1014–1027, January 2008.
249. O. Giustolisi, A. Doglioni, D.A. Savic and F. di Pierro, “An evolutionary multiobjective strategy for the effective
management of groundwater resources”, Water Resources Research, Vol. 44 No. 1, article number W01403, January 3,
2008.
250. Eduardo Fernandez, Nora Cancela and Rafael Olmedo, “Deriving a final ranking from fuzzy preferences: An approach
compatible with the Principle of Correspondence”, Mathematical and Computer Modelling, Vol. 47, Nos. 1–2, pp.
218–234, January 2008.
251. Sanjoy Das, Balasubramaniam Natarajan, Daniel Stevens and Praveen Koduru, “Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principle”, Applied Soft Computing, Vol. 8, No. 1, pp.
788–797, January 2008.
252. Antonio Pinto, Daniele Peri and Emilio F. Campana, “Multiobjective optimization of a containership using deterministic
particle swarm optimization”, Journal of Ship Research, Vol. 51, No. 3, pp. 217–228, September 2007.
253. Murat Koekalan and Selcen (Pamuk) Phelps, “An evolutionary metaheuristic for approximating preference-nondominated
solutions”, Informs Journal on Computing, Vol. 19, No. 2, pp. 291–301, Spring 2007.
254. J. Galuski and C.L. Bloebaum, “Multi-objective Pareto concurrent subspace optimization for multidisciplinary design”,
AIAA Journal, Vol. 45, No. 8, pp. 1894–1906, August 2007.
255. V. Mazur, “Fuzzy thermoeconomic optimization of energy-transforming systems”, Applied Energy, Vol. 84, Nos. 7–8,
pp. 749–762, July-August 2007.
256. Jie Hu, Yinghong Peng and Guangleng Xiong, “Knowledge network driven coordination and robust optimization to
support concurrent and collaborative parameter design”, Concurrent Engineering-Research and Applications, Vol. 15,
No. 1, pp. 43–52, March 2007.
257. Mostafa I.H. Abd-El-Barr and Salman A. Khan, “Design and analysis of a fault tolerant hybrid mobile scheme”, Information Sciences, Vol. 177, No. 12, pp. 2602–2620, June 15, 2007.
258. E.J. Solteiro Pires, P.B. de Moura Oliveira and J.A. Tenreiro Machado, “Manipulator trajectory planning using a
MOEA”, Applied Soft Computing, Vol. 7, No. 3, pp. 659–667, June 2007.
259. Pascal Cote, Lael Parrott and Robert Sabourin, “Multi-objective optimization of an ecological assembly model”, Ecological Informatics, Vol. 2, No. 1, pp. 23–31, January 1, 2007.
260. C. K. Goh and K. C. Tan, “An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization”,
IEEE Transactions on Evolutionary Computation, Vol. 11, No. 3, pp. 354–381, June 2007.
261. Samya Elaoud, Jacques Teghem and Bassem Bouaziz, “Genetic algorithms to solve the cover printing problem”, Computers & Operations Research, Vol. 34, No. 11, pp. 3346–3361, November 2007.
122
262. Sahnan A. Khan and Andries P. Engelbrecht, “A new fuzzy operator and its application to topology design of distributed
local area networks”, Information Sciences, Vol. 177, No. 13, pp. 2692–2711, July 1, 2007.
263. Samya Elaoud, Taicir Loukil and Jacques Teghem, “The Pareto fitness genetic algorithm: Test function study”, European
Journal of Operational Research, Vol. 177, No. 3, pp. 1703–1719, March 16, 2007.
264. J.Y. Goulermas, R. Liatsis and T. Fernando, “Strained non linear energy minimization framework for the regularization
of the stereo correspondence problem”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 15, No.
4, pp. 550–565, April 2005.
265. M.S. Osman, M.A. Abo-Sinna and A.A. Mousa, “An effective genetic algorithm approach multiobjective resource allocation problems (MORAPs)”, Applied Mathematics and Computation, Vol. 163, No. 2, pp. 755–768, April 15, 2005.
266. C. Jiang and C. Wang, “Improved evolutionary programming with dynamic mutation and metropolis criteria for multiobjective reactive power optimisation”, IEE Proceedings–Generation Transmission and Distribution, Vol. 152, No. 2,
pp. 291–294, March 2005.
267. B. Suman, “Study of self-stopping PDMOSA and performance measure in multiobjective optimization”, Computers &
Chemical Engineering, Vol. 29, No. 5, pp. 1131–1147, April 15, 2005.
268. S.R. Anderson, V. Kadirkamanathan, A. Chipperfield, V. Sharifi and J. Swithenbank, “Multi-objective optimization of
operational variables in a waste incineration plant”, Computers & Chemical Engineering, Vol. 29, No. 5, pp. 1121–1130,
April 15, 2005.
269. B. Gaal, I. Vassanyi and G. Kozmann, “A novel artificial intelligence method for weekly dietary menu planning”, Methods
of Information in Medicine, Vol. 44, No. 5, pp. 655–664, 2005.
270. K.C. Tan, C.K. Goh, Y.J. Yang and T.H. Lee, “Evolving better population distribution and exploration in evolutionary
multi-objective optimization”, European Journal of Operational Research, Vol. 171, No. 2, pp. 463–495, June 1, 2006.
271. K.C. Tan, Y.H. Chew and L.H. Lee, “A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem
with time windows”, Computational Optimization and Applications, Vol. 34, No. 1, pp. 115–151, May 2006.
272. K.C. Tan, Y.H. Chew and L.H. Lee, “A hybrid multi-objective evolutionary algorithm for solving truck and trailer
vehicle routing problems”, European Journal of Operational Research, Vol. 172, No. 3, pp. 855–885, August 1st, 2006.
273. X. Yao and Y. Xu, “Recent advances in evolutionary computation”, Journal of Computer Science and Technology, Vol.
21, No. 1, pp. 1–18, January 2006.
274. D. De, S. Ray, A. Konar and A. Chatterjee, “An evolutionary SPDE breeding-based hybrid particle swarm optimizer:
Application in coordination of robot ants for camera coverage area optimization”, in Pattern Recognition and Machine
Intelligence, Proceedings, pp. 413–416, Springer, Lecture Notes in Computer Science Vol. 3776, 2005.
275. M. Sprogar, M. Sprogar and M. Colnaric, “Autonomous evolutionary algorithm in medical data analysis”, Computer
Methods and Programs in Biomedicine, Vol. 80, pp. S29–S38, Suppl. 1, December 2005.
276. C.J.K. Lee, T. Furukawa and S. Yoshimura, “A human-like numerical technique for design of engineering systems”,
International Journal for Numerical Methods in Engineering, Vol. 64, No. 14, pp. 1915–1943, December 14, 2005.
277. K. El-Rayes and K. Hyari, “Optimal lighting arrangements for nighttime highway construction projects”, Journal of
Construction Engineering and Management–ASCE, Vol. 131, No. 12, pp. 1292–1300, December 2005.
278. C.O.S. Sorzano, R. Marabini, G.T. Herman and J.M. Carazo, “Multiobjective algorithm parameter optimization using
multivariate statistics in three-dimensional electron microscopy reconstruction”, Pattern Recognition, Vol. 38, No. 12,
pp. 2587–2601, December 2005.
279. A. Kamiya, S.J. Ovaska, R. Roy and S. Kobayashi, “Fusion of soft computing and hard computing for large-scale plants:
a general model”, Applied Soft Computing, Vol. 5, No. 3, pp. 265–279, March 2005.
280. E.K. Burke and J.D. Landa Silva, “The influence of the fitness evaluation method on the performance of multiobjective
search algorithms”, European Journal of Operational Research, Vol. 169, No. 3, pp. 875–897, March 16, 2006.
281. K. Atashkari, N. Nariman-Zadeh, A. Pilechi, A. Jamali and X. Yao, “Thermodynamic Pareto optimization of turbojet
engines using multi-objective genetic algorithms”, International Journal of Thermal Sciences, Vol. 44, No. 11, pp.
1061–1071, November 2005.
282. J.E.C. Arroyo and V.A. Armentano, “Genetic local search for multi-objective flowshop scheduling problems”, European
Journal of Operational Research, Vol. 167, No. 3, pp. 717–738, December 16, 2005.
283. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
284. C. Setzkorn and R.C. Paton, “On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems”, Biosystems, Vol. 81, No. 2, pp. 101–112, August 2005.
285. N. Nariman-Zadeh, K. Atashkari, A. Jamali, A. Pilechi and X. Yao, “Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms”, Engineering
Optimization, Vol. 37, No. 5, pp. 437–462, July 2005.
123
286. B.V. Babu, P.G. Chakole and J.H.S. Mubeen, “Multiobjective differential evolution (MODE) for optimization of adiabatic
styrene reactor”, Chemical Engineering Science, Vol. 60, No. 17, pp. 4822–4837, September 2005.
287. J. Martin, C. Bielza and D.R. Insua, “Approximating nondominated sets in continuous multiobjective optimization
problems”, Naval Research Logistics, Vol. 52, No. 5, pp. 469–480, August 2005.
288. J.H. Chen, H.M. Chen and S.Y. Ho, “Design of nearest neighbor classifiers: multi-objective approach”, International
Journal of Approximate Reasoning, Vol. 40, Nos. 1–2, pp. 3–22, July 2005.
289. Jessica Andrea Carballido, Ignacio Ponzoni and Nélida Beatriz Brignole, “A Novel Application of Evolutionary Computing in Process Systems Engineering”, in Günther R. Raidl and Jens Gottlieb (editors), Evolutionary Computation in
Combinatorial Optimization. 5th European Conference, EvoCOP 2005, pp. 12–22, Springer, Lecture Notes in Computer
Science Vol. 3448, Lausanne, Switzerland, March/April 2005.
290. Nicolás Garcı́a-Pedrajas, César Hervás-Martı́nez and Domingo Ortiz-Boyer, “Cooperative Coevolution of Artificial Neural Network Ensembles for Pattern Classification”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 3, pp.
271–302, June 2005.
291. Juan Carlos Leyva-Lopez and Miguel Angel Aguilera-Contreras, “A Multiobjective Evolutionary Algorithm for Deriving
Final Ranking from a Fuzzy Outranking Relation”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart
Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 235–249,
Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
292. Milan Zeleny, “The Evolution of Optimality: De Novo Programming”, in Carlos A. Coello Coello, Arturo Hernández
Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO
2005, pp. 1–13, Springer. Lecture Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
293. M. Galea, Q. Shen and J. Levine, “Evolutionary approaches to fuzzy modelling for classification”, Knowledge Engineering
Review, Vol. 19, No. 2, pp. 27–59, March 2004.
294. A. Dogan and F. Ozguner, “Biobjective scheduling algorithms for execution time-reliability trade-off in heterogeneous
computing systems”, Computer Journal, Vol. 48, No. 3, pp. 300–314, 2005.
295. E.J. Solteiro Pires, P.B. de Moura Oliveira and J.A. Tenreiro Machado, “Multi-objective MaxiMin Sorting Scheme”,
in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 165–175, Springer. Lecture Notes in Computer Science Vol.
3410, Guanajuato, México, March 2005.
296. K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali and A. Jamali, “Modelling and multi-objective optimization
of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms”, Energy
Conversion and Management, Vol. 48, No. 3, pp. 1029–1041, March 2007.
297. Hisao Ishibuchi and Yusuke Nojima, “Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective
fuzzy genetics-based machine learning”, International Journal of Approximate Reasoning, Vol. 44, No. 1, pp. 4–31,
January 2007.
298. L. Grandinetti, F. Guerriero, G. Lepera and M. Mancini, “A niched genetic algorithm to solve a pollutant emission
reduction problem in the manufacturing industry: A case study”, Computers & Operations Research, Vol. 34, No. 7,
pp. 2191–2214, July 2007.
299. M. Ali-Tavoli, N. Nariman-Zadeh, A. Khakhali and M. Mehran, “Multi-objective optimization of abrasive flow machining
processes using polynomial neural networks and genetic algorithms”, Machining Science and Technology, Vol. 10, No.
4, pp. 491–510, October-December 2006.
300. B. Suman and P. Kumar, “A survey of simulated annealing as a tool for single and multiobjective optimization”, Journal
of the Operational Research Society, Vol. 57, No. 10, pp. 1143–1160, October 2006.
301. B. Qian, L. Wang, D.X. Huang and X. Wang, “Multi-objective flow shop scheduling using differential evolution”,
Intelligent Computing in Signal Processing and Pattern Recognition, Springer-Verlag, pp. 1125–1136, Lecture Notes in
Control and Information Sciences Vol. 345, 2006.
302. D. Salazar, C.M. Rocco and B.J. Galvan, “Optimization of constrained multiple-objective reliability problems using
evolutionary algorithms”, Reliability Engineering & System Safety, Vol. 91, No. 9, pp. 1057–1070, September 2006.
303. A. Konak, D.W. Coit and A.E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial”, Reliability
Engineering & System Safety, Vol. 91, No. 9, pp. 992–1007, September 2006.
304. J.P. Ponthot and J.P. Kleinermann, “A cascade optimization methodology for automatic parameter identification and
shape/process optimization in metal forming simulation”, Computer Methods in Applied Mechanics and Engineering,
Vol. 195, Nos. 41–43, pp. 5472–5508, 2006.
305. M. Ma, L.B. Zhang, J. Ma and C.G. Zhou, “Fuzzy neural network optimization by a particle swarm optimization
algorithm”, Advances in Neural Networks–ISSN 2006, Part 1, pp. 752–761, Springer, Lecture Notes in Computer
Science Vol. 3971, 2006.
124
306. N. Nariman-Zadeh, A. Darvizeh and A. Jamali, “Pareto optimization of energy absorption of square aluminium columns
using multi-objective genetic algorithms”, Proceedings of the Institution of Mechanical Engineers Part B–Journal of
Engineering Manufacture, Vol. 220, No. 2, pp. 213–224, February 2006.
307. D.A.M. Rocha, E.F. Goldbarg and M.C. Goldbarg, “A memetic algorithm for the biobjective minimum spanning tree
problem”, Evolutionary Computation in Combinatorial Optimization, pp. 222–233, Springer, Lecture Notes in Computer
Science, Vol. 3906, 2006.
308. R.M. Everson and J.E. Fieldsend, “Multi-class ROC analysis from a multi-objective optimisation perspective”, Pattern
Recognition Letters, Vol. 27, No. 8, pp. 918–927, June 2006.
309. M. Mahfouf, M. Jamei and D.A. Linkens, “Optimal design of alloy steels using multiobjective genetic algorithms”,
Materials and Manufacturing Processes, Vol. 20, No. 3, pp. 553–567, 2005.
310. M.A. Elsays, M. Naguib Aly and A.A. Badawi, “Design optimization of shell-and-tube heat exchangers using single
objective and multiobjective particle swarm optimization”, Kerntechnik, Vol. 75, Nos. 1–2, pp. 38–46, March 2010.
311. Junzhou Huo, Wei Sun, Jing Chen, Pengcheng Su and Liying Deng, “Optimal disc cutters plane layout design of the
full-face rock tunnel boring machine (tbm) based on a multi-objective genetic algorithm”, Journal of Mechanical Science
and Technology, Vol. 24, No. 2, pp. 521–528, February 2010.
312. Chung Min Kwan and C.S. Chang, “Timetable synchronization of mass rapid transit system using multiobjective evolutionary approach”, IEEE Transactions on Systems, Man, and Cybernetics Part C–Applications and Reviews, Vol. 38,
No. 5, pp. 636–648, September 2008.
313. E. Zio, P. Baraldi and N. Pedroni, “Optimal power system generation scheduling by multi-objective genetic algorithms
with preferences”, Reliability Engineering & System Safety, Vol. 94, No. 2, pp. 432–444, February 2009.
314. Siew-Chin Neoh, Norhashimah Morad, Chee-Peng Lim and Zalina Abdul Aziz, “A Layered-Encoding Cascade Optimization Approach to Product-Mix Planning in High-Mix-Low-Volume Manufacturing”, IEEE Transactions on Systems,
Man, and Cybernetics Part A—Systems and Humans, Vol. 40, No. 1, pp. 133–146, January 2010.
315. Yahong Yang, Guiling Wu, Jianping Chen and Wei Dai, “Multi-objective optimization based on ant colony optimization
in grid over optical burst switching networks”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1769–1775, March
2010.
316. Asish Kumar Sharma and Kee-Sun Sohn, “Search for phosphors for use in displays and lighting using heuristics-based
combinatorial materials science”, Journal of the Society for Information Display, Vol. 17, No. 12, pp. 1073–1080,
December 2009.
317. Ke-Shiuan Lynn, Li-Lan Li, Yen-Ju Lin, Chiuen-Huei Wang, Shu-Hui Sheng, Ju-Hwa Lin, Wayne Liao, Wen-Lian
Hsu and Wen-Harn Pan, “A neural network model for constructing endophenotypes of common complex diseases: an
application to male young-onset hypertension microarray data”, Bioinformatics, Vol. 25, No. 8, pp. 981–988, April 15,
2009.
318. Sriparna Saha, Susmita Sur-Kolay, Parthasarathi Dasgupta and Sanghamitra Bandyopadhyay, “MAkE: Multiobjective
algorithm for k-way equipartitioning of a point set”, Applied Soft Computing, Vol. 9, No. 2, pp. 711–724, March 2009.
319. Ragnar Arnason, “Fisheries management and operations research”, European Journal of Operational Research, Vol. 193,
No. 3, pp. 741–751, March 16, 2009.
320. Kamyoung Kim, Alan T. Murray and Ningchuan Xiao, “A multiobjective evolutionary algorithm for surveillance sensor
placement”, Environment and Planning B–Planning & Design, Vol. 35, No. 5, pp. 935–948, September 2008.
321. N. Nariman-Zadeh, M. Felezi, A. Jamali and M. Ganji, “Pareto optimal synthesis of four-bar mechanisms for path
generation”, Mechanism and Machine Theory, Vol. 44, No. 1, pp. 180–191, January 2009.
322. C.K. Panigrahi, R. Chakrabarti and P.K. Chattopadhyay, “Economic Environmental Dispatch by a MODE Technique”,
Journal of Circuits Systems and Computers, Vol. 17, No. 3, pp. 499–512, June 2008.
323. Xuesong Wang, Minglin Hao, Yuhu Cheng and Ruhai Lei, “PDE-PEDA: A New Pareto-Based Multi-objective Optimization Algorithm”, Journal of Universal Computer Science, Vol. 15, No. 4, pp. 722–741, 2009.
324. Yusuke Nojima, Hisao Ishibuchi and Isao Kuwajima, “Parallel distributed genetic fuzzy rule selection”, Soft Computing,
Vol. 13, No. 5, pp. 511–519, March 2009.
325. Eduardo Fernandez, Jorge Navarro and Sergio Bernal, “Handling multicriteria preferences in cluster analysis”, European
Journal of Operational Research, Vol. 202, No. 3, pp. 819–827, May 1, 2010.
326. J.E. Mendoza, L.A. Villaleiva, M.A. Castro and E.A. Lopez, “Multi-objective Evolutionary Algorithms for DecisionMaking in Reconfiguration Problems Applied to the Electric Distribution Networks”, Studies in Informatics and Control,
Vol. 18, No. 4, pp. 325–336, December 2009.
327. M. Basu, “Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II”, International Journal
of Electrical Power & Energy Systems, Vol. 30, No. 2, pp. 140–149, February 2008.
125
328. Sidhartha Panda, “Multi-Objective Non-Dominated Shorting Genetic Algorithm-II for Excitation and TCSC-Based
Controller Design”, Journal of Electrical Engineering, Vol. 60, No. 2, pp. 86–93, 2009.
329. Mohammad Saadatseresht, Ali Mansourian and Mohammad Taleai, “Evacuation planning using multiobjective evolutionary optimization approach”, European Journal of Operational Research, Vol. 198, No. 1, pp. 305–314, October 1,
2009.
330. F. Yang and C.S. Chang, “Multiobjective Evolutionary Optimization of Maintenance Schedules and Extents for Composite Power Systems”, IEEE Transactions on Power Systems, Vol. 24, No. 4, pp. 1694–1702, November 2009.
331. O. Feyzioglu and H. Pierreval, “Hybrid organization of functional departments and manufacturing cells in the presence
of imprecise data”, International Journal of Production Research, Vol. 47, No. 2, pp. 343–368, 2009.
332. Anirban Mukhopadhyay, Ujjwal Maulik and Sanghamitra Bandyopadhyay, “Multiobjective Genetic Algorithm-Based
Fuzzy Clustering of Categorical Attributes”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp.
991–1005, October 2009.
333. F. Yang and C.S. Chang, “Optimisation of maintenance schedules and extents for composite power systems using multiobjective evolutionary algorithm”, IET Generation Transmission & Distribution, Vol. 3, No. 10, pp. 930–940, October
2009.
334. A. Albers, N. Leon-Rovira, H. Aguayo and T. Maier, “Development of an engine crankshaft in a framework of computeraided innovation”, Computers in Industry, Vol. 60, No. 8, pp. 604–612, October 2009.
335. Jose L. Ceciliano Meza, Mehmet Bayram Yildirim and Abu S.M. Masud, “A Multiobjective Evolutionary Programming
Algorithm and Its Applications to Power Generation Expansion Planning”, IEEE Transactions on Systems, Man, and
Cybernetics, Part A–Systems and Humans, Vol. 39, No. 5, pp. 1086–1096, September 2009.
336. Ruhul Sarker and Tapabrata Ray, “An improved evolutionary algorithm for solving multi-objective crop planning models”, Computers and Electronics in Agriculture, Vol. 68, No. 2, pp. 191–199, October 2009.
337. Eugene Y.C. Wong, Henry S.C. Yeung and Henry Y.K. Lau, “Immunity-based hybrid evolutionary algorithm for multiobjective optimization in global container repositioning”, Engineering Applications of Artificial Intelligence, Vol. 22,
No. 6, pp. 842–854, September 2009.
338. A. Jamali, N. Nariman-zadeh, A. Darvizeh, A. Masoumi and S. Hamrang, “Multi-objective evolutionary optimization
of polynomial neural networks for modelling and prediction of explosive cutting process”, Engineering Applications of
Artificial Intelligence, Vol. 22, Nos. 4-5, pp. 676–687, June 2009.
339. Dimitrios Makris, Georgios Bardis, Georgios Miaoulis amd Dimitri Plemenos, “Acquisition and Exploitation of Qualitative Aspects in 3D Scene Synthesis”, International Journal on Artificial Intelligence Tools, Vol. 18, No. 1, pp. 39–59,
February 2009.
340. Jun-Zhou Huo and Hong-Fei Teng, “Optimal Layout Design of a Satellite Module Using a Coevolutionary Method with
Heuristic Rules”, Journal of Aerospace Engineering, Vol. 22, No. 2, pp. 101–111, April 2009.
341. M.A. Elsays, M. Naguib Aly and A.A. Badawi, “Optimizing the dynamic response of the H. B. Robinson nuclear plant
using multiobjective particle swarm optimization”, Kerntechnik, Vol. 74, Nos. 1–2, pp. 70–78, April 2009.
342. Asish Kumar Sharma, Chandramouli Kulshreshtha and Kee-Sun Sohn, “Discovery of New Green Phosphors and Minimization of Experimental Inconsistency Using a Multi-Objective Genetic Algorithm-Assisted Combinatorial Method”,
Advanced Functional Materials, Vol. 19, No. 11, pp. 1705–1712, June 9, 2009.
343. G.N. Beligiannis, C. Moschopoulos, S.D. Likothanassis, “A genetic algorithm approach to school timetabling”, Journal
of the Operational Research Society, Vol. 60, No. 1, pp. 23–42, January 2009.
344. Utpal Biswas, Ujjwal Maulik, Anirban Mukhopadhyay and Mrinal Kanti Naskar, “Multiobjective evolutionary approach
to cost-effective traffic grooming in unidirectional SONET/WDM rings”, Photonic Network Communications, Vol. 18,
No. 1, pp. 105–115, August 2009.
345. M.A. Abido, “Multiobjective particle swarm optimization for environmental/economic dispatch problem”, Electric Power
Systems Research, Vol. 79, No. 7, pp. 1105–1113, July 2009.
346. Zhiyong Li, Guenter Rudolph and Kenli Li, “Convergence performance comparison of quantum-inspired multi-objective
evolutionary algorithms”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 1843–1854, June 2009.
347. Fangqi Cheng, Feifan Ye and Jianguo Yang, “Multi-objective optimization of collaborative manufacturing chain with
time-sequence constraints”, International Journal of Advanced Manufacturing Technology, Vol. 40, Nos. 9–10, pp.
1024–1032, February 2009.
• Arturo Hernández Aguirre, Salvador Botello Rionda, Carlos A. Coello Coello, Giovanni Lizárraga Lizárraga,
and Efrén Mezura Montes, “Handling Constraints using Multiobjective Optimization Concepts”, International Journal for Numerical Methods in Engineering, Vol. 59, No. 15, pp. 1989–2017, April 2004.
1. Rommel G. Regis, “Constrained optimization by radial basis function interpolation for high-dimensional expensive blackbox problems with infeasible initial points”, Engineering Optimization, Vol. 46, No. 2, pp. 218–243, February 1, 2014.
126
2. Xiangtong Kong, Haibin Ouyang and Xiaoxue Piao, “A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization”, Soft Computing, Vol. 17, No. 12, pp. 2293–2309, December 2013.
3. Chunjiang Zhang, Xinyu Li, Liang Gao and Qing Wu, “An improved electromagnetism-like mechanism algorithm for
constrained optimization”, Expert Systems with Applications, Vol. 40, No. 14, pp. 5621–5634, October 15, 2013.
4. Syeda Darakhshan Jabeen, “Split and Discard Strategy: A New Approach for Constrained Global Optimization”,
International Journal of Artificial Intelligence Tools, Vol. 22, No. 4, Article Number: 1350023, August 2013.
5. Sanyou Zeng, Yang Yang, Yulong Shi, Xianqiang Yang, Bo Xiao, Song Gao, Danping Yu and Zu Yan, “A micro niche evolutionary algorithm with lower-dimensional-search crossover for optimisation problems with constraints”, International
Journal of Bio-Inspired Computation, Vol. 1, No. 3, pp. 177–185, 2009.
6. Adil Amirjanov, “Modelling the dynamics of an adjustment of a search space size in a Genetic Algorithm”, International
Journal of Modern Physics C, Vol. 19, No. 7, pp. 1047–1062, July 2008.
7. Issam Mazhoud, Khaled Hadj-Hamou, Jean Bigeon and Patrice Joyeux, “Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism”, Engineering Applications of Artificial Intelligence, Vol. 26,
No. 4, pp. 1263–1273, April 2013.
8. LiCheng Jiao, Lin Li, RongHua Shang, Fang Liu and Rustam Stolkin, “A novel selection evolutionary strategy for
constrained optimization”, Information Sciences, Vol. 239, pp. 122–141, August 1, 2013.
9. Guanbo Jia, Yong Wang, Zixing Cai and Yaochu Jin, “An improved (µ + λ)-constrained differential evolution for
constrained optimization”, Information Sciences, Vol. 222, pp. 302–322, February 10, 2013.
10. Yong Wang and Zixing Cai, “Combining Multiobjective Optimization with Differential Evolution to Solve Constrained
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 117–134, February
2012.
11. A. Rama Mohan Rao and K. Lakshmi, “Discrete hybrid PSO algorithm for design of laminate composites with multiple
objectives”, Journal of Reinforced Plastics and Composites, Vol. 30, No. 20, pp. 1703–1727, October 2011.
12. Jiaquan Gao and Jun Wang, “A hybrid quantum-inspired immune algorithm for multiobjective optimization”, Applied
Mathematics and Computation, Vol. 217, No. 9, pp. 4754–4770, January 1, 2011.
13. Min Gan, Hui Peng, Xiaoyan Peng, Xiaohong Chen and Garba Inoussa, “An adaptive decision maker for constrained
evolutionary optimization”, Applied Mathematics and Computation, Vol. 215, No. 12, pp. 4172–4184, February 15,
2010.
14. Jiaquan Gao, Lei Fang and Jun Wang, “A weight-based multiobjective immune algorithm: WBMOIA”, Engineering
Optimization, Vol. 42, No. 8, pp. 719–745, 2010.
15. Abdelaziz Hammache, Marzouk Benali and Francois Aube, “Multi-objective self-adaptive algorithm for highly constrained problems: Novel method and applications”, Applied Energy, Vol. 87, No. 8, pp. 2467–2478, August 2010.
16. Jinhua Wang and Zeyong Yin, “A ranking selection-based particle swarm optimizer for engineering design optimization
problems”, Structural and Multidisciplinary Optimization, Vol. 37, No. 2, pp. 131–147, December 2008.
17. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
18. Yong Wang, Zixing Cai, Yuren Zhou and Wei Zeng, “An Adaptive Tradeoff Model for Constrained Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 80–92, February 2008.
19. Yong Wang, Hui Liu, Zixing Cai and Yuren Zhou, “An orthogonal design based constrained evolutionary optimization
algorithm”, Engineering Optimization, Vol. 39, No. 6, pp. 715–736, September 2007.
20. Pei Yee Ho and Kazuyuki Shimizu, “Evolutionary constrained optimization using an addition of ranking method and a
percentage-based tolerance value adjustment scheme”, Information Sciences, Vol. 177, No. 14, pp. 2985–3004, July 15,
2007.
21. Yong Wang, Zixing Cai, Guanqi Guo and Yuren Zhou, “Multiobjective optimization and hybrid evolutionary algorithm
to solve constrained optimization problems”, IEEE Transactions on Systems, Man and Cybernetics Part B–Cybernetics,
Vol. 37, No. 3, pp. 560–575, June 2007.
22. Zhuhong Zhang, “Constrained multiobjective optimization immune algorithm: Convergence and application”, Computers
& Mathematics with Applications, Vol. 52, No. 5, pp. 791–808, September 2006.
23. Zhuhong Zhang, “Immune optimization algorithm for constrained nonlinear multiobjective optimization problems”,
Applied Soft Computing, Vol. 7, No. 3, pp. 840–857, June 2007.
24. Jingxuan Wei and Yuping Wang, “A Novel Multi-objective PSO Algorithm for Constrained Optimization Problems”, in
T.-D. Wang et al. (editors), Simulated Evolution and Learning (SEAL 2006), pp. 174–180, Springer, Lecture Notes in
Computer Science Vol. 4247, 2006.
127
25. Philip Hingston, Luigi Barone, Simon Huband and Lyndon While, “Multi-level Ranking for Constrained Multi-objective
Evolutionary Optimisation”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós,
L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference,
pp. 563–572, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
26. Fabio Freschi and Maurizio Repetto, “VIS: an artificial immune network for multi-objective optimization”, Engineering
Optimization, Vol. 38, No. 8, pp. 975–996, December 2006.
27. Yuping Wang, Dalian Liu, and Yiu-Ming Cheung, “Preference Bi-objective Evolutionary Algorithm for Constrained
Optimization”, in Yue Hao et al. (editors), Computational Intelligence and Security. International Conference, CIS
2005, pp. 184–191, Springer, Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December 2005.
28. Tetsuyuki Takahama and Setsuko Sakai, “Constrained Optimization by Applying the α Constrained Method to the
Nonlinear Simplex Method With Mutations”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 5, pp.
437–451, October 2005.
29. Fabio Freschi and Maurizio Repetto, “Multiobjective Optimization by a Modified Artificial Immune System Algorithm”,
in Christian Jacob, Marcin L. Pilat, Peter J. Bentley and Jonathan Timmis (editors), Artificial Immune Systems. 4th
International Conference, ICARIS 2005, pp. 248–261, Springer. Lecture Notes in Computer Science Vol. 3627, Banff,
Canada, August 2005.
30. Zixing Cai and Yong Wang, “A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 658–675, December 2006.
31. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique for
constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,
pp. 1501–1534, March 2009.
32. Adil Amirjanov, “The dynamics of a changing range genetic algorithm”, International Journal for Numerical Methods
in Engineering, Vol. 81, No. 7, pp. 892–909, February 12, 2010.
33. A. Rama Mohan Rao and P.P. Shyju, “A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of Hybrid
Laminate Composite Structures”, Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 3, pp. 149–170,
April 2010.
34. Jamie A. Lennon and Ella M. Atkins, “Preference-Based Trajectory Generation”, Journal of Aerospace Computing
Information and Communication, Vol. 6, No. 3, pp. 142–170, 2009.
35. Adil Amirjanov, “The Dynamics of a Changing Range Genetic Algorithm under Stabilizing Selection”, International
Journal of Modern Physics C, Vol. 20, No. 7, pp. 1063–1079, July 2009.
36. Adil Amirjanov, “The Performance of Genetic Algorithm with Adjustment of a Search Space”, International Journal of
Modern Physics C, Vol. 20, No. 4, pp. 565–583, April 2009.
37. Tetsuyuki Takahama and Setsuko Sakai, “Fast and Stable Constrained Optimization by the −constrained Differential
Evolution”, Pacific Journal of Optimization, Vol. 5, No. 2, pp. 261–282, May 2009.
• Carlos A. Coello Coello, Gregorio Toscano Pulido and Maximino Salazar Lechuga, “Handling Multiple Objectives with Particle Swarm Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 8, No.
3, pp. 256–279, June 2004.
1. A. Kaveh and K. Laknejadi, “A new multi-swarm multi-objective optimization method for structural design”, Advances
in Engineering Software, Vol. 58, pp. 54–69, April 2013.
2. Feizi E. Ashtiani, M.H. Niksokhan and M. Ardestani, “Multi-objective Waste Load Allocation in River System by
MOPSO Algorithm”, International Journal of Environmental Research, Vol. 9, No. 1, pp. 69–76, Winter 2015.
3. Gang Xu, Yu-qun Yang, Bin-Bin Liu, Yi-hong Xu and Ai-jun Wu, “An efficient hybrid multi-objective particle swarm
optimization with a multi-objective dichotomy line search”, Journal of Computational and Applied Mathematics, Vol.
280, pp. 310–326, May 15, 2015.
4. Kazuhiro Izui, Takayuki Yamada, Shinji Nishiwaki and Kazuto Tanaka, “Multiobjective optimization using an aggregative gradient-based method”, Structural and Multidisciplinary Optimization, Vol. 51, No. 1, pp. 173–182, January
2015.
5. Yu-Bin Zhong, Yi Xiang and Hai-Lin Liu, “A multi-objective artificial bee colony algorithm based on division of the
searching space”, Applied Intelligence, Vol. 41, No. 4, pp. 987–1011, December 2014.
6. Wang Hu and Gary G. Yen, “Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate
System”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 1–18, February 2015.
7. Yong Zhang, Dun-Wei Gong and Na Gong, “Multi-Objective Optimization Problems Using Cooperative Evolvement
Particle Swarm Optimizer”, Journal of Computational and Theoretical Nanoscience, Vol. 10, No. 3, pp. 655-663, March
2013.
128
8. A. Chatterjee, S.P. Ghoshal and V. Mukherjee, “Solution of combined economic and emission dispatch problems of
power systems by an opposition-based harmony search algorithm’, International Journal of Electrical Power & Energy
Systems, Vol. 39, No. 1, pp. 9–20, July 2012.
9. Khin Lwin, Rong Qu and Graham Kendall, “A learning-guided multi-objective evolutionary algorithm for constrained
portfolio optimization”, Applied Soft Computing, Vol. 24, pp. 757–772, November 2014.
10. Ching-Tang Hsieh and Chia-Shing Hu, “Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with
SVM”, Journal of Applied Research and Technology, Vol. 12, No. 6, pp. 1014–1024, December 2014.
11. Lianbo Ma, Kunyuan Hu, Yunlong Zhu and Hanning Chen, “Cooperative artificial bee colony algorithm for multiobjective RFID network planning”, Journal of Network and Computer Applications, Vol. 42, pp. 143–162, June 2014.
12. Ya-zhong Luo and Li-ni Zhou, “Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization”,
Mathematical Problems in Engineering, Article Number: 823659, 2014.
13. Amir Ameli, Shabab Bahrami, Farid Khazaeli and Mahmood-Reza Haghifam, “A Multiobjective Particle Swarm Optimization for Sizing and Placement of DGs from DG Owner’s and Distribution Company’s Viewpoints”, IEEE Transactions on Power Delivery, Vol. 29, No. 4, pp. 1831–1840, August 2014.
14. Wanye Xu, B.Y. Duan, Peng Li, Naigang Hu and Yuanying Qiu, “Multiobjective Particle Swarm Optimization of
Boresight Error and Transmission Loss for Airborne Radomes”, IEEE Transactions on Antennas and Propagation, Vol.
62, No. 11, pp. 5880–5885, November 2014.
15. Hamdy A. El-Ghandour and Emad Elbeltagi, “Optimal Groundwater Management Using Multiobjective Particle Swarm
with a New Evolution Strategy”, Journal of Hydrologic Engineering, Vol. 19, No. 6, pp. 1141–1149, June 1, 2014.
16. Ali Habibi Khalaj, Thomas Scherer, Jayantha Siriwardana and Saman K. Halgamuge, “Multi-objective efficiency enhancement using workload spreading in an operational data center”, Applied Energy, Vol. 138, pp. 432–444, January
15, 2015.
17. Joshua T. Knight, Frank T. Zahradka, David J. Singer and Matthew D. Collette, “Multiobjective Particle Swarm
Optimization of a Planing Craft with Uncertainty”, Journal of Ship Production and Design, Vol. 30, No. 4, pp.
194–200, November 2014.
18. Heming Xu, Yinglin Wang and Xin Xu, “The crowd framework for multiobjective particle swarm optimization”, Artificial
Intelligence Review, Vol. 42, No. 4, pp. 1095–1138, December 2014.
19. Bin Zhou, Ka Wing Chan, Tao Yu, Hua Wei and Jie Tang, “Strength Pareto Multigroup Search Optimizer for Multiobjective Optimal Reactive Power Dispatch”, IEEE Transactions on Industrial Informatics, Vol. 10, No. 2, pp. 1012–1022,
May 2014.
20. Enze Zhang, Yifei Wu and Qingwei Chen, “ A practical approach for solving multi-objective reliability redundancy
allocation problems using extended bare-bones particle swarm optimization”, Reliability Engineering & System Safety,
Vol. 127, pp. 65–76, July 2014.
21. Kangning Huang, Xiaoping Liu, Xia Li, Jiayong Liang and Shenjing He, “An improved artificial immune system for
seeking the Pareto front of land-use allocation problem in large areas”, International Journal of Geographical Information
Science, Vol. 27, No. 5, pp. 922–946, May 1, 2013.
22. Ullah Saif, Zailin Guan, Weiqi Liu, Chaoyong Zhang and Baoxi Wang, “Pareto based artificial bee colony algorithm for
multi objective single model assembly line balancing with uncertain task times”, Computers & Industrial Engineering,
Vol. 76, pp. 1–15, October 2014.
23. Weijian Kong, Tianyou Chai, Jinliang Ding and Shengxiang Yang, “Multifurnace Optimization in Electric Smelting
Plants by Load Scheduling and Control”, IEEE Transactions on Automation Science and Engineering, Vol. 11, No. 3,
pp. 850–862, July 2014.
24. A. Ghanei, E. Assareh, M. Biglari, A. Ghanbarzadeh and A.R. Noghrehabadi, “Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO)”, Heat and Mass Transfer, Vol. 50,
No. 10, pp. 1375–1384, October 2014.
25. Ali Sadollah, Hadi Eskandar and Joong Hoon Kim, “Water cycle algorithm for solving constrained multi-objective
optimization problems”, Applied Soft Computing, Vol. 27, pp. 279–298, February 2015.
26. Yan-Yan Tan, Yong-Chang Jiao, Hong Li and Xin-Kuan Wang, “MOEA/D-SQA: a multi-objective memetic algorithm
based on decomposition”, Engineering Optimization, Vol. 44, No. 9, pp. 1095–1115, 2012.
27. Mengqi Hu, Jeffery D. Weir and Teresa Wu, “An augmented multi-objective particle swarm optimizer for building cluster
operation decisions”, Applied Soft Computing, Vol. 25, pp. 347–359, December 2014.
28. Feng Zou, Lei Wang, Xinhong Hei, Debao Chen and Bin Wang, “Multi-objective optimization using teaching-learningbased optimization algorithm”, Engineering Applications of Artificial Intelligence, Vol. 26, No. 4, pp. 1291–1300, April
2013.
129
29. Maria Dominguez, Antonio Fernandez-Cardador, Asuncion P. Cucala, Tad Gonsalves and Adrian Fernandez, “Multi
objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines”, Engineering
Applications of Artificial Intelligence, Vol. 29, pp. 43–53, March 2014.
30. Nguyen Long, Lam T. Bui and Hussein A. Abbass, “DMEA-II: the direction-based multi-objective evolutionary algorithmII”, Soft Computing, Vol. 18, No. 11, pp. 2119–2134, November 2014.
31. S.A. Torabi, M. Hamedi and J. Ashayeri, “A new optimization approach for nozzle selection and component allocation
in multi-head beam-type SMD placement machines”, Journal of Manufacturing Systems, Vol. 32, No. 4, pp. 700–714,
October 2013.
32. Ping-Che Hsiao, Tsung-Che Chiang and Li-Chen Fu, “Static and dynamic minimum energy broadcast problem in wireless
ad-hoc networks: A PSO-based approach and analysis”, Applied Soft Computing, Vol. 13, No. 12, pp. 4786–4801,
December 2013.
33. M. Taheri, M.R. Alavi Moghaddam and M. Arami, “Techno-economical optimization of Reactive Blue 19 removal by
combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models”, Journal of Environmental Management, Vol. 128, pp. 798–806, October 15, 2013.
34. Mohammad-Reza Andervazh, Javad Olamaei and Mahmoud-Reza Haghifam, “Adaptive multi-objective distribution
network reconfiguration using multi-objective discrete particles swarm optimisation algorithm and graph theory”, IET
Generation Transmission & Distribution, Vol. 7, No. 12, pp. 1367–1382, December 2013.
35. S. Ramesh, S. Kannan and S. Baskar, “An improved generalized differential evolution algorithm for multi-objective
reactive power dispatch”, Engineering Optimization, Vol. 44, No. 4, pp. 391–405, 2012.
36. Jiuping Xu, Yan Tu and Ziqiang Zeng, “A Nonlinear Multiobjective Bilevel Model for Minimum Cost Network Flow
Problem in a Large-Scale Construction Project”, Mathematical Problems in Engineering, Article Number: 463976, 2012.
37. Soren Ebbesen, Christian Donitz and Lin Guzzella, “Particle swarm optimisation for hybrid electric drive-train sizing”,
International Journal of Vehicle Design, Vol. 58, Nos. 2-4, pp. 181–199, 2012.
38. Roghieh Karimzadeh Baee, Keyvan Forooraghi and Somayyeh Chamaani, “Conformal Array Pattern Synthesis Using a
Hybrid WARP/2LB-MOPSO Algorithm”, International Journal of Antennas and Propagation, Article Number: 202906,
2012.
39. T. Krausse, J. Cullmann, P. Saile and G.H. Schmitz, “Robust multi-objective calibration strategies - possibilities for
improving flood forecasting”, Hydrology and Earth System Sciences, Vol. 16, No. 10, pp. 3579–3606, 2012.
40. Hu Wang, Weiyi Li and Guangyao Li, “A Robust Inverse Method Based on Least Square Support Vector Regression for
Johnson-cook Material Parameters”, CMC-Computers Materials & Continua, Vol. 28, No. 2, pp. 121–146, April 2012.
41. Hui Lu and Xin Liu, “Compass Augmented Regional Constellation Optimization by a Multi-objective Algorithm Based
on Decomposition and PSO”, Chinese Journal of Electronics, Vol. 21, No. 2, pp. 374–378, April 2012.
42. Ya-Chin Chang, “Multi-Objective Optimal SVC Installation for Power System Loading Margin Improvement”, IEEE
Transactions on Power Systems, Vol. 27, No. 2, pp. 984–992, May 2012.
43. Mohammad Shafiul Alam, Md. Monirul Islam, Xin Yao and Kazuyuki Murase, “Diversity Guided Evolutionary Programming: A novel approach for continuous optimization”, Applied Soft Computing, Vol. 12, No. 6, pp. 1693–1707,
June 2012.
44. Jun Liu, Xuemei Ren and Hogbi Ma, “A new PSO algorithm with Random C/D Switchings”, Applied Mathematics and
Computation, Vol. 218, No. 19, pp. 9579–9593, June 1, 2012.
45. Taher Niknam, Rasoul Azizipanah-Abarghooee, Alireza Roosta and Babak Amiri, “A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch”, Energy, Vol. 42, No. 1, pp. 530–545, June
2012.
46. A. Chatterjee, S.P. Ghoshal and V. Mukherjee, “Solution of combined economic and emission dispatch problems of
power systems by an opposition-based harmony search algorithm”, International Journal of Electrical Power & Energy
Systems, Vol. 39, No. 1, pp. 9–20, July 2012.
47. Zhongkai Li, Zhencai Zhu, Yan Song and Zhe Wei, “A multi-objective particle swarm optimizer with distance ranking
and its applications to air compressor design optimization”, Transactions of the Institute of Measurement and Control,
Vol. 34, No. 5, pp. 546–556, July 2012.
48. S. PrasannaVenkatesan and S. Kumanan, “Multi-objective supply chain sourcing strategy design under risk using PSO
and simulation”, International Journal of Advanced Manufacturing Technology, Vol. 61, Nos. 1-4, pp. 325–337, July
2012.
49. Wenzhu Zhang, Kyung Sup Kwak and Chengxiao Feng, “Network Selection Algorithm for Heterogeneous Wireless Networks Based on Multi-Objective Discrete Particle Swarm Optimization”, KSII Transactions on Internet and Information
Systems, Vol. 6, No. 7, pp. 1802–1814, July 25, 2012.
50. K. Lakshmi and A. Rama Mohan Rao, “Hybrid shuffled frog leaping optimisation algorithm for multi-objective optimal
design of laminate composites”, Computers & Structures, Vol. 125, pp. 200–216, September 2013.
130
51. Fei Tao, Ying Feng, Lin Zhang and T.W. Liao, “CLPS-GA: A case library and Pareto solution-based hybrid genetic
algorithm for energy-aware cloud service scheduling”, Applied Soft Computing, Vol. 19, pp. 264–279, June 2014.
52. Arunanshu Mahapatro and Ajit Kumar Panda, “Choice of Detection Parameters on Fault Detection in Wireless Sensor
Networks: A Multiobjective Optimization Approach”, Wireless Personal Communications, Vol. 78, No. 1, pp. 649–669,
September 2014.
53. Pyari Mohan Pradhan and Ganapati Panda, “Connectivity constrained wireless sensor deployment using multiobjective
evolutionary algorithms and fuzzy decision making”, Ad Hoc Networks, Vol. 10, No. 6, pp. 1134–1145, August 2012.
54. Hao Tian, Xiaohui Yuan, Bin Ji and Zhihuan Chen, “Multi-objective optimization of short-term hydrothermal scheduling
using non-dominated sorting gravitational search algorithm with chaotic mutation”, Energy Conversion and Management, Vol. 81, pp. 504–519, May 2014.
55. Shan Cheng and Min-You Chen, “Multi-objective reactive power optimization strategy for distribution system with
penetration of distributed generation”, International Journal of Electrical Power & Energy Systems, Vol. 62, pp. 221–
228, November 2014.
56. Kian Sheng Lim, Zuwairie Ibrahim, Salinda Buyamin, Anita Ahmad, Faradila Naim, Kamarul Hawari Ghazali, Norrima Mokhtar, “Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions”,
Scientific World Journal, Article Number: 510763, 2013.
57. Kian Sheng Lim, Salinda Buyamin, Anita Ahmad, Mohd Ibrahim Shapiai, Faradila Naim, Marizan Mubin and Dong
Hwa Kim, “Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders”, Scientific
World Journal, Article Number: 364179, 2014.
58. Kaveh Khalili-Damghani, Amir-Reza Abtahi and Madjid Tavana, “A new multi-objective particle swarm optimization
method for solving reliability redundancy allocation problems”, Reliability Engineering & System Safety, Vol. 111, pp.
58–75, March 2013.
59. Hanning Chen, Ma Lian Bo, Yunlong Zhu, “Multi-hive bee foraging algorithm for multi-objective optimal power flow
considering the cost, loss, and emission”, International Journal of Electrical Power & Energy Systems, Vol. 60, pp.
203–220, September 2014.
60. Zhongkai Li, Guangdong Tian, Gang Cheng, Houguang Liu and Zhihong Cheng, “An integrated cultural particle swarm
algorithm for multi-objective reliability-based design optimization”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical Engineering Science, Vol. 228, No. 7, pp. 1185–1196, May 2014.
61. Shan Cheng, Min-you Chen, Rong-jong Wai and Fang-zong Wang, “Optimal placement of distributed generation units
in distribution systems via an enhanced multi-objective particle swarm optimization algorithm”, Journal of Zhejiang
University–Science C–Computers & Electronics, Vol. 15, No. 4, pp. 300–311, April 2014.
62. Maoguo Gong, Qing Cai, Xiaowei Chen and Lijia Ma, “Complex Network Clustering by Multiobjective Discrete Particle
Swarm Optimization Based on Decomposition”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 1, pp.
82–97, February 2014.
63. Sajad Tabatabaei, “A new gravitational search optimization algorithm to solve single and multiobjective optimization
problems”, Journal of Intelligent & Fuzzy Systems, Vol. 26, No. 2, pp. 993–1006, 2014.
64. Zhi-Hui Zhan, Jingjing Li, Jiannong Cao, Jun Zhang, Henry Shu-Hung Chung and Yu-Hui Shi, “Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems”, IEEE
Transactions on Cybernetics, Vol. 43, No. 2, pp. 445–463, April 2013.
65. Bahareh Kamali, S. Jamshid Mousavi and K.C. Abbaspour, “Automatic calibration of HEC-HMS using single-objective
and multi-objective PSO algorithms”, Hydrological Processes, Vol. 27, No. 26, pp. 4028–4042, December 30, 2013.
66. Eusebio Angulo, Enrique Castillo, Ricardo Garcia-Rodenas and Jesus Sanchez-Vizcaino, “A continuous bi-level model
for the expansion of highway networks”, Computers & Operations Research, Vol. 41, pp. 262–276, January 2014.
67. H.C.W. Lau, G.T.S. Ho, T.M. Chan and T.W. Tsui, “An innovation approach for achieving cost optimization in supply
chain management”, Journal of Intelligent & Fuzzy Systems, Vol. 26, No. 1, pp. 173–192, 2014.
68. Mazdak Shokrian and Karen Ann High, “Application of a multi objective multi-leader particle swarm optimization
algorithm on NLP and MINLP problems”, Computers & Chemical Engineering, Vol. 60, pp. 57–75, January 10, 2014.
69. Hao Quan, Dipti Srinivasan and Abbas Khosravi, “Particle swarm optimization for construction of neural network-based
prediction intervals”, Neurocomputing, Vol. 127, pp. 172–180, March 15, 2014.
70. B. Latha Shankar, S. Basavarajappa, Rajeshwar S. Kadadevaramath and Jason C.H. Chen, “A bi-objective optimization
of supply chain design and distribution operations using non-dominated sorting algorithm: A case study”, Expert Systems
with Applications, Vol. 40, No. 14, pp. 5730–5739, October 15, 2013.
71. Seyed Hamid Reza Pasandideh, Seyed Taghi Akhavan Niaki and Sharareh Sharafzadeh, “Optimizing a bi-objective
multi-product EPQ model with defective items, rework and limited orders: NSGA-II and MOPSO algorithms”, Journal
of Manufacturing Systems, Vol. 32, No. 4, pp. 764–770, October 2013.
131
72. Taher Niknam, Rasoul Azizipanah-Abarghooee, Mohsen Zare and Bahman Bahmani-Firouzi, “Reserve Constrained
Dynamic Environmental/Economic Dispatch: A New Multiobjective Self-Adaptive Learning Bat Algorithm”, IEEE
Systems Journal, Vol. 7, No. 4, pp. 763–776, December 2013.
73. Ki-Baek Lee and Jong-Hwan Kim, “ Multiobjective Particle Swarm Optimization With Preference-Based Sort and
Its Application to Path Following Footstep Optimization for Humanoid Robots”, IEEE Transactions on Evolutionary
Computation, Vol. 17, No. 6, pp. 755–766, December 2013.
74. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
75. Huifeng Zhang, Jianzhong Zhou, Na Fang, Rui Zhang and Yongchuan Zhang, “Daily hydrothermal scheduling with
economic emission using simulated annealing technique based multi-objective cultural differential evolution approach”,
Energy, Vol. 50, pp. 24–37, February 1, 2013.
76. Xianpeng Wang and Lixin Tang, “ Multiobjective Operation Optimization of Naphtha Pyrolysis Process Using Parallel
Differential Evolution”, Industrial & Engineering Chemistry Research, Vol. 52, No. 40, pp. 14415–14428, October 9,
2013.
77. Engin Ufuk Ergul and Ilyas Eminoglu, “DOPGA: a new fitness assignment scheme for multi-objective evolutionary
algorithms”, International Journal of Systems Science, Vol. 45, No. 3, pp. 407–426, March 1, 2014.
78. Xingjuan Cai and Ying Tan, “A study on the effect of upsilon(max) in particle swarm optimisation with high dimension”,
International Journal of Bio-Inspired Computation, Vol. 1, No. 3, pp. 210–216, 2009.
79. Qi Kang, Lei Wang and Qidi Wu, “Swarm-based approximate dynamic optimization process for discrete particle swarm
optimization system”, International Journal of Bio-Inspired Computation, Vol. 1, Nos. 1-2, pp. 61–70, 2009.
80. Fuqing Zhao, Qiuyu Zhang and Yahong Yang, “Petri net modeling method to scheduling problem of holonic manufacturing system (HMS) and its solution with a hybrid PSO algorithm”, in De-Shuang Huang, Kang Li and George William
Irwin (editors), Intelligent Control and Automation, International Conference on Intelligent Computing, ICIC 2006, pp.
361–372, Springer, Lecture Notes in Control and Information Sciences Vol. 344, Kunming, China, August 16-19, 2006.
81. Durul Ulutan and Tugrul Özel, “Multiobjective Optimization of Experimental and Simulated Residual Stresses in Turning
of Nickel-Alloy IN100”, Materials and Manufacturing Processes, Vol. 28, No. 7, pp. 835–841, July 3, 2013.
82. Heming Xu, Yinglin Wang and Xin Xu, “Multiobjective Particle Swarm Optimization based on Dimensional Update”,
International Journal on Artificial Intelligence Tools, Vol. 22, No. 3, Article Number: 1350015, June 2013.
83. Yu-Jun Zheng and Sheng-Yong Chen, “Cooperative particle swarm optimization for multiobjective transportation planning”, Applied Intelligence, Vol. 39, No. 1, pp. 202–216, July 2013.
84. Sultan Nomal Qasem, Siti Mariyam Shamsuddin, Siti Zaiton Mohd Hashim, Maslina Darus and Eiman Al-Shammari,
“Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems”,
Information Sciences, Vol. 239, pp. 165–190, August 1, 2013.
85. Chia-Hung Hsu and Chia-Feng Juang, “Multi-Objective Continuous-Ant-Colony-Optimized FC for Robot Wall-Following
Control”, IEEE Computational Intelligence Magazine, Vol. 8, No. 3, pp. 28–40, August 2013.
86. Satyasai Jagannath Nanda and Ganapati Panda, “Automatic clustering algorithm based on multi-objective Immunized
PSO to classify actions of 3D human models”, Engineering Applications of Artificial Intelligence, Vol. 26, Nos. 5-6, pp.
1429–1441, May-June 2013.
87. Hamed Zeinoddini-Meymand, Behrooz Vahidi, Ramezan Ali Naghizadeh and Moosa Moghimi-Haji, “Optimal Surge
Arrester Parameter Estimation Using a PSO-Based Multiobjective Approach”, IEEE Transactions on Power Delivery,
Vol. 28, No. 3, pp. 1758–1769, July 2013.
88. Chenye Qiu, Chunlu Wang and Xingquan Zuo, “A novel multi-objective particle swarm optimization with K-means
based global best selection strategy”, International Journal of Computational Intelligence Systems, Vol. 6, No. 5, pp.
822–835, September 2013.
89. T. Niknam, M.R. Narimani, J. Aghaei and R. Azizipanah-Abarghooee, “Improved particle swarm optimisation for
multi-objective optimal power flow considering the cost, loss, emission and voltage stability index”, IET Generation,
Transmission & Distribution, Vol. 6, No. 6, pp. 515–527, June 2012.
90. Yang Liu and Fan Sun, “Parameter estimation of a pressure swing adsorption model for air separation using multiobjective optimisation and support vector regression model”, Expert Systems with Applications, Vol. 40, No. 11, pp.
4496–4502, September 1, 2013.
91. Bo Wang and Junzo Watada, “Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem”,
IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, No. 2, pp. 146–154, March 2013.
92. Jianguang Fang, Yunkai Gao, Guangyong Sun and Qing Li, “Multiobjective reliability-based optimization for design of
a vehicledoor”, Finite Elements in Analysis and Design, Vol. 67, pp. 13–21, May 2013.
93. Maoguo Gong, Xiaowei Chen, Lijia Ma, Qingfu Zhang and Licheng Jiao, “Identification of multi-resolution network
structures with multi-objective immune algorithm”, Applied Soft Computing, Vol. 13, No. 4, pp. 1705–1717, April 2013.
132
94. Yang Liu and Gareth Pender, “Automatic calibration of a rapid flood spreading model using multiobjective optimisations”, Soft Computing, Vol. 17, No. 4, pp. 713–724, April 2013.
95. Mohammad Rasoul Narimani, Rasoul Azizipanah-Abarghooee, Behrouz Zoghdar-Moghadam-Shahrekohne and Kayvan
Gholami, “A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering
generator constraints and multi-fuel type”, Energy, Vol. 49, pp. 119–136, January 1, 2013.
96. Ran Li, Huizhuo Ma, Feifei Wang, Yihe Wang, Yang Liu and Zenghui Li, “Game Optimization Theory and Application
in Distribution System Expansion Planning, Including Distributed Generation”, Energies, Vol. 6, No. 2, pp. 1101–1124,
February 2013.
97. Jingrong Yu, Shiqi Ding, Yijun Wang, Weibiao Wu and Mi Dong, “The engineering design and optimization of main
circuit for hybrid active power filter”, International Journal of Electrical Power & Energy Systems, Vol. 46, pp. 40–48,
March 2013.
98. Lixin Tang and Xianpen Wang, “A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization
Problems”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 20–45, February 2013.
99. Hamid Ali, Waseem Shahzad and Farrukh Aslam Khan, “Energy-efficient clustering in mobile ad-hoc networks using
multi-objective particle swarm optimization”, Applied Soft Computing, Vol. 12, No. 7, pp. 1913–1928, July 2012.
100. Yong Zhang, Dun-wei Gong and Jian-hua Zhang, “Robot path planning in uncertain environment using multi-objective
particle swarm optimization”, Neurocomputing, Vol. 103, pp. 172–185, March 1, 2013.
101. Chang Qi, Shu Yang and Fangliang Dong, “Crushing analysis and multiobjective crashworthiness optimization of tapered
square tubes under oblique impact loading”, Thin-Walled Structures, Vol. 59, pp. 103–119, October 2012.
102. Taher Niknam, Rasoul Azizipanah-Abarghooee and Mohammad Rasoul Narimani, “A new multi objective optimization
approach based on TLBO for location of automatic voltage regulators in distribution systems”, Engineering Applications
of Artificial Intelligence, Vol. 25, No. 8, pp. 1577–1588, December 2012.
103. Lie-Jane Kao and Cheng-Few Lee, “Alternative method for determining industrial bond ratings: theory and empirical
evidence”, International Journal of Information Technology & Decision Making, Vol. 11, No. 6, pp. 1215–1235,
November 2012.
104. Anabel Martinez-Vargas and Angel G. Andrade, “Comparing particle swarm optimization variants for a cognitive radio
network”, Applied Soft Computing, Vol. 13, No. 2, pp. 1222–1234, February 2013.
105. Taher Niknam, Mohammad Rasoul Narimani and Rasoul Azizipanah-Abarghooee, “A Multi-objective Fuzzy Adaptive
PSO Algorithm for Location of Automatic Voltage Regulators in Radial Distribution Networks”, International Journal
of Control Automation and Systems, Vol. 10, No. 4, pp. 772–777, August 2012.
106. Zhou Wu and Tommy W.S. Chow, “A local multiobjective optimization algorithm using neighborhood field”, Structural
and Multidisciplinary Optimization, Vol. 46, No. 6, pp. 853–870, December 2012.
107. Baabak Ashuri and Mehdi Tavakolan, “Fuzzy Enabled Hybrid Genetic Algorithm-Particle Swarm Optimization Approach
to Solve TCRO Problems in Construction Project Planning”, Journal of Construction Engineering and Management–
ASCE, Vol. 138, No. 9, pp. 1065–1074, September 2012.
108. T. Niknam and H. Doagou-Mojarrad, “Multiobjective economic/emission dispatch by multiobjective theta-particle
swarm optimisation”, IET Generation Transmission & Distribution, Vol. 6, No. 5, pp. 363–377, May 2012.
109. Xiang Li and Gang Du, “BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems”,
Computers & Operations Research, Vol. 40, No. 1, pp. 282–302, January 2013.
110. Maoguo Gong, Lijia Ma, Qingfu Zhang and Licheng Jiao, “Community detection in networks by using multiobjective
evolutionary algorithm with decomposition”, Physica A–Statistical Mechanics and Its Applications, Vol. 391, No. 15,
pp. 4050–4060, August 1, 2012.
111. Mao-Guo Gong, Ling-Jun Zhang, Jing-Jing Ma and Li-Cheng Jiao, “Community Detection in Dynamic Social Networks
Based on Multiobjective Immune Algorithm”, Journal of Computer Science and Technology, Vol. 27, No. 3, pp. 455–467,
May 2012.
112. W.K. Wong, S.Y.S. Leung and Z.X. Guo, “Feedback controlled particle swarm optimization and its application in
time-series prediction”, Expert Systems with Applications, Vol. 39, No. 10, pp. 8557–8572, August 2012.
113. Maoguo Gong, Lijia Ma, Qingfu Zhang and Licheng Jiao, “Community detection in networks by using multiobjective
evolutionary algorithm with decomposition”, Physica A-Statistical Mechanics and its Applications, Vol. 391, No. 15,
pp. 4050-4060, August 1, 2012.
114. Muhammad Naeem, Udit Pareek and Daniel C. Lee, “Swarm Intelligence for Sensor Selection Problems”, IEEE Sensors
Journal, Vol. 12, No. 8, pp. 2577–2585, August 2012.
115. Adam Pedrycz, Kaoru Hirota, Witold Pedrycz and Fangya Dong, “Granular representation and granular computing
with fuzzy sets”, Fuzzy Sets and Systems, Vol. 203, pp. 17–32, September 16, 2012.
133
116. Jiuping Xu and Zongmin Li, “Multi-Objective Dynamic Construction Site Layout Planning in Fuzzy Random Environment”, Automation in Construction, Vol. 27, pp. 155–169, November 2012.
117. Yan-Yan Tan, Yong-Chang Jiao, Hong Li and Xin-Kuan Wang, “A modification to MOEA/D-DE for multiobjective
optimization problems with complicated Pareto sets”, Information Sciences, Vol. 213, pp. 14–38, December 5, 2012.
118. Hao Zhang, Yunlonh Zhu, Wenping Zou and Xiaohui Yan, “A hybrid multi-objective artificial bee colony algorithm for
burdening optimization of copper strip production”, Applied Mathematical Modelling, Vol. 36, No. 6, pp. 2578–2591,
June 2012.
119. Amirhossain Chambari, Seyed Habib A. Rahmati, Amir Abbas Najafi and Aida Karimi, “A bi-objective model to
optimize reliability and cost of system with a choice of redundancy strategies”, Computers & Industrial Engineering,
Vol. 63, No. 1, pp. 109–119, August 2012.
120. Jun Liu, Xuemei Ren and Hongbin Ma, “Adaptive swarm optimization for locating and tracking multiple targets”,
Applied Soft Computing, Vol. 12, No. 11, pp. 3656–3670, November 2012.
121. Yong Wang, Jian Xiang and Zixing Cai, “A regularity model-based multiobjective estimation of distribution algorithm
with reducing redundant cluster operator”, Applied Soft Computing, Vol. 12, No. 11, pp. 3526–3538, November 2012.
122. I-Tung Yang, Yo-Ming Hsieh and Li-Ou Kung, “Parallel Computing Platform for Multiobjective Simulation Optimization
of Bridge Maintenance Planning”, Journal of Construction Engineering and Management–ASCE, Vol. 138, No. 2, pp.
215–226, February 2012.
123. Feng Qian, Bing Xu, Rongbin Qi and Huaglory Tianfield, “Self-adaptive differential evolution algorithm with αconstrained-domination principle for constrained multi-objective optimization”, Soft Computing, Vol. 16, No. 8, pp.
1353–1372, August 2012.
124. Davide Bianchi, Simone Genovesi and Agostino Monorchio, “Constrained Pareto Optimization of Wide Band and Steerable Concentric Ring Arrays”, IEEE Transactions on Antennas and Propagation, Vol. 60, No. 7, pp. 3195–3204, July
2012.
125. Satoshi Kitayama and Koetsu Yamazaki, “Compromise point incorporating trade-off ratio in multi-objective optimization”, Applied Soft Computing, Vol. 12, No. 8, pp. 1959–1964, August 2012.
126. Fangqing Gu, Hai-lin Liu and Kay Chen Tan, “A Multiobjective Evolutionary Algorithm using Dynamic Weight Design
Method”, International Journal of Innovative Computing Information and Control, Vol. 8, No. 5B, pp. 3677–3688, May
2012.
127. Yakoub Bazi, Naif Alajlan and Farid Melgani, “Improved Estimation of Water Chlorophyll Concentration With Semisupervised Gaussian Process Regression”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 7, pp.
2733–2743, Part 2, July 2012.
128. Yong Zhang, Dun-Wei Gong and Zhonghai Ding, “A bare-bones multi-objective particle swarm optimization algorithm
for environmental/economic dispatch”, Information Sciences, Vol. 192, pp. 213–227, June 1, 2012.
129. Francesco Castellini and Michele R. Lavagna, “Comparative Analysis of Global Techniques for Performance and Design
Optimization of Launchers”, Journal of Spacecraft and Rockets, Vol. 49, No. 2, pp. 274–285, March-April 2012.
130. A. Boloori Arabani, M. Zandieh and S.M.T. Fatemi Ghomi, “A cross-docking scheduling problem with sub-population
multi-objective algorithms”, International Journal of Advanced Manufacturing Technology, Vol. 58, Nos. 5-8, pp. 741–
761, January 2012.
131. A. Kaveh and K. Laknejadi, “A Hybrid Multi-Objective Optimization and Decision Making Procedure for Optimal
Design of Truss Structures”, Iranian Journal of Science and Technology–Transactions of Civil Engineering, Vol. 35, No.
C2, pp. 137–154, August 2011.
132. Reza Akbari and Koorush Ziarati, “Multi-objective Bee Swarm Optimization”, International Journal of Innovative
Computing Information and Control, Vol. 8, No. 1B, pp. 715–726, January 2012.
133. Ali Kaveh, Karim Laknejadi and Babak Alinejad, “Performance-based multi-objective optimization of large steel structures”, Acta Mechanica, Vol. 223, No. 2, pp. 355–369, February 2012.
134. Wen-an Yang, Yu Guo and Wenhe Liao, “Economic and statistical design of (X)over-bar and S control charts using an
improved multi-objective particle swarm optimisation algorithm”, International Journal of Production Research, Vol.
50, No. 1, pp. 97–117, 2012.
135. Minh-Trien Pham, Diahai Zhang and Chang Seop Koh, “Multi-Guider and Cross-Searching Approach in Multi-Objective
Particle Swarm Optimization for Electromagnetic Problems”, IEEE Transactions on Magnetics, Vol. 48, No. 2, pp.
539–542, February 2012.
136. Chunshien Li and Jhao-Wun Hu, “A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series
forecasting”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 2, pp. 295–308, March 2012.
137. Leandro dos S. Coelho, Fabio A. Guerra and Jean V. Leite, “Multiobjective Exponential Particle Swarm Optimization
Approach Applied to Hysteresis Parameters Estimation”, IEEE Transactions on Magnetics, Vol. 48, No. 2, pp. 283–286,
February 2012.
134
138. Amjad Anvari Moghaddam, Alireza Seifi and Taher Niknam, “Multi-operation management of a typical micro-grids
using Particle Swarm Optimization: A comparative study”, Renewable & Sustainable Energy Reviews, Vol. 16, No. 2,
pp. 1268–1281, February 2012.
139. C.W. Bong and M. Rajeswari, “Multiobjective clustering with metaheuristic: current trends and methods in image
segmentation”, IET Image Processing, Vol. 6, No. 1, pp. 1–10, February 2012.
140. A. Farshidianfar, A. Saghafi, S.M. Kalami and I. Saghafi, “Active vibration isolation of machinery and sensitive equipment
using H (a) control criterion and particle swarm optimization method”, Mecchanica, Vol. 47, No. 2, pp. 437–453,
February 2012.
141. C.-N. Ko, C.-C. Yang and C.-J. Wu, “A particle swarm optimization-based time-scaling method for quasi-time-optimal
control of rigid spacecraft along specified paths”, Proceedings of the Institution of Mechanical Engineers Part I–Journal
of Systems and Control Engineering, Vol. 222, No. I1, pp. 1–9, February 2008.
142. Taohong Zhang, Linxin Li, Fujun Liang and Bingru Yang, “Parameter optimization of laser die-surface hardening using
the particle swarm optimization technique”, International Journal of Advanced Manufacturing Technology, Vol. 36, Nos.
11-12, pp. 1104–1112, April 2008.
143. Zne-Jung Lee, “A novel hybrid algorithm for function approximation”, Expert Systems with Applications, Vol. 34, No.
1, pp. 384–390, January 2008.
144. Zne-Jung Lee, “An integrated algorithm for gene selection and classification applied to microarray data of ovarian
cancer”, Artificial Intelligence in Medicine, Vol. 42, No. 1, pp. 81–93, January 2008.
145. Peng-Yeng Yin and Jing-Yu Wang, “Optimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique”, Journal of Computational and Applied Mathematics, Vol. 216, No.
1, pp. 73–86, June 15, 2008.
146. Zne-Jung Lee, “A robust learning algorithm based on support vector regression and robust fuzzy cerebellar model
articulation controller”, Applied Intelligence, Vol. 29, No. 1, pp. 47–55, August 2008.
147. Vijay Kalivarapu, Jung-Leng Foo and Eliot Winer, “Improving solution characteristics of particle swarm optimization
using digital pheromones”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 415–427, January 2009.
148. Shih-Wei Lin and Shih-Chieh Chen, “PSOLDA: A particle swarm optimization approach for enhancing classification
accuracy rate of linear discriminant analysis”, Applied Soft Computing, Vol. 9, No. 3, pp. 1008–1015, June 2009.
149. Yang Liu, “Automatic calibration of a rainfall-runoff model using a fast and elitist multi-objective particle swarm
algorithm”, Expert Systems with Applications, Vol. 36, No. 5, pp. 9533–9538, July 2009.
150. Peng-Yeng Yin, Fred Glover, Manuel Laguna and Jia-Xian Zhu, “Cyber Swarm Algorithms - Improving particle swarm
optimization using adaptive memory strategies”, European Journal of Operational Research, Vol. 201, No. 2, pp.
377–389, March 1, 2010.
151. Maria Alejandra Guzman, Alberto Delgado and Jonas De Carvalho, “A novel multiobjective optimization algorithm
based on bacterial chemotaxis”, Engineering Applications of Artificial Intelligence, Vol. 23, No. 3, pp. 292–301, April
2010.
152. Moayed Daneshyari and Gary G. Yen, “Cultural-Based Multiobjective Particle Swarm Optimization”, IEEE Transactions
on Systems, Man and Cybernetics Part B—Cybernetics, Vol. 41, No. 2, pp. 553–567, April 2011.
153. Guilong Wang, Guoqun Zhao, Huiping Li and Yanjin Guan, “Multi-objective optimization design of the heating/cooling
channels of the steam-heating rapid thermal response mold using particle swarm optimization”, International Journal
of Thermal Sciences, Vol. 50, No. 5, pp. 790–802, May 2011.
154. Youlin Lu, Jianzhong Zhou, Hui Qin, Ying Wang and Yongchuan Zhang, “A hybrid multi-objective cultural algorithm
for short-term environmental/economic hydrothermal scheduling”, Energy Conversion and Management, Vol. 52, No.
5, pp. 2121–2134, May 2011.
155. Hamid Reza Golmakani and Mehrshad Fazel, “Constrained Portfolio Selection using Particle Swarm Optimization”,
Expert Systems with Applications, Vol. 38, No. 7, pp. 8327–8335, July 2011.
156. B.K. Panigrahi, V. Ravikumar Pandi, Sanjoy Das and Swagatam Das, “Multiobjective fuzzy dominance based bacterial
foraging algorithm to solve economic emission dispatch problem”, Energy, Vol. 35, No. 12, pp. 4761–4770, December
2010.
157. G.S. Piperagkas, A.G. Anastasiadis and N.D. Hatziargyriou, “Stochastic PSO-based heat and power dispatch under
environmental constraints incorporating CHP and wind power units”, Electric Power Systems Research, Vol. 81, No. 1,
pp. 209–218, January 2011.
158. A. Boloori Arabani, M. Zandieh and S.M.T. Fatemi Ghomi, “Multi-objective genetic-based algorithms for a cross-docking
scheduling problem”, Applied Soft Computing, Vol. 11, No. 8, pp. 4954–4970, December 2011.
159. Yang Tang, Zidong Wang and Jian-an Fang, “Feedback learning particle swarm optimization”, Applied Soft Computing,
Vol. 11, No. 8, pp. 4713–4725, December 2011.
135
160. De-bao Chen, Feng Zou and Jiang-tao Wang, “A multi-objective endocrine PSO algorithm and application”, Applied
Soft Computing, Vol. 11, No. 8, pp. 4508–4520, December 2011.
161. Zhi-Hui Zhan, Jun Zhang, Yun Li and Yu-Hui Shi, “Orthogonal Learning Particle Swarm Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 15, No. 6, pp. 832–847, December 2011.
162. Wei Huang, Sung-Kwun Oh, Lixin Ding, Hyun-Ki Kim and Su-Chong Joo, “Identification of Fuzzy Inference Systems
Using a Multi-objective Space Search Algorithm and Information Granulation”, Journal of Electrical Engineering &
Technology, Vol. 6, No. 6, pp. 853–866, November 2011.
163. Mohammad Shafiul Alam, Md. Monirul Islam, Xin Yao and Kazuyuk Murase, “Recurring Two-Stage Evolutionary
Programming: A Novel Approach for Numeric Optimization”, IEEE Transactions on Systems, Man, and Cybernetics
Part B–Cybernetics, Vol. 41, No. 5, pp. 1352–1365, October 2011.
164. Ruiyi Su, Liangjin Gui and Zijie Fan, “Multi-objective optimization for bus body with strength and rollover safety
constraints based on surrogate models”, Structural and Multidisciplinary Optimization, Vol. 44, No. 3, pp. 431–441,
September 2011.
165. Keith Worden, Wieslaw J. Staszewski and James J. Hensman, “Natural computing for mechanical systems research: A
tutorial overview”, Mechanical Systems and Signal Processing, Vol. 25, No. 1, pp. 4–111, January 2011.
166. Leandro dos Santos Coelho, Helon Vicente Hultmann Ayala and Piergiorgio Alotto, “A Multiobjective Gaussian Particle
Swarm Approach Applied to Electromagnetic Optimization ”, IEEE Transactions on Magnetics, Vol. 46, No. 8, pp.
3289–3292, August 2010.
167. A. Kaveh and K. Laknejadi, “A novel hybrid charge system search and particle swarm optimization method for multiobjective optimization”, Expert Systems with Applications, Vol. 38, No. 12, pp. 15475–15488, November-December
2011.
168. Jingxuan Wei, Yuping Wang and Hua Wang, “A Hybrid Particle Swarm Evolutionary Algorithm for Constrained MultiObjective Optimization”, Computing and Informatics, Vol. 29, No. 5, pp. 701–718, 2010.
169. Xixiang Yang and Weihua Zhang, “An Improved Multi-Objective Particle Swarm Optimization”, Advanced Science
Letters, Vol. 4, Nos. 4-5, pp. 1491–1495, April-May 2011.
170. Guang-ho Hu, Zhi-zhong Mao and Da-kuo He, “Multi-objective optimization for leaching process using improved twostage guide PSO algorithm”, Journal of Central South University of Technology, Vol. 18, No. 4, pp. 1200–1210, August
2011.
171. Yong Zhang, Dun-wei Gong and Zhong-hai Ding, “Handling multi-objective optimization problems with a multi-swarm
cooperative particle swarm optimizer”, Expert Systems with Applications, Vol. 38, No. 11, pp. 13933–13941, October
2011.
172. Chi Zhoum Xuejun Zhang, Kaiquan Cai and Jun Zhang, “Comprehensive Learning Multi-Objective Particle Swarm
Optimizer for Crossing Waypoints Location in Air Route Network”, Chinese Journal of Electronics, Vol. 20, No. 3, pp.
533–538, July 2011.
173. H. Amin-Tahmasbi and R. Tavakkoli-Moghaddam, “Solving a bi-objective flowshop scheduling problem by a Multiobjective Immune System and comparing with SPEA2+and SPGA”, Advances in Engineering Software, Vol. 42, No.
10, pp. 772–779, October 2011.
174. H. Moslemi and M. Zandieh, “Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory
system”, Expert Systems with Applications, Vol. 38, No. 10, pp. 12051–12057, September 15, 2011.
175. N.C. Sahoo, S. Ganguly and D. Das, “Simple heuristics-based selection of guides for multi-objective PSO with an
application to electrical distribution system planning”, Engineering Applications of Artificial Intelligence, Vol. 24, No.
4, pp. 567–585, June 2011.
176. Tad Gonsalves and Kiyoshi Itoh, “GA optimization of Petri net-modeled concurrent service systems”, Applied Soft
Computing, Vol. 11, No. 5, pp. 3929–3937, July 2011.
177. Jiuping Xu and Fang Yan, “A multi-objective decision making model for the vendor selection problem in a bifuzzy
environment”, Expert Systems with Applications, Vol. 38, No. 8, pp. 9684–9695, August 2011.
178. Somayyeh Chamaani, S. Abdullah Mirtaheri and Mohammad S. Abrishamian, “Improvement of Time and Frequency
Domain Performance of Antipodal Vivaldi Antenna Using Multi-Objective Particle Swarm Optimization”, IEEE Transactions on Antennas and Propagation, Vol. 59, No. 5, pp. 1738–1742, May 2011.
179. Yen-Liang Chen and Xiang-Han Chen, “An evolutionary PageRank approach for journal ranking with expert judgements”, Journal of Information Science, Vol. 37, No. 3, pp. 254–272, June 2011.
180. Jiaquan Gao and Jun Wang, “A hybrid quantum-inspired immune algorithm for multiobjective optimization”, Applied
Mathematics and Computation, Vol. 217, No. 9, pp. 4754–4770, January 1, 2011.
181. Ping-Feng Pai, Ming-Fu Hsu and Ming-Chieh Wang, “A support vector machine-based model for detecting top management fraud”, Knowledge-Based Systems, Vol. 24, No. 2, pp. 314–321, March 2011.
136
182. C.W. Hudson, J.J. Carruthers and A.M. Robinson, “A comparison of three population-based optimization techniques for
the design of composite sandwich materials”, Journal of Sandwich Structures & Materials, Vol. 13, No. 2, pp. 213–235,
March 2011.
183. Miltiadis Kotinis, “Implementing co-evolution and parallelization in a multi-objective particle swarm optimizer”, Engineering Optimization, Vol. 43, No. 6, pp. 635–656, June 2011.
184. S. Jeyadevi, S. Baskar, C.K. Babulal, M. Willjuice Iruthayarajan, “Solving multiobjective optimal reactive power dispatch
using modified NSGA-II”, International Journal of Electrical Power & Energy Systems, Vol. 33, No. 2, pp. 219–228,
February 2011.
185. Elisa Vazquez, Joaquim Ciurana, Ciro A. Rodriguez, Thanongsak Thepsonthi and Tugrul Özel, “Swarm Intelligent
Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices”, Materials
and Manufacturing Processes, Vol. 26, No. 3, pp. 403–414, 2011.
186. Chin-Wei Bong and Mandava Rajeswari, “Multi-objective nature-inspired clustering and classification techniques for
image segmentation”, Applied Soft Computing, Vol. 11, No. 4, pp. 3271–3282, June 2011.
187. Peifeng Wu, Liqun Gao, Dexuan Zou and Steven Li, “An improved particle swarm optimization algorithm for reliability
problems”, ISA Transactions, Vol. 50, No. 1, pp. 71–81, January 2011.
188. Hong Xiao, Yuan Li, Kaifu Zhang, Jainfeng Yu, Zhenxing Liu and Jianbin Su, “Multi-objective Optimization Method
for Automatic Drilling and Riveting Sequence Planning”, Chinese Journal of Aeronautics, Vol. 23, No. 6, pp. 734–742,
December 2010.
189. Jamal Saeedi and Karim Faez, “A new pan-sharpening method using multiobjective particle swarm optimization and the
shiftable contourlet transform”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 3, pp. 365–381,
May 2011.
190. D.S. Liu, K.C. Tan, S.Y. Huang, C.X. Goh and W.K. Ho, “On solving multiobjective bin packing problems using
evolutionary particle swarm optimization”, European Journal of Operational Research, Vol. 190, No. 2, pp. 357–382,
October 16, 2008.
191. James Bekker and Chris Aldrich, “The cross-entropy method in multi-objective optimisation: An assessment”, European
Journal of Operational Research, Vol. 211, No. 1, pp. 112–121, May 16, 2011.
192. Yuanxia Shen, Guoyin Wang and Chunmei Tao, “Particle Swarm Optimization with Novel Processing Strategy and Its
Application”, International Journal of Computational Intelligence Systems, Vol. 4, No. 1, pp. 100–111, February 2011.
193. Prithwish Chakraborty, Swagatam Das, Gourab Ghosh Roy and Ajith Abraham, “On convergence of the multi-objective
particle swarm optimizers”, Information Sciences, Vol. 181, No. 8, pp. 1411–1425, April 15, 2011.
194. Xiangwei Zheng and Hong Liu, “A scalable coevolutionary multi-objective particle swarm optimizer”, International
Journal of Computational Intelligence Systems, Vol. 3, No. 5, pp. 590–600, October 2010.
195. Nannan Yan and Zhengcai Fu, “Optimization and Coordination of UPFC Controls Using MOPSO”, International Review
of Electrical Engineering–IREE, Vol. 5, No. 5, pp. 2327–2332, Part B, September-October 2010.
196. Miltiadis Kotinis, “A particle swarm optimizer for constrained multi-objective engineering design problems”, Engineering
Optimization, Vol. 42, No. 10, pp. 907–926, October 2010.
197. S.-Z. Zhao and P.N. Suganthan, “Two-lbests based multi-objective particle swarm optimizer”, Engineering Optimization,
Vol. 43, No. 1, pp. 1–17, January 2011.
198. Dongdong Yang, Licheng Jiao, Maoguo Gong and Jie Feng, “Adaptive Ranks Clone and k-Nearest Neighbor List-Based
Immune Multi-Objective Optimization”, Computational Intelligence, Vol. 26, No. 4, pp. 359–385, November 2010.
199. Jingxuan Wei and Yuping Wang, “An Infeasible Elitist Based Particle Swarm Optimization for Constrained Multiobjective Optimization and Its Convergence”, International Journal of Pattern Recognition and Artificial Intelligence, Vol.
24, No. 3, pp. 381–400, May 2010.
200. Hao Cui and Osman Turan, “Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation
methodology in ship design”, Computer-Aided Design, Vol. 42, No. 11, pp. 1013–1027, November 2010.
201. Hui Xiao, Qi Kang, Jie Zhao and Yun-shi Xiao, “A dynamic sky recognition method for use in energy efficient lighting
design based on CIE standard general skies”, Building and Environment, Vol. 45, No. 5, pp. 1319–1328, May 2010.
202. Hai-bin Duan, Guan-jun Ma and De-lin Luo, “Optimal Formation Reconfiguration Control of Multiple UCAVs Using
Improved Particle Swarm Optimization”, Journal of Bionic Engineering, Vol. 5, No. 4, pp. 340–347, December 2008.
203. Qi Kang, Lei Wang and Qi-di Wu, “A novel ecological particle swarm optimization algorithm and its population dynamics
analysis”, Applied Mathematics and Computation, Vol. 205, No. 1, pp. 61–72, November 1, 2008.
204. Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab and Ali Khaki Sedigh, “Identification using ANFIS with intelligent
hybrid stable learning algorithm approaches”, Neural Computing & Applications, Vol. 18, No. 2, pp. 157–174, February
2009.
137
205. Vijay Kumar Garlapati, Pandu Ranga Vundavilli and Rintu Banerjee, “Evaluation of Lipase Production by Genetic
Algorithm and Particle Swarm Optimization and Their Comparative Study”, Applied Biochemistry and Biotechnology,
Vol. 162, No. 5, pp. 1350–1361, November 2010.
206. E. Rashidi, M. Jahandar and M. Zandieh, “An improved hybrid multi-objective parallel genetic algorithm for hybrid
flow shop scheduling with unrelated parallel machines”, International Journal of Advanced Manufacturing Technology,
Vol. 49, Nos. 9-12, pp. 1129–1139, August 2010.
207. Jaroslav Hajek, Andras Szollos and Jakub Sistek, “A new mechanism for maintaining diversity of Pareto archive in
multi-objective optimization”, Advances in Engineering Software, Vol. 41, Nos. 7-8, pp. 1031–1057, July-August 2010.
208. Huidong Jin and Man-Leung Wong, “Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms”, Expert Systems with Applications, Vol. 37, No. 12, pp. 8462–8470, December 2010.
209. Andre Alberton, Marcio Schwaab, Evaristo Chalbaud Biscaia, Jr. and Jose Carlos Pinto, “Sequential experimental
design based on multiobjective optimization procedures”, Chemical Engineering Science, Vol. 65, No. 20, pp. 5482–
5494, October 15, 2010.
210. Yixiong Feng, Bing Zheng and Zhongkai Li, “Exploratory study of sorting particle swarm optimizer for multiobjective
design optimization”, Mathematical and Computer Modelling, Vol. 52, Nos. 11-12, pp. 1966–1975, December 2010.
211. Ricardo Perera, Sheng-En Fang and Antonio Ruiz, “Application of particle swarm optimization and genetic algorithms
to multiobjective damage identification inverse problems with modelling errors”, Meccanica, Vol. 45, No. 5, pp. 723–734,
October 10, 2010.
212. Somayyeh Chamaani, Mohammad Sadegh Abrishamian and Seyed Abdullah Mirtaheri, “Time-Domain Design of UWB
Vivaldi Antenna Array Using Multiobjective Particle Swarm Optimization”, IEEE Antennas and Wireless Propagation
Letters, Vol. 9, pp. 666–669, 2010.
213. Jiaquan Gao, Lei Fang and Jun Wang, “A weight-based multiobjective immune algorithm: WBMOIA”, Engineering
Optimization, Vol. 42, No. 8, pp. 719–745, 2010.
214. Antonio C. Briza and Prospero C. Naval, Jr., “Stock trading system based on the multi-objective particle swarm
optimization of technical indicators on end-of-day market data”, Applied Soft Computing, Vol. 11, No. 1, pp. 1191–
1201, January 2011.
215. Ankit Kumar Gandhi, Sri Krishna Kumar, Mayank Kumar Pandey and M.K. Tiwari, “EMPSO-based optimization for
inter-temporal multi-product revenue management under salvage consideration”, Applied Soft Computing, Vol. 11, No.
1, pp. 468–476, January 2011.
216. Sultan Noman Qasem and Siti Mariyam Shamsuddin, “Radial basis function network based on time variant multiobjective particle swarm optimization for medical diseases diagnosis”, Applied Soft Computing, Vol. 11, No. 1, pp.
1427–1438, January 2011.
217. Weiling Cai, Songcan Chen and Daoqiang Zhang, “A Multiobjective Simultaneous Learning Framework for Clustering
and Classification”, IEEE Transactions on Neural Networks, Vol. 21, No. 2, pp. 185–200, February 2010.
218. Ronghua Jiang, Houjun Wang, Shulin Tian and Bing Long, “Multidimensional Fitness Function DPSO Algorithm for
Analog Test Point Selection”, IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 6, pp. 1634–1641,
June 2010.
219. M.A. Abido, “Multiobjective particle swarm optimization with nondominated local and global sets”, Natural Computing,
Vol. 9, No. 3, pp. 747–766, September 2010.
220. Z.H. Che, “PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection
molding”, Computers & Industrial Engineering, Vol. 58, No. 4, pp. 625–637, May 2010.
221. Jiaquan Gao, Lei Fang and Jun Wang, “A weight-based multiobjective immune algorithm: WBMOIA”, Engineering
Optimization, Vol. 42, No. 8, pp. 719–745, 2010.
222. L.H. Wu, Y.N. Wang, X.F. Yuan and S.W. Zhou, “Environmental/economic power dispatch problem using multiobjective differential evolution algorithm”, Electric Power Systems Research, Vol. 80, No. 9, pp. 1171–1181, September
2010.
223. Shang-Jeng Tsai, Tsung-Ying Sun, Chan-Cheng Liu, Sheng-Ta Hsieh, Wun-Ci Wu and Shih-Yuan Chiu, “An improved
multi-objective particle swarm optimizer for multi-objective problems”, Expert Systems with Applications, Vol. 37, No.
8, pp. 5872–5886, August 2010.
224. Dun-wei Gong, Yong Zhang and Cheng-liang Qi, “Environmental/economic power dispatch using a hybrid multi-objective
optimization algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 32, No. 6, pp. 607–614,
July 2010.
225. Chang Wook Ahn and R.S. Ramakrishna, “A diversity preserving selection in multiobjective evolutionary algorithms”,
Applied Intelligence, Vol. 32, No. 3, pp. 231–248, June 2010.
138
226. Xuesong Zhang, Raghavan Srinivasan and Michael Van Liew, “On the use of multi-algorithm, genetically adaptive multiobjective method for multi-site calibration of the SWAT model”, Hydrological Processes, Vol. 24, No. 8, pp. 955–969,
April 15, 2010.
227. Yee Ming Chen and Wen-Shiang Wang, “Environmentally constrained economic dispatch using Pareto archive particle
swarm optimisation”, International Journal of System Science, Vol. 41, No. 5, pp. 593–605, 2010.
228. Shi-Zheng Zhao and Ponnuthurai Nagaratnam Suganthan, “Multi-Objective Evolutionary Algorithm with Ensemble
of External Archives”, International Journal of Innovative Computing Information and Control, Vol. 6, No. 4, pp.
1713–1726, April 2010.
229. C.N. Nyirenda and D.S. Dawoud, “Self-Organization in a Particle Swarm Optimized Fuzzy Logic Congestion Detection
Mechanism for IP Networks”, Scientia Iranica, Vol. 15, No. 6, pp. 589–604, November-December 2008.
230. S.C. Chiam, K.C. Tan, C.K. Goh and A. Al Mamun, “Improving locality in binary representation via redundancy”,
IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 38, No. 3, pp. 808–825, June 2008.
231. M. Cioffi, P. Di Barba, A. Formisano and R. Martone, “Pareto optima and Nash equilibria - An effective approach to the
shape design in electromagnetics”, COMPEL–The International Journal for Computation and Mathematics in Electrical
and Electronic Engineering, Vol. 27, No. 4, pp. 845–854, 2008.
232. Naoki Nishida, Yasuhito Takahashi and Shinji Wakao, “Robust design optimization approach by combination of sensitivity analysis and sigma level estimation”, IEEE Transactions on Magnetics, Vol. 44, No. 6, pp. 998–1001, June
2008.
233. Wen-Fung Leong and Gary G. Yen, “PSO-Based Multiobjective Optimization with Dynamic Population Size and Adaptive Local Archives”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 38, No. 5, pp.
1270–1293, October 2008.
234. Tomoyuki Miyamoto, So Noguchi and Hideo Yamashita, “Selection of an optimal solution for multiobjective electromagnetic apparatus design based on Game Theory”, IEEE Transactions on Magnetics, Vol. 44, No. 6, pp. 1026–1029, June
2008.
235. Heike Trautmann and Jörn Mehnen, “Preference-based Pareto optimization in certain and noisy environments”, Engineering Optimization, Vol. 41, No. 1, pp. 23–38, January 2009.
236. Hongwu Liu and Ji Li, “A particle swarm optimization-based multiuser detection for receive-diversity-aided STBC
systems”, IEEE Signal Processing Letters, Vol. 15, pp. 29–32, 2008.
237. Ali R. Yildiz, Nursel Ozturk, Necmettin Kaya and Ferruh Ozturk, “Hybrid multi-objective shape design optimization
using Taguchi’s method and genetic algorithm”, Structural and Multidisciplinary Optimization, Vol. 34, No. 4, pp.
317–332, October 2007.
238. Ching-Shih Tsou, “Multi-objective inventory planning using MOPSO and TOPSIS”, Expert Systems with Applications,
Vol. 35, Nos. 1–2, pp. 136–142, July-August 2008.
239. Shubham Agrawal, B.K. Panigrahi and Manoj Kumar Tiwari, “Multiobjective Particle Swarm Algorithm with Fuzzy
Clustering for Electrical Power Dispatch”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 5, pp.
529–541, October 2008.
240. Kazuhiro Izui, Shinji Nishiwaki, Masataka Yoshimura, Masahiko Nakamura and John E. Renaud, “Enhanced multiobjective particle swarm optimization in combination with adaptive weighted gradient-based searching”, Engineering
Optimization, Vol. 40, No. 9, pp. 789–804, September 2008.
241. Elizabeth F. Wanner, Frederico G. Guimarães, Ricardo H.C. Takahashi and Peter J. Fleming, “Local Search with
Quadratic Approximations into Memetic Algorithms for Optimization with Multiple Criteria”, Evolutionary Computation, Vol. 16, No. 2, pp. 185–224, Summer 2008.
242. Antonio J. Nebro, Francisco Luna, Enrique Alba, Bernabé Dorronsoro, Juan J. Durillo and Andreas Beham, “AbYSS:
Adapting Scatter Search to Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12,
No. 4, pp. 439–457, August 2008.
243. Min-Rong Chen and Yong-Zal Lu, “A novel elitist multiobjective optimization algorithm: Multiobjective extremal
optimization”, European Journal of Operational Research, Vol. 188, No. 3, pp. 637–651, August 1, 2008.
244. Yifeng Niu, Lincheng Shen and Yanlong Bu, “Multi-objective blind image fusion”, in Rough Sets and Knowledge Technology, Springer. Lecture Notes in Artificial Intelligence Vol. 4062, pp. 713–720, 2006.
245. Hamidreza Eskandari and Christopher D. Geiger, “A fast Pareto genetic algorithm approach for solving expensive
multiobjective optimization problems”, Journal of Heuristics, Vol. 14, No. 3, pp. 203–241, June 2008.
246. Yamille del Valle, Ganesh Kumar Venayagamoorthy, Salman Mohagheghi, Jean-Carlos Hernandez and Ronald G. Harley,
“Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems”, IEEE Transactions on
Evolutionary Computation, Vol. 12, No. 2, pp. 171–195, April 2008.
139
247. Shubham Agrawal, Yogesh Dashora, Manoj Kumar Tiwari and Young-Jun Son, “Interactive Particle Swarm: A ParetoAdaptive Metaheuristic to Multiobjective Optimization”, IEEE Transactions on Systems, Man, and Cybernetics Part
A–Systems and Humans, Vol. 38, No. 2, pp. 258–277, March 2008.
248. Yifeng Niu and Lincheng Shen, “An Adaptive Multi-objective Particle Swarm Optimization for Color Image Fusion”, in
Tzai-Der Wang, Xiaodong Li, Shu-Heng Chen, Xufa Wang, Hussein Abbass, Hitoshi Iba, Guoliang Chen and Xin Yao
(editors), Simulated Evolution and Learning, 6th International Conference, SEAL 2006, pp. 473–480, Springer. Lecture
Notes in Computer Science Vol. 4247, Hefei, China, October 2006.
249. Stavros Koulouridis, Dimitris Psychoudakis and John L. Volakis, “Multiobjective Optimal Antenna Design Based on
Volumetric Material Optimization”, IEEE Transactions on Antennas and Propagation, Vol. 55, No. 3, pp. 594–603,
March 2007.
250. Qingfu Zhang, Aimin Zhou and Yaochu Jin, “RM-MEDA: A Regularity Model-Based Multiobjective Estimation of
Distribution Algorithm”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 41–63, February 2008.
251. K. Izui, S. Nishiwaki and M. Yoshimura, “Swarm algorithms for single- and multi-objective optimization problems
incorporating sensitivity analysis”, Engineering Optimization, Vol. 39, No. 8, pp. 981–998, December 2007.
252. A.R. Rahimi-Vahed, S.M. Mirghorbani and M. Rabbani, “A hybrid multi-objective particle swarm algorithm for a
mixed-model assembly line sequencing problem”, Engineering Optimization, Vol. 39, No. 8, pp. 877–898, December
2007.
253. Qingfu Zhang and Hui Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition”, IEEE
Transactions on Evolutionary Computation, Vol. 11, No. 6, pp. 712–731, December 2007.
254. M. Janga Reddy and D. Nagesh Kumar, “Multi-objective particle swarm optimization for generating optimal trade-offs
in reservoir operation”, Hydrological Processes, Vol. 21, No. 21, pp. 2897–2909, October 15, 2007.
255. Praveen Kumar Tripathi, Sanghamitra Bandyopadhyay, and Sankar Kumar Pal, “Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients”, Information Sciences, Vol. 177, No. 22, pp. 5033–5049,
November 15, 2007.
256. Lingfeng Wang and Chanan Singh, “Environmental/economic power dispatch using a fuzzified multi-objective particle
swarm optimization algorithm”, Electric Power Systems Research, Vol. 77, No. 12, pp. 1654–1664, October 2007.
257. Zne-Jung Lee, Shih-Wei Lin, Shun-Feng Su and Chun-Yen Lin, “A hybrid watermarking technique applied to digital
images”, Applied Soft Computing, Vol. 8, No. 1, pp. 798–808, January 2008.
258. V. Cavaliere, A. Formisano, R. Martone, G. Masullo, A. Matrone and R. Quarantiello, “Design and test of a compound
persistent-pulsed magnet for fast field cycling NMR”, IEEE Transactions on Applied Superconductivity, Vol. 17, No. 2,
pp. 1426–1429, Part 2, June 2007.
259. Vincenzo Cavaliere, Marco Cioffi, Alessandro Formisano and Raffaele Martone, “Pareto swarm optimisation of high
temperature superconducting generators”, International Journal of Applied Electromagnetics and Mechanics, Vol. 25,
Nos. 1–4, pp. 273–279, 2007.
260. I-Tung Yang, “Using elitist particle swarm optimization to facilitate bicriterion time-cost trade-off analysis”, Journal of
Construction Engineering and Management-ASCE, Vol. 133, No. 7, pp. 498–505, July 2007.
261. Yakoub Bazi and Farid Melgani, “Semisupervised PSO-SVM regression for biophysical parameter estimation”, IEEE
Transactions on Geoscience and Remote Sensing, Vol. 45, No. 6, pp. 1887–1895, Part 2, June 2007.
262. Peng-Yeng Yin, Shiuh-Sheng Yu, Pei-Pei Wang and Yi-Te Wang, “Task allocation for maximizing reliability of a distributed system using hybrid particle swarm optimization”, Journal of Systems and Software, Vol. 80, No. 5, pp.
724–735, May 2007.
263. Pei-Chann Chang, Shih-Hsin Chen and Chen-Hao Liu, “Sub-population genetic algorithm with mining gene structures
for multiobjective flowshop scheduling problems”, Expert Systems with Applications, Vol. 33, No. 3, pp. 762–771,
October 2007.
264. Fuqing Zhao, Yi Hong, Dongmei Yu, Yahong Yang, Qiuyu Zhang and Huawei Yi, “A hybrid algorithm based on particle
swarm optimization and simulated annealing to holon task allocation for holonic manufacturing system”, International
Journal of Advanced Manufacturing Technology, Vol. 32, Nos. 9–10, pp. 1021–1032, April 2007.
265. Frederico G. Guimaraes, Reinaldo M. Palhares, Felipe Campelo and Hajime Igarashi, “Design of mixed H-2/H infinity
control systems using algorithms inspired by the immune system”, Information Sciences, Vol. 177, No. 20, pp. 4368–
4386, October 15, 2007.
266. A.R. Rahimi-Vahed, S.M. Mirghorbani and M. Rabbani, “A new particle swarm algorithm for a multi-objective mixedmodel assembly line sequencing problem”, Soft Computing, Vol. 11, No. 10, pp. 997–1012, August 2007.
267. Sotirios K. Goudos, “A versatile software tool for microwave planar radar absorbing materials design using global
optimization algorithms”, Materials and Design, Vol. 28, pp. 2585–2595, 2007.
140
268. C.S. Chang and C.M. Kwan, “Evaluation of evolutionary algorithms for multi-objective train schedule optimization”,
AI 2004: Advances in Artificial Intelligence, Springer-Verlag, Lecture Notes in Artificial Intelligence, Vol. 3339, pp.
803–815, 2004.
269. H.Y. Meng, X.H. Zhang and S.Y. Liu, “A co-evolutionary particle swarm optimization-based method for multiobjective
optimization”, AI 2005: Advances in Artificial Intelligence, pp. 349–359, Springer-Verlag, Lecture Notes in Artificial
Intelligence Vol. 3809, 2005.
270. Lyndon While, Phil Hingston, Luigi Barone, and Simon Huband, “A Faster Algorithm for Calculating Hypervolume”,
IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, pp. 29–38, February 2006.
271. Joshua Knowles, “ParEGO: A Hybrid Algorithm With On-Line Landscape Approximation for Expensive Multiobjective
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, pp. 50–66, February 2006.
272. V.L. Huang, P.N. Suganthan and J.J. Liang, “Comprehensive learning particle swarm optimizer for solving multiobjective
optimization problems”, International Journal of Intelligent Systems, Vol. 21, No. 2, pp. 209–226, February 2006.
273. Xiaohua Zhang, Hongyun Meng and Licheng Jiao, “Improving PSO-Based Multiobjective Optimization Using Competition and Immunity Clonal”, in Yue Hao et al. (editors), Computational Intelligence and Security. International
Conference, CIS 2005, pp. 839–845, Springer, Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December
2005.
274. H.Y. Meng, X.H. Zhang and S.Y. Liu, “Intelligent multiobjective particle swarm optimization based on AER model”, in
Progress in Artificial Intelligence, Proceedings, pp. 178–189, Springer, Lecture Notes in Artificial Intelligence Vol. 3808,
2005.
275. Y.F. Chen and V.K. Dubey, “Ultra-wideband source localization using a particle-swarm-optimized Capon estimator
from a frequency-dependent channel modeling viewpoint”, Eurasip Journal on Applied Signal Processing 2005, Vol. 12,
pp. 1854–1866, July 21, 2005.
276. N.B. Jin and Y. Rahmat-Samii, “Parallel particle swarm optimization and finite-difference time-domain (PSO/FDTD)
algorithm for multiband and wide-band patch antenna designs”, IEEE Transactions on Antennas and Propagation, Vol.
53, No. 11, pp. 3459–3468, November 2005.
277. F.Q. Zhao, Q.Y. Zhang, D.M. Yu, X.H. Chen and Y.H. Yang, “A hybrid algorithm based on PSO and simulated annealing
and its applications for partner selection in virtual enterprise”, Advances in Intelligent Computing, Pt 1, Proceedings,
Springer, pp. 380–389, Lecture Notes in Computer Science Vol. 3644, 2005.
278. Y.J. Li, D.Z. Yao, J. Yao and W.F. Chen, “A particle swarm optimization algorithm for beam angle selection in intensitymodulated radiotherapy planning”, Physics in Medicine and Biology, Vol. 15, No. 15, pp. 3491–3514, August 7, 2005.
279. Fabio Freschi and Maurizio Repetto, “Multiobjective Optimization by a Modified Artificial Immune System Algorithm”,
in Christian Jacob, Marcin L. Pilat, Peter J. Bentley and Jonathan Timmis (editors), Artificial Immune Systems. 4th
International Conference, ICARIS 2005, pp. 248–261, Springer. Lecture Notes in Computer Science Vol. 3627, Banff,
Canada, August 2005.
280. Jason Teo and Hussein A. Abbass, “Multiobjectivity and Complexity in Embodied Cognition”, IEEE Transactions on
Evolutionary Computation, Vol. 9, No. 4, pp. 337–360, August 2005.
281. Julio E. Alvarez-Benitez, Richard M. Everson and Jonathan E. Fieldsend, “A MOPSO Algorithm Based Exclusively
on Pareto Dominance Concepts”, in Carlos A. Coello Coello, Arturo Hernández Aguirre and Eckart Zitzler (editors),
Evolutionary Multi-Criterion Optimization. Third International Conference, EMO 2005, pp. 459–473, Springer. Lecture
Notes in Computer Science Vol. 3410, Guanajuato, México, March 2005.
282. Ganesh K. Venayagamoorthy, Scott C. Smith and Gaurav Singhal, “Particle swarm-based optimal partitioning algorithm
for combinational CMOS circuits”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 2, pp. 177–184, March
2007.
283. Yumin Liu and Zhongyuan Yu, “Optimal designing of multi-channel WDM filter using intelligent particle swarm optimization algorithm”, Simulated Evolution and Learning, Proceedings, pp. 205–212, Springer, Lecture Notes in Computer
Science Vol. 4247, 2006.
284. Min Zhang, Huantong Geng, Wenjian Luo, Linfeng Huang and Xufa Wang, “A hybrid of differential evolution and genetic
algorithm for constrained multiobjective optimization problems”, Simulated Evolution and Learning, Proceedings, pp.
318–327, Springer, Lecture Notes in Computer Science Vol. 4247, 2006.
285. Hung-Ming Chen, Bo-Fu Liu, Hui-Ling Huang, Shiow-Fen Hwang and Shinn-Ying Ho, “SODOCK: Swarm optimization
for highly flexible protein-ligand docking”, Journal of Computational Chemistry, Vol. 28, No. 2, pp. 612–623, January
30, 2007.
286. Zhuhong Zhang, “Constrained multiobjective optimization immune algorithm: Convergence and application”, Computers
& Mathematics with Applications, Vol. 52, No. 5, pp. 791–808, September 2006.
287. Haluk Yapicioglu, Alice E. Smith and Gerry Dozier, “Solving the semi-desirable facility location problem using biobjective particle swarm”, European Journal of Operational Research, Vol. 177, No. 2, pp. 733–749, March 1, 2007.
141
288. Yumin Liu, Zhongyuan Yu, “Intelligent particle swarm optimization algorithm and its application in optimal designing
of LPG devices for optical communications fields”, Advances in Natural Computation, Part 2, Springer, Lecture Notes
in Computer Science Vol. 4222, pp. 166–175, 2006.
289. Pei-Chann Chang, Shih-Hsin Chen and Jih-Chang Hsieh, “A global archive sub-population genetic algorithm with
adaptive strategy in multi-objective parallel-machine scheduling problem”, Advances in Natural, Part 1, Springer, Lecture
Notes in Computer Science Vol. 4221, pp. 730–739, 2006.
290. A.R. Yildiz and F. Ozturk, “Hybrid enhanced genetic algorithm to select optimal machining parameters in turning
operation”, Proceedings of the Institution of Mechanical Engineers Part B–Journal of Engineering Manufacture, Vol.
220, No. 12, pp. 2041–2053, December 2006.
291. P. Kumar, D. Gospodaric and P. Bauer, “Improved genetic algorithm inspired by biological evolution”, Soft Computing,
Vol. 11, No. 10, pp. 923–941, August 2007.
292. A.R. Rahimi-Vahed and S.M. Mirghorbani, “A multi-objective particle swarm for a flow shop scheduling problem”,
Journal of Combinatorial Optimization, Vol. 13, No. 1, pp. 79–102, January 2007.
293. M. Janga Reddy and D. Nagesh Kumar, “An efficient multi-objective optimization algorithm based on swarm intelligence
for engineering design”, Engineering Optimization, Vol. 39, No. 1, pp. 49–68, January 2007.
294. Fabio Freschi and Maurizio Repetto, “VIS: an artificial immune network for multi-objective optimization”, Engineering
Optimization, Vol. 38, No. 8, pp. 975–996, December 2006.
295. Y.F. Niu and L.C. Shen, “Multi-resolution image fusion using AMOPSO-II”, Intelligent Computing in Signal Processing
and Pattern Recognition, Springer-Verlag, pp. 343–352, Lecture Notes in Control and Information Sciences Vol. 345,
2006.
296. N. Ozturk, A.R. Yildiz, N. Kaya and F. Ozturk, “Neuro-genetic design optimization framework to support the integrated
robust design optimization process in CE”, Concurrent Engineering–Research and Applications, Vol. 14, No. 1, pp. 5–16,
March 2006.
297. H. Yamachi, Y. Tsujimura, Y. Kambayashi and H. Yamamoto, “Multi-objective genetic algorithm for solving N-version
program design problem”, Reliability Engineering & System Safety, Vol. 91, No. 9, pp. 1083–1094, September 2006.
298. M.K. Gill, Y.H. Kaheil, A. Khalil, M. Mckee and L. Bastidas, “Multiobjective particle swarm optimization for parameter
estimation in hydrology”, Water Resources Research, Vol. 42, No. 7, Art. No. W07417, July 22, 2006.
299. Z.H. Cui, J.C. Zeng and G.J. Sun, “Adaptive velocity threshold particle swarm optimization”, Rough Sets and Knowledge
Technology, pp. 327–332, Springer, Lecture Notes in Artificial Vol. 4062, 2006.
300. Daniel W. Boeringer and Douglas H. Werner, “Bézier representations for the multiobjective, optimization of conformal
array amplitude weights”, IEEE Transactions on Antennas and Propagation, Vol. 54, No. 7, pp. 1964–1970, July 2006.
301. S.K. Goudos and J.N. Sahalos, “Microwave absorber optimal design using multi-objective particle swarm optimization”,
Microwave and Optical Technology Letters, Vol. 48, No. 8, pp. 1553–1558, August 2006.
302. Visakan Kadirkamanathan, Kirusnapillai Selvarajah and Peter J. Fleming, “Stability Analysis of the Particle Dynamics
in Particle Swarm Optimizer”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 245–255, June
2006.
303. J.J. Liang, A.K. Qin, Ponnuthurai Nagaratnam Suganthan and S. Baskar, “Comprehensive Learning Particle Swarm
Optimizer for Global Optimizations of Multimodal Functions”, IEEE Transactions on Evolutionary Computation, Vol.
10, No. 3, pp. 230–244, June 2006.
304. M.A. Abido, “Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem”, IEEE Transactions on
Evolutionary Computation, Vol. 10, No. 3, pp. 315–329, June 2006.
305. S.J. Ho, W.Y. Ku, J.W. Jou, M.H. Hung and S.Y. Ho, “Intelligent particle swarm optimization in multi-objective
problems”, in Advances in Knowledge Discovery and Data Mining, Springer, pp. 790–800, Lecture Notes in Artificial
Intelligence Vol. 3918, 2006.
306. Kuei-Hsien Chen and Chwen-Tzeng Su, “Activity assigning of fourth party logistics by particle swarm optimizationbased preemptive fuzzy integer goal programming”, Expert Systems with Applications, Vol. 37, No. 5, pp. 3630–3637,
May 2010.
307. Liang Zhao, Feng Qian, Yupu Yang, Yong Zeng and Haijun Su, “Automatically extracting T-S fuzzy models using
cooperative random learning particle swarm optimization”, Applied Soft Computing, Vol. 10, No. 3, pp. 938–944, June
2010.
308. G.B.M. Heuvelink, Z. Jiang, S. De Bruin and C.J.W. Twenhofel, “Optimization of mobile radioactivity monitoring
networks”, International Journal of Geographical Information Science, Vol. 24, No. 3, pp. 365–382, 2010.
309. Ahmed Elhossini, Shawki Areibi and Robert Dony, “Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO
for Multi-Objective Optimization”, Evolutionary Computation, Vol. 18, No. 1, pp. 127–156, Spring 2010.
142
310. Sotirios K. Goudos and John N. Sahalos, “Pareto Optimal Microwave Filter Design Using Multiobjective Differential
Evolution”, IEEE Transactions on Antennas and Propagation, Vol. 58, No. 1, pp. 132–144, January, 2010.
311. Omid Khayat, Mohammad Mehdi Ebadzadeh, Hamid Reza Shahdoosti, Ramin Rajaei and Iman Khajehnasiri, “A novel
hybrid algorithm for creating self-organizing fuzzy neural networks”, Neurocomputing, Vol. 73, Nos. 1–3, pp. 517–524,
December 2009.
312. D.Y. Sha and Hsing-Hung Lin, “A multi-objective PSO for job-shop scheduling problems”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1065–1070, March 2010.
313. Yinghai Li, Jianzhong Zhou, Yongchuan Zhang, Hui Qin and Li Liu, “Novel Multiobjective Shuffled Frog Leaping Algorithm with Application to Reservoir Flood Control Operation”, Journal of Water Resources Planning and Management–
ASCE, Vol. 136, No. 2, pp. 217–226, March-April 2010.
314. Andrea Paoli, Farid Melgani and Edoardo Pasolli, “Clustering of Hyperspectral Images Based on Multiobjective Particle
Swarm Optimization”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 12, pp. 4175–4188, Part 2,
December 2009.
315. A. Rama Mohan Rao and P.P. Shyju, “A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of Hybrid
Laminate Composite Structures”, Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 3, pp. 149–170,
April 2010.
316. Yu-Bo Tian, “Solving Complex Transcendental Equations Based on Swarm Intelligence”, IEEJ Transactions on Electrical
and Electronic Engineering, Vol. 4, No. 6, pp. 755–762, November 2009.
317. Lingjuan Wang, Chengjian Wei and Shuai Huang, “Computing Nash equilibria with particle swarm optimization algorithm”, Dynamics of Continuous Discrete and Impulsive Systems–Series B–Applications & Algorithms, Vol. 13, pp.
26–30, December 2006.
318. Jing Jie, Jianchao Zeng, Chongzhao Han and Qinghua Wang, “Knowledge-based cooperative particle swarm optimization”, Applied Mathematics and Computation, Vol. 205, No. 2, pp. 861–873, November 15, 2008.
319. Zhihua Cui, Xingjuan Cai, Jianchao Zeng and Guoji Sun, “Particle swarm optimization with FUSS and RWS for high
dimensional functions”, Applied Mathematics and Computation, Vol. 205, No. 1, pp. 98–108, November 1, 2008.
320. Ngai M. Kwok, Q.P. Ha, Dikai Liu and Gu Fang, “Contrast Enhancement and Intensity Preservation for Gray-Level
Images Using Multiobjective Particle Swarm Optimization”, IEEE Transactions on Automation Science and Engineering,
Vol. 6, No. 1, pp. 145–155, January 2009.
321. Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki Sedigh and M. Ahmadieh Khanesar, “Identification using
ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods”, Applied
Soft Computing, Vol. 9, No. 2, pp. 833–850, March 2009.
322. Keisuke Kameyama, “Particle Swarm Optimization - A Survey”, IEICE Transactions on Information and Systems, Vol.
E92D, No. 7, pp. 1354–1361, July 2009.
323. Fei Tao, Dongming Zhao, Yefa Hu and Zude Zhou, “Correlation-aware resource service composition and optimal-selection
in manufacturing grid”, European Journal of Operational Research, Vol. 201, No. 1, pp. 129–143, February 16, 2010.
324. Fuqing Zhao, Yi Hong, Dongmei Yu, Yahong Yang and Qiuyu Zhang, “A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems”,
International Journal of Computer Integrated Manufacturing, Vol. 23, No. 1, pp. 20–39, 2010.
325. Yahong Yang, Guiling Wu, Jianping Chen and Wei Dai, “Multi-objective optimization based on ant colony optimization
in grid over optical burst switching networks”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1769–1775, March
2010.
326. Chin-Hsiung Hsu, Ching-Shih Tsou and Fong-Jung Yu, “Multicriteria Tradeoffs in Inventory Control using Memetic
Particle Swarm Optimization’, International Journal of Innovative Computing Information and Control, Vol. 5, No.
11A, pp. 3755–3768, November 2009.
327. Sayantani Bhattacharya, Amit Konar, Swagatam Das and Sang Yong Han, “A Lyapunov-Based Extension to Particle
Swarm Dynamics for Continuous Function Optimization”, Sensors, Vol. 9, No. 12, pp. 9977–9997, December 2009.
328. Masaru Kawarabayashi, Junichi Tsuchiya and Keiichiro Yasuda, “Integrated Optimization by Multi-Objective Particle
Swarm Optimization”, IEEJ Transactions on Electrical and Electronic Engineering, Vol. 5, No. 1, pp. 79–81, January
2010.
329. Tsu-Feng Ho, Peng-Yeng Yin, Gwo-Jen Hwang, Shyong Jian Shyu and Ya-Nan Yean, “Multi-Objective Parallel TestSheet Composition Using Enhanced Particle Swarm Optimization”, Educational Technology & Society, Vol. 12, No. 4,
pp. 193–206, October 2009.
330. C.K. Goh, K.C. Tan, D.S. Liu and S.C. Chiam, “A competitive and cooperative co-evolutionary approach to multiobjective particle swarm optimization algorithm design”, European Journal of Operational Research, Vol. 202, No. 1,
pp. 42–54, April 1, 2010.
143
331. Deming Lei, “Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems”,
International Journal of Advanced Manufacturing Technology, Vol. 37, Nos. 1-2, pp. 157–165, April 2008.
332. Deming Lei, “A Pareto archive particle swarm optimization for multi-objective job shop scheduling”, Computers &
Industrial Engineering, Vol. 54, No. 4, pp. 960–971, May 2008.
333. Jingxuan Wei and Yuping Wang, “Multi-objective fuzzy particle swarm optimization based on elite archiving and its
convergence”, Journal of Systems Engineering and Electronics, Vol. 19, No. 5, pp. 1035–1040, October 2008.
334. Yujia Wang and Yupu Yang, “Particle swarm optimization with preference order ranking for multi-objective optimization”, Information Sciences, Vol. 179, No. 12, pp. 1944–1959, May 30, 2009.
335. Yujia Wang and Yupu Yang, “Particle swarm with equilibrium strategy of selection for multi-objective optimization”,
European Journal of Operational Research, Vol. 200, No. 1, pp. 187–197, January 1, 2010.
336. Aimin Zhou, Qingfu Zhang and Yaochu Jin, “Approximating the Set of Pareto-Optimal Solutions in Both the Decision
and Objective Spaces by an Estimation of Distribution Algorithm”, IEEE Transactions on Evolutionary Computation,
Vol. 13, No. 5, pp. 1167–1189, October 2009.
337. Yao-Nan Wang, Liang-Hong Wu and Xiao-Fang Yuan, “Multi-objective self-adaptive differential evolution with elitist
archive and crowding entropy-based diversity measure”, Soft Computing, Vol. 14, No. 3, pp. 193–209, February 2010.
338. A. Rama Mohan Rao and K. Lakshmi, “Multi-objective Optimal Design of Hybrid Laminate Composite Structures Using
Scatter Search”, Journal of Composite Materials, Vol. 43, No. 20, pp. 2157–2182, September 2009.
339. Gary G. Yen and Weng Fung Leong, “Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization”, IEEE
Transactions on Systems Man and Cybernetics Part A–Systems and Humans, Vol. 39, No. 4, pp. 890–911, July 2009.
340. Pei-Chann Chang and Shih-Hsin Chen, “The development of a sub-population genetic algorithm II (SPGA II) for
multi-objective combinatorial problems”, Applied Soft Computing, Vol. 9, No. 1, pp. 173–181, January 2009.
341. Pei-Chann Chang, Shih-Hsin Chen, Chin-Yuan Fan and Chien-Lung Chan, “Genetic algorithm integrated with artificial
chromosomes for multi-objective flowshop scheduling problems”, Applied Mathematics and Computation, Vol. 205, No.
2, pp. 550–561, November 15, 2008.
342. Hai-Lin Liu, Yuping Wang and Yiu-Ming Cheung, “A Multi-Objective Evolutionary Algorithm using Min-Max Strategy
and Sphere Coordinate Transformation”, Intelligent Automation and Soft Computing, Vol. 15, No. 3, pp. 361–384,
2009.
343. F. Logist, P.M.M. Van Erdeghem and J.F. Van Impe, “Efficient deterministic multiple objective optimal control of
(bio)chemical processes”, Chemical Engineering Science, Vol. 64, No. 11, pp. 2527–2538, June 1, 2009.
344. Vijay Kalivarapu, Jung-Leng Foo and Eliot Winer, “Synchronous parallelization of Particle Swarm Optimization with
digital pheromones”, Advances in Engineering Software, Vol. 40, No. 10, pp. 975–985, October 2009.
345. Shih-Wei Lin, Yeou-Ren Shiue, Shih-Chi Chen and Hui-Miao Cheng, “Applying enhanced data mining approaches in
predicting bank performance: A case of Taiwanese commercial banks”, Expert Systems with Applications, Vol. 36, No.
9, pp. 11543–11551, November 2009.
346. Vijay K. Kalivarapu and Eliot H. Winer, “Asynchronous parallelization of particle swarm optimization through digital
pheromone sharing”, Structural and Multidisciplinary Optimization, Vol. 39, No. 3, pp. 263–281, September 2008.
347. Shu-Kai Fan and Ju-Ming Chang, “A parallel particle swarm optimization algorithm for multi-objective optimization
problems”, Engineering Optimization, Vol. 41, No. 7, pp. 673–697, July 2009.
348. Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab and Ali Khak Sedigh, “Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter”, Fuzzy Sets
and Systems, Vol. 160, No. 7, pp. 922–948, April 1, 2009.
349. Ying-Nan Zhang and Hong-Fei Teng, “Detecting particle swarm optimization”, Concurrency and Computation–Practice
& Experience, Vol. 21, No. 4, pp. 449–473, March 25, 2009.
350. S.G. Li and Y.L. Rong, “The research of online price quotation for the automobile parts exchange programme”, International Journal of Computer Integrated Manufacturing, Vol. 22, No. 3, pp. 245–256, 2009.
351. Alexandre M. Baltar and Darrell G. Fontane, “Use of multiobjective particle swarm optimization in water resources
management”, Journal of Water Resources Planning and Management–ASCE, Vol. 134, No. 3, pp. 257–265, May-June
2008.
352. Junjie Yang, Jianzhong Zhou, Li Liu and Yinghai Li, “A novel strategy of pareto-optimal solution searching in multiobjective particle swarm optimization (MOPSO)”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12,
pp. 1995–2000, June 2009.
353. Xiangwei Zheng and Hong Liu, “A hybrid vertical mutation and self-adaptation based MOPSO”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 2030–2038, June 2009.
354. Min-Rong Chen, Yong-Zai Lu and Genke Yang, “Multiobjective optimization using population-based extremal optimization”, Neural Computing and Applications, Vol. 17, No. 2, pp. 101–109, March 2008.
144
355. Dongdong Yang, Licheng Jiao and Maoguo Gong, “Adaptive Multi-Objective Optimization Based on Nondominated
Solutions”, Computational Intelligence, Vol. 25, No. 2, pp. 84–108, May 2009.
• Carlos A. Coello Coello, “Theoretical and Numerical Constraint-Handling Techniques used with Evolutionary
Algorithms: A Survey of the State of the Art”, Computer Methods in Applied Mechanics and Engineering,
Vol. 191, No. 11–12, pp. 1245–1287, January 2002.
1. Minggang Dong, Ning Wang, Xiaohui Cheng and Chuanxian Jiang, “Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems”, Mathematical Problems in Engineering, Article Number:
617905, 2014.
2. Haipeng Kong, Li Ni and Yuzhong Shen, “Adaptive double chain quantum genetic algorithm for constrained optimization
problems”, Chinese Journal of Aeronautics, Vol. 28, No. 1, pp. 214–228, February 2015.
3. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics inspired optimization
(OIO)”, Computer-Aided Design, Vol. 63, pp. 52–71, June 2015.
4. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Adaptive Ranking Mutation Operator Based Differential Evolution for
Constrained Optimization”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 716–727, April 2015.
5. Mostafa Z. Ali and Noor H. Awad, “A novel class of niche hybrid Cultural Algorithms for continuous engineering
optimization”, Information Sciences, Vol. 267, pp. 158–190, May 20, 2014.
6. Chengyong Si, Jing An, Tian Lan, Thomas Ussmuller, Lei Wang and Qidi Wu, “On the equality constraints tolerance
of Constrained Optimization Problems”, Theoretical Computer Science, Vol. 551, pp. 55–65, September 25, 2014.
7. Jian Wang, Xiaolong Wang, Aipeng Jiang, Jiangzhou Shu and Pin Li, “Operational Optimization of Large-Scale ParallelUnit SWRO Desalination Plant Using Differential Evolution Algorithm”, Scientific World Journal, Article Number:
584068, 2014.
8. Mohsen Davarynejad, Jan van den Berg and Jafar Rezaei, “Evaluating center-seeking and initialization bias: The case of
particle swarm and gravitational search algorithms”, Information Sciences, Vol. 278, pp. 802–821, September 10, 2014.
9. Khin Lwin, Rong Qu and Graham Kendall, “A learning-guided multi-objective evolutionary algorithm for constrained
portfolio optimization”, Applied Soft Computing, Vol. 24, pp. 757–772, November 2014.
10. Gilbert Reynoso-Meza, Xavier Blasco, Javier Sanchis and Miguel Martinez, “Controller tuning using evolutionary multiobjective optimisation: Current trends and applications”, Control Engineering Practice, Vol. 28, pp. 58–73, July 2014.
11. Rodrigo Ribeiro de Lucena, Juliana Souza Baioco, Beatriz Souza Leite Pires de Lima, Carl Horst Albrecht and Breno
Pinheiro Jacob, “Optimal design of submarine pipeline routes by genetic algorithm with different constraint handling
techniques”, Advances in Engineering Software, Vol. 76, pp. 110–124, October 2014.
12. Zhenzhou Hu, Xinye Cai and Zhun Fan, “An improved memetic algorithm using ring neighborhood topology for constrained optimization”, Soft Computing, Vol. 18, No. 10, pp. 2023–2041, October 2014.
13. Claudio Comis Da Ronco, Rita Ponza and Ernesto Benini, “Aerodynamic Shape Optimization in Aeronautics: A Fast
and Effective Multi-Objective Approach”, Archives of Computational Methods in Engineering, Vol. 21, No. 3, pp.
189–271, September 2014.
14. Hong Li and Li Zhang, “A discrete hybrid differential evolution algorithm for solving integer programming problems”,
Engineering Optimization, Vol. 46, No. 9, pp. 1238–1268, September 2, 2014.
15. Marsil de Athayde Costa e Silva, Carlos Eduardo Klein, Viviana Cocco Mariani and Leandro dos Santos Coelho,
“Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch
problem”, Energy, Vol. 53, pp. 14–21, May 1, 2013.
16. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.
53, No. 4, pp. 1168–1183, July 2014.
17. Hendra G. Harno and Ian R. Petersen, “Decentralized state feedback robust H-infinity control using a differential
evolution algorithm”, International Journal of Robust and Nonlinear Control, Vol. 24, No. 2, pp. 247–263, January 25,
2014.
18. J.M. Herrero, G. Reynoso-Meza, M. Martinez, X. Blasco and J. Sanchis, “A Smart-Distributed Pareto Front Using the
ev-MO GA Evolutionary Algorithm”, International Journal on Artificial Intelligence Tools, Vol. 23, No. 2, Article
Number: 1450002, April 2014.
19. Anupam Yadav and Kusu Deep, “Constrained Optimization Using Gravitational Search Algorithm”, National Academy
Science Letters–India, Vol. 36, No. 5, pp. 527–534, October 2013.
20. M. Khatibinia, E. Salajegheh, J. Salajegheh and M.J. Fadaee, “Reliability-based design optimization of reinforced
concrete structures including soil-structure interaction using a discrete gravitational search algorithm and a proposed
metamodel”, Engineering Optimization, Vol. 45, No. 10, pp. 1147–1165, October 1, 2013.
145
21. Andrea Maesani, Pradeep Ruben Fernando and Dario Floreano, “Artificial Evolution by Viability Rather than Competition”, Plos One, Vol. 9, No. 1, Article Number: e86831, January 29, 2014.
22. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Engineering optimization by means of an improved constrained differential evolution”, Computer Methods in Applied Mechanics and Engineering, Vol. 268, pp. 884–904, January 1,
2014.
23. Ilhern Boussaid, Julien Lepagnot and Patrick Siarry, “A survey on optimization metaheuristics”, Information Sciences,
Vol. 237, pp. 82–117, July 10, 2013.
24. Istvan Selek, Jozsef Gergely Bene and Csaba Hos, “Optimal (short-term) pump schedule detection for water distribution
systems by neutral evolutionary search”, Applied Soft Computing, Vol. 12, No. 8, pp. 2336–2351, August 2012.
25. Behrouz Ahmadi-Nedushan, “An optimized instance based learning algorithm for estimation of compressive strength of
concrete”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 5, pp. 1073–1081, August 2012.
26. Hongfeng Wang, Ilkyeong Moon, Shenxiang Yang and Dingwe Wang, “A memetic particle swarm optimization algorithm
for multimodal optimization problems”, Information Sciences, Vol. 197, pp. 38–52, August 15, 2012.
27. Haichuan Lou, Hongye Su, Lei Xie, Yong Gu and Gan Rong, “Inferential Model for Industrial Polypropylene Melt Index
Prediction with Embedded Priori Knowledge and Delay Estimation”, Industrial & Engineering Chemistry Research, Vol.
51, No. 25, pp. 8510–8525, June 27, 2012.
28. Adil Amirjanov and Konstantin Sobolev, “Fractal dimension of Apollonian packing of spherical particles”, Advanced
Powder Technology, Vol. 23, No. 5, pp. 591–595, September 2012.
29. Defang Liu and Bochu Wang, “Biological Swarm Intelligence Based Opportunistic Resource Allocation for Wireless Ad
Hoc Networks”, Wireless Personal Communications, Vol. 66, No. 4, pp. 629–649, October 2012.
30. A.C. Zecchin, A.R. Simpson, H.R. Maier, A. Marchi and J.B. Nixon, “Improved understanding of the searching behavior
of ant colony optimization algorithms applied to the water distribution design problem”, Water Resources Research, Vol.
48, Article Number: W09505, September 5, 2012.
31. Marta Verdaguer, Narcis Clara and Manel Poch, “Ant colony optimization-based method for managing industrial influents in wastewater systems”, AICHe Journal, Vol. 58, No. 10, pp. 3070–3079, October 2012.
32. Shouheng Tuo, Longquan Yong and Fang’an Deng, “A Novel Harmony Search Algorithm Based on Teaching-Learning
Strategies for 0-1 Knapsack Problems”, Scientific World Journal, Article Number: 637412, 2014.
33. B. Nouhi, S. Talatahari, H. Kheiri and C. Cattani, “Chaotic Charged System Search with a Feasible-Based Method for
Constraint Optimization Problems”, Mathematical Problems in Engineering, Article Number: 391765, 2013.
34. Marco Montemurro, Angela Vincenti and Paolo Vannucci, “The Automatic Dynamic Penalisation method (ADP) for
handling constraints with genetic algorithms”, Computer Methods in Applied Mechanics and Engineering, Vol. 256, pp.
70–87, April 1, 2013.
35. Harish Garg and S.P. Sharma, “Multi-objective reliability-redundancy allocation problem using particle swarm optimization”, Computers & Industrial Engineering, Vol. 64, No. 1, pp. 247–255, January 2013.
36. Onder Bulut and M. Fatih Tasgetiren, “An artificial bee colony algorithm for the economic lot scheduling problem”,
International Journal of Production Research, Vol. 52, No. 4, pp. 1150–1170, February 16, 2014.
37. Xinye Cai, Zhenzhou Hu and Zhun Fan, “A novel memetic algorithm based on invasive weed optimization and differential
evolution for constrained optimization”, Soft Computing, Vol. 17, No. 10, pp. 1893–1910, October 2013.
38. Takashi Okamoto and Hironori Hirata, “Constrained optimization using a multipoint type chaotic Lagrangian method
with a coupling structure”, Engineering Optimization, Vol. 45, No. 3, pp. 311–336, March 1, 2013.
39. K. Michail, A.C. Zolotas, R.M. Goodall and J.F. Whidborne, “Optimised configuration of sensors for fault tolerant
control of an electro-magnetic suspension system”, International Journal of Systems Science, Vol. 43, No. 10, pp.
1785–1804, 2012.
40. F. Samanlioglu, W.G. Ferrell and M.E. Kurz, “An interactive memetic algorithm for production and manufacturing
problems modelled as a multi-objective travelling salesman problem”, International Journal of Production Research,
Vol. 50, No. 20, pp. 5671–5682, 2012.
41. Moslem Yousefi, Milad Yousefi and Amer Nordin Darus, “A modified imperialist competitive algorithm for constrained
optimization of plate-fin heat exchangers”, Proceedings of the Institution of Mechanical Engineers Part A–Journal of
Power and Energy, Vol. 226, No. A8, pp. 1050–1059, 2012.
42. Qinqin Fan and Xuefeng Yan, “Differential evolution algorithm with co-evolution of control parameters and penalty
factors for constrained optimization problems”, Asia-Pacific Journal of Chemical Engineering, Vol. 7, No. 2, pp.
227–235, March-April 2012.
43. Lino Costa, Isabel A.C.P. Espirito Santo and Edite M.G.P. Fernandes, “A hybrid genetic pattern search augmented
Lagrangian method for constrained global optimization”, Applied Mathematics and Computation, Vol. 218, No. 18, pp.
9415–9426, May 15, 2012.
146
44. A. Kaveh and S. Talatahari, “An improved ant colony optimization for the design of planar steel frames”, Engineering
Structures, Vol. 32, No. 3, pp. 864–873, March 2010.
45. Hongfeng Wang, Shengxiang Yang, W.H. Ip and Dingwei Wang, “A memetic particle swarm optimisation algorithm for
dynamic multi-modal optimisation problems”, International Journal of Systems Science, Vol. 43, No. 7, pp. 1268–1283,
2012.
46. Nathan Sorenson, Philippe Pasquier and Steve DiPaola, “A Generic Approach to Challenge Modeling for the Procedural
Creation of Video Game Levels”, IEEE Transactions on Computational Intelligence and AI in Games, Vol. 3, No. 3,
pp. 229–244, September 2011.
47. Lauren Davis, Funda Samanlioglu, Xiaochun Jiang, Daniel Mota and Paul Stanfield, “A heuristic approach for allocation
of data to RFID tags: A data allocation knapsack problem (DAKP)”, Computers & Operations Research, Vol. 39, No.
1, pp. 93–104, January 2012.
48. Wen Long, Ximing Liang, Yafei Huang and Yixiong Chen, “A hybrid differential evolution augmented Lagrangian
method for constrained numerical and engineering optimization”, Computer-Aided Design, Vol. 45, No. 12, pp. 1562–
1574, December 2013.
49. A. Villagra, D. Pandolfi and G. Leguizamon, “ Handling constraints with an evolutionary tool for scheduling oil wells
maintenance visits”, Engineering Optimization, Vol. 45, No. 8, pp. 963–981, July-September, 2013.
50. Moslem Yousefi, Rasul Enayatifar, Amer Nordin Darus and Abdul Hanan Abdullah, “ A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers”, International Communications in
Heat and Mass Transfer, Vol. 39, No. 10, pp. 1605–1615, December 2012.
51. Ilhem Boussaid, Amitava Chatterjee, Patrick Siarry and Mohamed Ahmed-Nacer, “ Biogeography-based optimization
for constrained optimization problems”, Computers & Operations Research, Vol. 39, No. 12, pp. 3293–3304, December
2012.
52. Leandro dos Santos Coelho, “An efficient particle swarm approach for mixed-integer programming in reliability-redundancy
optimization applications”, Reliability Engineering & System Safety, Vol. 94, No. 4, pp. 830–837, April 2009.
53. Syeda Darakhshan Jabeen, “Split and Discard Strategy: A New Approach for Constrained Global Optimization”,
International Journal of Artificial Intelligence Tools, Vol. 22, No. 4, Article Number: 1350023, August 2013.
54. Sanyou Zeng, Yang Yang, Yulong Shi, Xianqiang Yang, Bo Xiao, Song Gao, Danping Yu and Zu Yan, “A micro niche evolutionary algorithm with lower-dimensional-search crossover for optimisation problems with constraints”, International
Journal of Bio-Inspired Computation, Vol. 1, No. 3, pp. 177–185, 2009.
55. Adil Amirjanov, “Modelling the dynamics of an adjustment of a search space size in a Genetic Algorithm”, International
Journal of Modern Physics C, Vol. 19, No. 7, pp. 1047–1062, July 2008.
56. Liang Bai, Yongheng Jiang and Dexian Huang, “A Novel Two-Level Optimization Framework Based on Constrained
Ordinal Optimization and Evolutionary Algorithms for Scheduling of Multipipeline Crude Oil Blending”, Industrial &
Engineering Chemistry Research, Vol. 51, No. 26, pp. 9078–9093, July 4, 2012.
57. Shivom Sharma and Gade Pandu Rangaiah, “An improved multi-objective differential evolution with a termination
criterion for optimizing chemical processes”, Computers & Chemical Engineering, Vol. 56 , pp. 155–173, September 13,
2013.
58. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
59. Dhish Saxena, Alessandro Rubino, João A. Duro and Ashutosh Tiwari, “Identifying the redundant, and ranking the
critical, constraints in practical optimization problems”, Engineering Optimization, Vol. 45, Nos. 7-9, pp. 787–809,
July-September, 2013.
60. LiCheng Jiao, Lin Li, RongHua Shang, Fang Liu and Rustam Stolkin, “A novel selection evolutionary strategy for
constrained optimization”, Information Sciences, Vol. 239, pp. 122–141, August 1, 2013.
61. Sabine Helwig, Juergen Branke and Sanaz Mostaghim, “Experimental Analysis of Bound Handling Techniques in Particle
Swarm Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 2, pp. 259–271, April 2013.
62. Francisco J. Rodriguez, Carlos Garcia-Martinez and Manuel Lozano, “Hybrid Metaheuristics Based on Evolutionary
Algorithms and Simulated Annealing: Taxonomy, Comparison, and Synergy Test”, IEEE Transactions on Evolutionary
Computation, Vol. 16, No. 6, pp. 787–800, December 2012.
63. Trung Thanh Nguyen and Xin Yao, “Continuous Dynamic Constrained Optimization—The Challenges”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 6, pp. 769–786, December 2012.
64. Paul Pitiot, Michel Aldanondo, Elise Vareilles, Paul Gaborit, Meriem Djefel and Sabine Carbonnel, “Concurrent product
configuration and process planning, towards an approach combining interactivity and optimality”, International Journal
of Production Research, Vol. 51, No. 2, pp. 524–541, 2013.
147
65. Yuanyuan Zhang, Mark Harman and Soo Ling Lim, “Empirical evaluation of search based requirements interaction
management”, Information and Software Technology, Vol. 55, No. 1, pp. 126–152, January 2013.
66. Colin E. Tschida and Larry M. Silverberg, “Cellular growth algorithms for shape design of truss structures”, Computers
& Structures, Vol. 116, pp. 1–6, January 2013.
67. S. Talatahari, E. Khalili and S.M. Alavizadeh, “Accelerated Particle Swarm for Optimum Design of Frame Structures”,
Mathematical Problems in Engineering, Article Number: 649857, 2013.
68. Mehmet Polat Saka and Zong Woo Geem, “Mathematical and Metaheuristic Applications in Design Optimization of
Steel Frame Structures: An Extensive Review”, Mathematical Problems in Engineering, Vol. Article Number: 271031,
2013.
69. Moslem Yousefi, Rasul Enayatifar, Amer Nordin Darus and Abdul Hanan Abdullah, “Optimization of plate-fin heat
exchangers by an improved harmony search algorithm”, Applied Thermal Engineering, Vol. 50, No. 1, pp. 877–885,
January 10, 2013.
70. Lino Costa, Isabel Espirito Santo and Pedro Oliveira, “An adaptive constraint handling technique for evolutionary
algorithms”, Optimization, Vol. 62, No. 2, pp. 241–253, February 1, 2013.
71. Gilberto Reynoso-Meza, Sergio Garcia-Nieto, Javier Sanchis and F. Xavier Blasco, “Controller Tuning by Means of MultiObjective Optimization Algorithms: A Global Tuning Framework”, IEEE Transactions on Control Systems Technology,
Vol. 21, No. 2, pp. 445–458, March 2013.
72. A. Kaveh and M. Ahangaran, “Social Harmony Search Algorithm for Continuous Optimization”, Iranian Journal of
Science and Technology-Transactions of Civil Engineering, Vol. 36, No. C2, pp. 121–137, August 2012.
73. Jui-Yu Wu, “Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial
Life Approaches”, Mathematical Problems in Engineering, Vol. Article Number: 841410, 2012.
74. Bahriye Akay and Dervis Karaboga, “Artificial bee colony algorithm for large-scale problems and engineering design
optimization”, Journal of Intelligent Manufacturing, Vol. 23, No. 4, pp. 1001–1014, August 2012.
75. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
76. Efrén Mezura-Montes and Omar Cetina-Dominguez, “Empirical analysis of a modified Artificial Bee Colony for constrained numerical optimization”, Applied Mathematics and Computation, Vol. 218, No. 22, pp. 10943–10973, July 15,
2012.
77. Yong Wang and Zixing Cai, “A Dynamic Hybrid Framework for Constrained Evolutionary Optimization”, IEEE Transactions on Systems, Man, and Cybernetics, Part B—Cybernetics, Vol. 42, No. 1, pp. 203–217, February 2012.
78. Yang Tang, Zidong Wang, Huijun Gao, Stephen Swift and Jürgen Kurths, “A Constrained Evolutionary Computation
Method for Detecting Controlling Regions of Cortical Networks”, IEEE-ACM Transactions on Computational Biology
and Bioinformatics, Vol. 9, No. 6, pp. 1569–1581, November-December 2012.
79. Thomas Weise, Raymond Chiong and Ke Tang, “Evolutionary Optimization: Pitfalls and Booby Traps”, Journal of
Computer Science and Technology, Vol. 27, No. 5, pp. 907–936, September 2012.
80. Xiang Li and Gang Du, “BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems”,
Computers & Operations Research, Vol. 40, No. 1, pp. 282–302, January 2013.
81. James N. Richardson, Sigrid Adriaenssens, Philippe Bouillard and Rajan Filomeno Coelho, “Multiobjective topology
optimization of truss structures with kinematic stability repair”, Structural and Multidisciplinary Optimization, Vol. 46,
No. 4, pp. 513–532, October 2012.
82. H.T. Ozturk, Ay. Durmus and Ah. Durmus, “Optimum design of a reinforced concrete beam using artificial bee colony
algorithm”, Computers and Concrete, Vol. 10, No. 3, pp. 295–306, September 2012.
83. Layak Ali, Samrat L. Sabat and Siba K. Udgata, “Particle swarm optimisation with stochastic ranking for constrained
numerical and engineering benchmark problems”, International Journal of Bio-Inspired Computation, Vol. 4, No. 3, pp.
155–166, 2012.
84. Ali Wagdy Mohamed and Hegazy Zaher Sabry, “Constrained optimization based on modified differential evolution
algorithm”, Information Sciences, Vol. 194, pp. 171–208, July 1, 2012.
85. Sanghoun Oh, Chang Wook Ahn and Moongu Jeon, “Effective Constraints Based Evolutionary Algorithm for Constrained Optimization Problems”, International Journal of Innovative Computing Information and Control, Vol. 8, No.
6, pp. 3997–4014, June 2012.
86. Abu S.S.M. Barkat Ullah, Ruhul Sarker and Chris Lokan, “Handling equality constraints in evolutionary optimization”,
European Journal of Operational Research, Vol. 221, No. 3, pp. 480–490, September 16, 2012.
87. Jia-qing Zhao, Ling Wang, Pan Zeng and Wen-hui Fan, “An effective hybrid genetic algorithm with flexible allowance
technique for constrained engineering design optimization”, Expert Systems with Applications, Vol. 39, No. 5, pp.
6041–6051, April 2012.
148
88. Young Ha Yoon and Seung Jo Kim, “Asynchronous Swarm Structural Optimization of the Satellite Adapter Ring”,
Journal of Spacecraft and Rockets, Vol. 49, No. 1, pp. 101–114, January-February 2012.
89. Haibo Zhang and G.P. Rangaiah, “An efficient constraint handling method with integrated differential evolution for
numerical and engineering optimization”, Computers & Chemical Engineering, Vol. 37, pp. 74–88, February 10, 2012.
90. Ali Haydar Kayhan, “Selection and Scaling of Ground Motion Records Using Harmony Search”, Teknik Dergi, Vol. 23,
No. 1, pp. 5751–5775, January 2012.
91. B.Y. Qu and P.N. Suganthan, “Constrained multi-objective optimization algorithm with an ensemble of constraint
handling methods”, Engineering Optimization, Vol. 43, No. 4, pp. 403–416, 2011.
92. Karsten Hentsch and Peter Köchel, “Job scheduling with forbidden setups and two objectives using genetic algorithms
and penalties”, Central European Journal of Operations Research, Vol. 19, No. 3, pp. 285–298, September 2011.
93. Yong Wang and Zixing Cai, “Combining Multiobjective Optimization with Differential Evolution to Solve Constrained
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 117–134, February
2012.
94. Kazuaki Masuda and Kenzo Kurihara, “A constrained global optimization method based on multi-objective particle
swarm optimization”, Electronics and Communications in Japan, Vol. 95, No. 1, pp. 43–54, January 2012.
95. Yong Wang and Zixing Cai, “A hybrid multi-swarm particle swarm optimization to solve constrained optimization
problems”, Frontiers of Computer Science in China, Vol. 3, No. 1, pp. 38–52, March 2009.
96. Massimo Spadoni and Luciano Stefanini, “A Differential Evolution algorithm to deal with box, linear and quadraticconvex constraints for boundary optimization”, Journal of Global Optimization, Vol. 52, No. 1, pp. 171–192, January
2012.
97. Hiroshi Someya, “Theoretical basis of parameter tuning for finding optima near the boundaries of search spaces in
real-coded genetic algorithms”, Soft Computing, Vol. 16, No. 1, pp. 23–45, January 2012.
98. Ali Husseinzadeh Kashan, “An efficient algorithm for constrained global optimization and application to mechanical
engineering design: League championship algorithm (LCA)”, Computer-Aided Design, Vol. 43, No. 12, pp. 1769–1792,
December 2011.
99. Jung Man Hong and Jong Hyup Lee, “Optimal Mobile Switching Center Positioning and Cells Assignment Using
Lagrangian Heuristic”, IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences,
Vol. E94A, No. 11, pp. 2425–2433, November 2011.
100. Amir Kamali, S.M.T. Fatemi Ghomi and F. Jolai, “A multi-objective quantity discount and joint optimization model
for coordination of a single-buyer multi-vendor supply chain”, Computers & Mathematics with Applications, Vol. 62,
No. 8, pp. 3251–3269, October 2011.
101. Maren Urselmann, Sabine Barkmann, Guido Sand and Sebastian Engell, “A Memetic Algorithm for Global Optimization
in Chemical Process Synthesis Problems”, IEEE Transactions on Evolutionary Computation, Vol. 15, No. 5, pp. 659–
683, October 2011.
102. Michael Angelo A. Pedrasa, Ted D. Spooner and Iain F. MacGill, “A novel energy service model and optimal scheduling
algorithm for residential distributed energy resources”, Electric Power Systems Research, Vol. 81, No. 12, pp. 2155–2163,
December 2011.
103. A. Rama Mohan Rao and K. Lakshmi, “Discrete hybrid PSO algorithm for design of laminate composites with multiple
objectives”, Journal of Reinforced Plastics and Composites, Vol. 30, No. 20, pp. 1703–1727, October 2011.
104. Monjur Mourshed, Shariful Shikder and Andrew D.F. Price, “Phi-array: A novel method for fitness visualization and
decision making in evolutionary design optimization”, Advanced Engineering Informatics, Vol. 25, No. 4, pp. 676–687,
October 2011.
105. Sanghoun Oh, Yaochu Jin and Moongu Jeon, “Approximate Models for Constraint Functions in Evolutionary Constrained Optimization”, International Journal of Innovative Computing Information and Control, Vol. 7, No. 11, pp.
6585–6603, November 2011.
106. Bo Liu, Ling Wang, Ying Liu and Shouyang Wang, “A unified framework for population-based metaheuristics”, Annals
of Operations Research, Vol. 186, No. 1, pp. 231–262, June 2011.
107. Ping-Teng Chang and Jung-Hua Lee, “A fuzzy DEA and knapsack formulation integrated model for project selection”,
Computers & Operations Research, Vol. 39, No. 1, pp. 112–125, January 2012.
108. Romanas Puisa and Heinrich Streckwall, “Prudent constraint-handling technique for multiobjective propeller optimisation”, Optimization and Engineering, Vol. 12, No. 4, pp. 657–680, December 2011.
109. Xiang Li and Gang Du, “Inequality constraint handling in genetic algorithms using a boundary simulation method”,
Computers & Operations Research, Vol. 39, No. 3, pp. 521–540, March 2012.
110. Ilya Tyapin and Geir Hovland, “The Gantry-Tau parallel kinematic machine-kinematic and elastodynamic design optimisation”, Meccanica, Vol. 46, No. 1, pp. 113–129, February 2011.
149
111. Ilhem Boussaid, Amitava Chatterjee, Patrick Siarry and Mohamed Ahmed-Nacer, “Hybridizing Biogeography-Based Optimization With Differential Evolution for Optimal Power Allocation in Wireless Sensor Networks”, IEEE Transactions
on Vehicular Technology, Vol. 60, No. 5, pp. 2347–2353, June 2011.
112. Dusko Kancev, Blaze Gjorgiev and Marko Cepin, “Optimization of test interval for ageing equipment: A multi-objective
genetic algorithm approach”, Journal of Loss Prevention in the Process Industries, Vol. 24, No. 4, pp. 397–404, July
2011.
113. Songtao Guo, Chuangyin Dang and Xiaofeng Liao, “Joint opportunistic power and rate allocation for wireless ad hoc
networks: An adaptive particle swarm optimization approach”, Journal of Network and Computer Applications, Vol. 34,
No. 4, pp. 1353–1365, July 2011.
114. P.W. Jansen and R.E. Perez, “Constrained structural design optimization via a parallel augmented Lagrangian particle
swarm optimization approach”, Computers & Structures, Vol. 89, Nos. 13-14, pp. 1352–1366, July 2011.
115. D. Safari, Mahmoud R. Maheri and A. Maheri, “Optimum design of steel frames using a multiple-deme GA with improved
reproduction operators”, Journal of Constructional Steel Research, Vol. 67, No. 8, pp. 1232–1243, August 2011.
116. Moo-Sun Kim, Woo Il Lee, Woo-Suck Han and Alain Vautrin, “Optimisation of location and dimension of SMC precharge
in compression moulding process”, Computers & Structures, Vol. 89, Nos. 15-16, pp. 1523–1534, August 2011.
117. S. Sivananaithaperumal, S. Miruna Joe Amali, S. Baskar and P.N. Suganthan, “Constrained self-adaptive differential
evolution based design of robust optimal fixed structure controller”, Engineering Applications of Artificial Intelligence,
Vol. 24, No. 6, pp. 1084–1093, September 2011.
118. Mahmoud Mesbah, Majid Sarvi and Graham Currie, “Optimization of Transit Priority in the Transportation Network
Using a Genetic Algorithm”, IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 3, pp. 908–919,
September 2011.
119. Jianyong Chen, Qiuzhen Lin and LinLin Shen, “An Immune-Inspired Evolution Strategy for Constrained Optimization
Problems”, International Journal on Artificial Intelligence Tools, Vol. 20, No. 3, pp. 549–561, June 2011.
120. Massimiliano Di Penta, Mark Harman and Giuliano Antoniol, “The use of search-based optimization techniques to
schedule and staff software projects: an approach and an empirical study”, Software–Practice & Experience, Vol. 41,
No. 5, pp. 495–519, April 2011.
121. Anthony John Medland and Jason Matthews, “The implementation of a direct search approach for the resolution of
complex and changing rule-based problems”, Engineering with Computers, Vol. 27, No. 2, pp. 105–115, April 2011.
122. Thomas Tometzki and Sebastian Engell, “Systematic Initialization Techniques for Hybrid Evolutionary Algorithms for
Solving Two-Stage Stochastic Mixed-Integer Programs”, IEEE Transactions on Evolutionary Computation, Vol. 15, No.
2, pp. 196–214, April 2011.
123. Yong Wang and Zixing Cai, “Constrained Evolutionary Optimization by Means of (µ + λ)-Differential Evolution and
Improved Adaptive Trade-Off Model”, Evolutionary Computation, Vol. 19, No. 2, 249–285, Summer 2011.
124. Ali Haydar Kayhan, Kasim Armagan Korkmaz and Ayhan Irfanoglu, “Selecting and scaling real ground motion records
using harmony search algorithm”, Soil Dynamics and Earthquake Engineering, Vol. 31, No. 7, pp. 941–953, July 2011.
125. Moslem Kazemi, Gary G. Wang, Shahryar Rahnamayan and Kamal Gupta, “Metamodel-Based Optimization for Problems With Expensive Objective and Constraint Functions”, Journal of Mechanical Design, Vol. 133, No. 1, Article
Number: 014505, January 2011.
126. Debarati Kundu, Kaushik Suresh, Sayan Ghosh, Swagatam Das, B.K. Panigrahi and Sanjoy Das, “Multi-objective
optimization with artificial weed colonies”, Information Sciences, Vol. 181, No. 12, pp. 2441–2454, June 15, 2011.
127. Cristian Perea, Victor Yepes, Julian Alcala, Antonio Hospitaler and Fernando Gonzalez-Vidosa, “A parametric study of
optimum road frame bridges by threshold acceptance”, Indian Journal of Engineering and MAterials Sciences, Vol. 17,
No. 6, pp. 427–437, December 2010.
128. Haiping Ma and Dan Simon, “Blended biogeography-based optimization for constrained optimization”, Engineering
Applications of Artificial Intelligence, Vol. 24, No. 3, pp. 517–525, April 2011.
129. Jui-Yu Wu, “Solving Constrained Global Optimization via Artificial Immune System”, International Journal on Artificial
Intelligence Tools, Vol. 20, No. 1, pp. 1–27, February 2011.
130. Hong Li, Yong-Chang Jiao and Li Zhang, “Hybrid differential evolution with a simplified quadratic approximation for
constrained optimization problems”, Engineering Optimization, Vol. 43, No. 2, pp. 115–134, 2011.
131. Lei Gao and Atakelty Hailu, “Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems”, International Journal of Computational Intelligence Systems, Vol. 3, No. 6, pp. 832–842, December
2010.
132. Andreas Konstantinidis and Kun Yang, “Multi-objective K-connected Deployment and Power Assignment in WSNs
using a problem-specific constrained evolutionary algorithm based on decomposition”, Computer Communications, Vol.
34, No. 1, pp. 83–98, January 15, 2011.
150
133. Hong-Shuang Li and Siu-Kiu Au, “Design optimization using Subset Simulation algorithm”, Structural Safety, Vol. 32,
No. 6, pp. 384–392, 2010.
134. Liang Bai, Yongheng Jiang, Dexian Huang and Xianguang Liu, “A Novel Scheduling Strategy for Crude Oil Blending”,
Chinese Journal of Chemical Engineering, Vol. 18, No. 5, pp. 777–786, October 2010.
135. Zai Wang, Ke Tang and Xin Yao, “A Memetic Algorithm for Multi-Level Redundancy Allocation”, IEEE Transactions
on Reliability, Vol. 59, No. 4, pp. 754–765, December 2010.
136. Manoj Kumar Maharana and K. Shanti Swarup, “Optimization based graph theoretic approach for corrective control
strategies to mitigate overloads”, European Transactions on Electrical Power, Vol. 20, No. 8, pp. 1009–1024, November
2010.
137. Javier Sanchis, Miguel A. Martinez, Xavier Blasco and Gilberto Reynoso-Meza, “Modelling preferences in multi-objective
engineering design”, Engineering Applications of Artificial Intelligence, Vol. 23, No. 8, pp. 1255–1264, December 2010.
138. Rammohan Mallipeddi and Ponnuthurai N. Suganthan, “Ensemble of Constraint Handling Techniques”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 4, pp. 561–579, August 2010.
139. Taha Chettibi, “Synthesis of dynamic motions for robotic manipulators with geometric path constraints”, Mechatronics,
Vol. 16, No. 9, pp. 547–563, November 2006.
140. Amir Poursamad and Morteza Montazeri, “Design of genetic-fuzzy control strategy for parallel Hybrid Electric Vehicles”,
Control Engineering Practice, Vol. 16, No. 7, pp. 861–873, July 2008.
141. S. Caux, W. Hankache, M. Fadel and D. Hissel, “On-line fuzzy energy management for hybrid fuel cell systems”,
International Journal of Hydrogen Energy, Vol. 35, No. 5, pp. 2134–2143, March 2010.
142. Gerardo Canfora, Massimiliano Di Penta, Raffaele Esposito and Maria Luisa Villani, “A framework for QoS-aware
binding and re-binding of composite web services”, Journal of Systems and Software, Vol. 81, No. 10, pp. 1754–1769,
October 2008.
143. A. Kaveh and S. Talatahari, “Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for
optimization of truss structures”, Computers & Structures, Vol. 87, Nos. 5-6, pp. 267–283, March 2009.
144. Stephanus Daniel Handoko, Chee Keong Kwoh and Yew-Soon Ong, “Feasibility Structure Modeling: An Effective
Chaperone for Constrained Memetic Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 5,
pp. 740–758, October 2010.
145. Tobias Wagner and Heike Trautmann, “Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary
Algorithms by Means of Desirability Functions”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 5, pp.
688–701, October 2010.
146. R. Toscano and P. Lyonnet, “A new heuristic approach for non-convex optimization problems”, Information Sciences,
Vol. 180, No. 10, pp. 1955–1966, May 15, 2010.
147. Efren Mezura-Montes, Mariana Miranda-Varela and Rubi del Carmen Gomez-Ramon, “Differential evolution in constrained numerical optimization: An empirical study”, Information Sciences, Vol. 180, No. 22, pp. 4223–4262, November
15, 2010.
148. Qiaoling Wang, Xiao-Zhi Gao and Changhong Wang, “An Adaptive Bacterial Foraging Algorithm for Constrained
Optimization”, International Journal of Innovative Computing Information and Control, Vol. 6, No. 8, pp. 3585–3593,
August 2010.
149. Soorathep Kheawhom, “Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical
engineering optimization problem”, Journal of Industrial and Engineering Chemistry, Vol. 16, No. 4, pp. 620–628, July
25, 2010.
150. A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search”, Acta Mechanica, Vol.
213, Nos. 3-4, pp. 267–289, September 2010.
151. Konstantin Sobolev and Adil Amirjanov, “Application of genetic algorithm for modeling of dense packing of concrete
aggregates”, Construction and Building Materials, Vol. 24, No. 8, pp. 1449–1455, August 2010.
152. Ling Wang and Ling-po Li, “An effective differential evolution with level comparison for constrained engineering design”,
Structural and Multidisciplinary Optimization, Vol. 41, No. 6, pp. 947–963, June 2010.
153. T.-H. Kim, I. Maruta and T. Sugie, “A simple and efficient constrained particle swarm optimization and its application
to engineering design problems”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical
Engineering Science, Vol. 224, No. C2, pp. 389–400, 2010.
154. K. Vijayalakshmi and S. Radhakrishnan, “A novel hybrid immune-based GA for dynamic routing to multiple destinations
for overlay networks”, Soft Computing, Vol. 14, No. 11, pp. 1227–1239, September 2010.
155. Cheng-gang Cui, Yan-jun Li and Tie-jun Wu, “A relative feasibility degree based approach for constrained optimization
problems”, Journal of Zhejiang University–Science C–Computers & Electronics, Vol. 11, No. 4, pp. 249–260, April
2010.
151
156. Youlin Lu, Jianzhong Zhou, Hui Qin, Yinghai Li and Yongchuan Zhang, “An adaptive hybrid differential evolution
algorithm for dynamic economic dispatch with valve-point effects”, Expert Systems with Applications, Vol. 37, No. 7,
pp. 4842–4849, July 2010.
157. Martin Schlueter and Matthias Gerdts, “The oracle penalty method”, Journal of Global Optimization, Vol. 47, No. 2,
pp. 293–325, June 2010.
158. S. Rajasekaran, “Optimal laminate sequence of thin-walled composite beams of generic section using evolution strategies”,
Structural Engineering and Mechanics, Vol. 34, No. 5, pp. 597–609, March 30, 2010.
159. Hong-Zhong Huang, Jian Qu and Ming J. Zuo, “Genetic-algorithm-based optimal apportionment of reliability and
redundancy under multiple objectives”, IIE Transactions, Vol. 41, No. 4, pp. 287–298, 2009.
160. Martin Schlueter, Jose A. Egea and Julio R. Banga, “Extended ant colony optimization for non-convex mixed integer
nonlinear programming”, Computers & Operations Research, Vol. 36, No. 7, pp. 2217–2229, July 2009.
161. Severino F. Galán and Ole J. Mengshoel, “Constraint Handling Using Tournament Selection: Abductive Inference in
Partly Deterministic Bayesian Networks”, Evolutionary Computation, Vol. 17, No. 1, pp. 55–88, Spring 2009.
162. Wenyin Gong, Zhihua Cai and Li Zhu, “An efficient multiobjective differential evolution algorithm for engineering
design”, Structural and Multidisciplinary Optimization, Vol. 38, No. 2, pp. 137–157, April 2009.
163. Hai Shen, Yunlong Zhu, Ben Niu and Q.H. Wu, “An improved group search optimizer for mechanical design optimization
problems”, Progress in Natural Science, Vol. 19, No. 1, pp. 91–97, January 10, 2009.
164. Joana Dias, M. Eugenia Captivo and Joao Climaco, “A memetic algorithm for multi-objective dynamic location problems”, Journal of Global Optimization, Vol. 42, No. 2, pp. 221–253, October 2008.
165. Jinhua Wang and Zeyong Yin, “A ranking selection-based particle swarm optimizer for engineering design optimization
problems”, Structural and Multidisciplinary Optimization, Vol. 37, No. 2, pp. 131–147, December 2008.
166. Karin Zielinski, Petra Weitkemper, Rainer Laur and Karl-Dirk Kammeyer, “Optimization of Power Allocation for
Interference Cancellation with Particle Swarm Optimization”, IEEE Transactions on Evolutionary Computation, Vol.
13, No. 1, pp. 128–150, February 2009.
167. Rajkumar Roy, Srichand Hinduja and Roberto Teti, “Recent advances in engineering design optimisation: Challenges
and future trends”, CIRP Annals-Manufacturing Technology, Vol. 57, No. 2, pp. 697–715, 2008.
168. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
169. Erwie Zahara and Chia-Hsin Hu, “Solving constrained optimization problems with hybrid particle swarm optimization”,
Engineering Optimization, Vol. 40, No. 11, pp. 1031–1049, November 2008.
170. Haiyan Lu and Weiqi Chen, “Self-adaptive velocity particle swarm optimization for solving constrained optimization
problems”, Journal of Global Optimization, Vol. 41, No. 3, pp. 427–445, July 2008.
171. Min Zhang, Wenjian Luo and Xufa Wang, “Differential evolution with dynamic stochastic selection for constrained
optimization”, Information Sciences, Vol. 178, No. 15, pp. 3043–3074, August 1, 2008.
172. Elizabeth F. Wanner, Frederico G. Guimarães, Ricardo H.C. Takahashi and Peter J. Fleming, “Local Search with
Quadratic Approximations into Memetic Algorithms for Optimization with Multiple Criteria”, Evolutionary Computation, Vol. 16, No. 2, pp. 185–224, Summer 2008.
173. Guido Sand, Jochen Till, Thomas Tometzki, Maren Urselmann, Michael Emmerich and Sebastian Engell, “Evolutionary
algorithms for the online optimization of batch production schedules”, AT-Automatisierungstechnik, Vol. 56, No. 2, pp.
80–89, 2008.
174. Steven Orla Kimbrough, Gary J. Koehler, Ming Lu and David Harlan Wood, “On a Feasible-Infeasible Two-Population
(FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch”, European Journal of
Operational Research, Vol. 190, No. 2, pp. 310–327, October 16, 2008.
175. J.R. Jimenez-Octavio, O. Lopez-Garcia, E. Pilot and A. Carnicero, “Coupled electromechanical optimization of power
transmission”, CMES-Computer Modeling in Engineering & Sciences, Vol. 25, No. 2, pp. 81–97, February 2008.
176. J.W. Wind, D. Akcay Perdahcioglu and A. de Boer, “Distributed multilevel optimization for complex structures”,
Structural and Multidisciplinary Optimization, Vol. 36, No. 1, pp. 71–81, July 2008.
177. Tien-Tung Chung and Chia-Sheng Shih, “Structural optimization using genetic algorithms with fuzzy rule-based systems”, Journal of the Chinese Society of Mechanical Engineering, Vol. 28, No. 5, pp. 523–532, October 2007.
178. Kusum Deep and Dipti, “A self-organizing migrating genetic algorithm for constrained optimization”, Applied Mathematics and Computation, Vol. 198, No. 1, pp. 237–250, April 15, 2008.
179. Simone Puzzi and Alberto Carpinteri, “A double-multiplicative dynamic penalty approach for constrained evolutionary
optimization”, Structural and Multidisciplinary Optimization, Vol. 35, No. 5, pp. 431–445, May 2008.
152
180. Yong Zhang, Lawrence O. Hall, Dmitry B. Goldgof and Sudeep Sarkar, “A Constrained Genetic Approach for Computing
Material Property of Elastic Objects”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 341–357,
June 2006.
181. Wai Kuan Foong, Holger R. Maier and Angus R. Simpson, “Power plant maintenance scheduling using ant colony
optimization: an improved formulation”, Engineering Optimization, Vol. 40, No. 4, pp. 309–319, April 2008.
182. Avi Ostfeld and Ariel Tubaltzev, “Ant colony optimization for least-cost design and operation of pumping water distribution systems”, Journal of Water Resources Planning and Management–ASCE, Vol. 134, No. 2, pp. 107–118,
March-April 2008.
183. Javier Sanchis, Miguel A. Martinez and Xavier Blasco, “Integrated multiobjective optimization and a priori preferences
using genetic algorithms”, Information Sciences, Vol. 178, No. 4, pp. 931–951, February 15, 2008.
184. Leandro dos Santos Coelho and Viviana Cocco Mariani, “Use of chaotic sequences in a biologically inspired algorithm
for engineering design optimization”, Expert Systems with Applications, Vol. 34, No. 3, pp. 1905–1913, April 2008.
185. A. Ponsich, C. Azzaro-Pantel, S. Domenech and L. Pibouleau, “Constraint handling strategies in Genetic Algorithms
application to optimal batch plant design”, Chemical Engineering and Processing, Vol. 47, No. 3, pp. 420–434, March
2008.
186. A. Kaveh and M. Shahrouzi, “Dynamic selective pressure using hybrid evolutionary and ant system strategies for
structural optimization”, International Journal for Numerical Methods in Engineering, Vol. 73, No. 4, pp. 544–563,
January 22, 2008.
187. J. Sanchis, M. Martinez and X. Blasco, “Multi-objective engineering design using preferences”, Engineering Optimization,
Vol. 40, No. 3, pp. 253–269, 2008.
188. O. Hasancebi, “Adaptive evolution strategies in structural optimization: Enhancing their computational performance
with applications to large-scale structures”, Computers & Structures, Vol. 86, Nos. 1–2, pp. 119–132, January 2008.
189. Adil Amirjanov, “Investigation of a changing range genetic algorithm in noisy environments”, International Journal for
Numerical Methods in Engineering, Vol. 73, No. 1, pp. 26–46, January 1, 2008.
190. Wai Kuan Foong, Angus R. Simpson, Holger R. Maier and Stephen Stolp, “Ant colony optimization for power plant
maintenance scheduling optimization - a five-station hydropower system”, Annals of Operations Research, Vol. 159, No.
1, pp. 433–450, March 2008.
191. Yong Wang, Zixing Cai, Yuren Zhou and Wei Zeng, “An Adaptive Tradeoff Model for Constrained Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 80–92, February 2008.
192. Chandra Sekhar Pedamallu and Linet Ozdamar, “Investigating a hybrid simulated annealing and local search algorithm
for constrained optimization”, European Journal of Operational Research, Vol. 185, No. 3, pp. 1230–1245, March 16,
2008.
193. M.P. Saka, “Optimum topological design of geometrically nonlinear single layer latticed domes using coupled genetic
algorithm”, Computers & Structures, Vol. 85, Nos. 21–22, pp. 1635–1646, November 2007.
194. Guangtao Fu, David Butler and Soon-Thiam Khu, “Multiple objective optimal control of integrated urban wastewater
systems”, Environmental Modelling & Software, Vol. 23, No. 2, pp. 225–234, February 2008.
195. S. Rajasekaran and S. Lavanya, “Hybridization of genetic algorithm with immune system for optimization problems in
structural engineering”, Structural and Multidisciplinary Optimization, Vol. 34, No. 5, pp. 415–429, November 2007.
196. Maren Urselmann, Michael T.M. Emmerich, Jochen Till, Guido Sand and Sebastian Engell, “Design of problem-specific
evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling”, Engineering Optimization, Vol. 39, No. 5, pp. 529–549, July 2007.
197. Panta Lucic and Dusan Teodorovic, “Metaheuristics approach to the aircrew rostering problem”, Annals of Operations
Research, Vol. 155, No. 1, pp. 311–338, November 2007.
198. Omid Bozorg Haddad and Miguel A. Marino, “Dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with honey-bee mating optimization (HBMO) algorithm”, Journal of Hydroinformatics,
Vol. 9, No. 3, pp. 233–250, July 2007.
199. Jing Liu, Weicai Zhong and Licheng Hao, “An organizational evolutionary algorithm for numerical optimization”, IEEE
Transactions on Systems, Man and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 1052–1064, August 2007.
200. A. Andrade-Campos, S. Thuillier, P. Pilvin and F. Teixeira-Dias, “On the determination of material parameters for
internal variable thermoelastic-viscoplastic constitutive models”, International Journal of Plasticity, Vol. 23, No. 8, pp.
1349–1379, 2007.
201. Yong Wang, Hui Liu, Zixing Cai and Yuren Zhou, “An orthogonal design based constrained evolutionary optimization
algorithm”, Engineering Optimization, Vol. 39, No. 6, pp. 715–736, September 2007.
202. Yuren Zhou and Jun He, “A Runtime Analysis of Evolutionary Algorithms for Constrained Optimization Problems”,
IEEE Transactions on Evolutionary Computation, Vol. 11, No. 5, pp. 608–619, October 2007.
153
203. Jie Hu, Yinghong Peng and Guangleng Xiong, “Knowledge network driven coordination and robust optimization to
support concurrent and collaborative parameter design”, Concurrent Engineering-Research and Applications, Vol. 15,
No. 1, pp. 43–52, March 2007.
204. Pei Yee Ho and Kazuyuki Shimizu, “Evolutionary constrained optimization using an addition of ranking method and a
percentage-based tolerance value adjustment scheme”, Information Sciences, Vol. 177, No. 14, pp. 2985–3004, July 15,
2007.
205. M. Mahdavi, M. Fesanghary and E. Damangir, “An improved harmony search algorithm for solving optimization problems”, Applied Mathematics and Computation, Vol. 188, No. 2, pp. 1567–1579, May 15, 2007.
206. Daniel E. Salazar and Claudio M. Rocco, “Solving advanced multi-objective robust designs by means of multiple objective
evolutionary algorithms (MOEA): A reliability application”, Reliability Engineering & System Safety, Vol. 92, No. 6,
pp. 697–706, June 2007.
207. Akira Oyama, Koji Shimoyama and Kozo Fujii, “New constraint-handling method for multi-objective and multiconstraint evolutionary optimization”, Transactions of the Japan Society for Aeronautical and Space Sciences, Vol.
50, No. 167, pp. 56–62, May 2007.
208. Yong Wang, Zixing Cai, Guanqi Guo and Yuren Zhou, “Multiobjective optimization and hybrid evolutionary algorithm
to solve constrained optimization problems”, IEEE Transactions on Systems, Man and Cybernetics Part B–Cybernetics,
Vol. 37, No. 3, pp. 560–575, June 2007.
209. Fu-zhuo Huang, Ling Wang and Qie He, “An effective co-evolutionary differential evolution for constrained optimization”,
Applied Mathematics and Computation, Vol. 186, No. 1, pp. 340–356, March 1, 2007.
210. Qie He and Ling Wang, “A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization”,
Applied Mathematics and Computation, Vol. 186, No. 2, pp. 1407–1422, March 15, 2007.
211. Aaron C. Zecchin, Angus R. Simpson, Holger R. Maier and John B. Nixon, “Parametric Study for an Ant Algorithm
Applied to Water Distribution System Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 2,
pp. 175–191, April 2005.
212. R. Farmani, J.A. Wright, D.A. Savic and G.A. Walters, “Self-adaptive fitness formulation for evolutionary constrained
optimization of water systems”, Journal of Computing in Civil Engineering, Vol. 19, No. 2, pp. 212–216, April 2005.
213. Xavier Bonnaire and Marı́a-Cristina Riff, “Adapting Evolutionary Parameters by Dynamic Filtering for Operators
Inheritance Strategy”, in Christian Lemaı̂tre, Carlos A. Reyes and Jesús A. González (editors), Advances in Artificial
Intelligence—IBERAMIA 2004, Springer, Lecture Notes in Artificial Intelligence Vol. 3315, pp. 225–234, Puebla,
México, November 2004.
214. R.F. Coelho and P. Bouillard, “A multicriteria evolutionary algorithm for mechanical design optimization with expert
rules”, International Journal for Numerical Methods in Engineering, Vol. 62, No. 4, pp. 516–536, January 28, 2005.
215. Steven Orla Kimbrough, Ming Lu, and David Harlan Wood, “Exploring the Evolutionary Details of a Feasible-Infeasible
Two-Population GA”, in Xin Yao et al. (editors), Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag,
Lecture Notes in Computer Science, Vol. 3242, pp. 292–301, September 2004.
216. Anders Angantyr and Jan Olov Aidanpää, “A Pareto-Based Genetic Algorithm Search Approach to Handle Damped
Natural Frequency Constraints in Turbo Generator Rotor System Design”, Journal of Engineering for Gas Turbines and
Power, Vol. 126, No. 3, pp. 619–625, July 2004.
217. B. Lin and D.C. Miller, “Tabu search algorithm for chemical process optimization”, Computers & Chemical Engineering,
Vol. 28, No. 11, pp. 2287–2306, October 15, 2004.
218. B. Meyer and A. Ernst, “Integrating ACO and constraint propagation”, in Proceedings of Ant Colony Optimization and
Swarm Intelligence, Springer, Lecture Notes in Computer Science, Vol. 3172, pp. 166–177, 2004.
219. Talib Hussain, David Montana and Gordon Vidaver, “Evolution-Based Deliberative Planning for Cooperating Unmanned Ground Vehicles in a Dynamic Environment”, in Kalyanmoy Deb et al. (editors), Genetic and Evolutionary
Computation–GECCO 2004. Proceedings of the Genetic and Evolutionary Computation Conference. Part II, SpringerVerlag, Lecture Notes in Computer Science Vol. 3103, pp. 1017–1029, Seattle, Washington, USA, June 2004.
220. Lauren M. Clevenger and William E. Hart, “Convergence Examples of a Filter-Based Evolutionary Algorithm”, in
Kalyanmoy Deb et al. (editors), Genetic and Evolutionary Computation–GECCO 2004. Proceedings of the Genetic
and Evolutionary Computation Conference. Part I, Springer-Verlag, Lecture Notes in Computer Science Vol. 3102, pp.
666–677, Seattle, Washington, USA, June 2004.
221. S. He, E. Prempain and Q.H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems”,
Engineering Optimization, Vol. 36, No. 5, pp. 585–605, October 2004.
222. R. Ganguli, “Survey of recent developments in rotorcraft design optimization”, Journal of Aircraft, Vol. 41, No. 3, pp.
493–510 May-June 2004.
223. T. Wu and P. O’Grady, “A methodology for improving the design of a supply chain”, International Journal of Computer
Integrated Manufacturing, Vol. 17, No. 4, pp. 281–293, June 2004.
154
224. A.G. Bakirtzis, P.N. Biskas, C.E. Zoumas and V. Petridis, “Closure on “Optimal power flow by enhanced genetic
algorithm””, IEEE Transactions on Power Systems, Vol. 18, No. 3, pp. 1219–1220, August 2003.
225. L. Du, J. Bigham and L. Cuthbert, “Towards intelligent geographic load balancing for mobile cellular networks”, IEEE
Transactions on Systems, Man and Cybernetics Part C—Applications and Reviews, Vol. 33, No. 4, pp. 480–491,
November 2003.
226. S. Rajasekaran, V.S. Mohan and O. Khamis, “The optimisation of space structures using evolution strategies with
functional networks”, Engineering with Computers, Vol. 20, No. 1, pp. 75–87, March 2004.
227. Lin Du and John Bigham, “Constrained Coverage Optimisation for Mobile Cellular Networks”, in Günther Raidl et al.
(editors), Applications of Evolutionary Computing. Evoworkshops 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART,
EvoROB, and EvoSTIM, pp. 199–210, Springer, Lecture Notes in Computer Science Vol. 2611, Essex, UK, April 2003.
228. E.M.R. Fairbairn, M.M. Silvoso, R.D. Toledo, J.L.D. Alves and N.F.F. Ebecken, “Optimization of mass concrete construction using genetic algorithms”, Computers & Structures, Vol. 82, Nos. 2–3, pp. 281–299, January 2004.
229. A. Kanarachos, D. Koulocheris and H. Vrazopoulos, “Evolutionary algorithms with deterministic mutation operators
used for the optimization of the trajectory of a four-bar mechanism”, Mathematics and Computers in Simulation, Vol.
63, No. 6, pp. 483–492, November 24, 2003.
230. D.S. Juang, Y.T. Wu and W.T. Chang, “Optimum design of truss structures using discrete Lagrangian method”, Journal
of the Chinese Institute of Engineers, Vol. 26, No. 5, pp. 635–646, September 2003.
231. K. Miettinen, M.M. Makela and J. Toivanen, “Numerical comparison of some penalty-based constraint handling techniques in genetic algorithms”, Journal of Global Optimization, Volume 27, No. 4, pp. 427–446, December 2003.
232. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical design with constraints and preferences: application
to a purge valve”, Computer Methods in Applied Mechanics and Engineering, Vol. 192, Nos. 39–40, pp. 4355–4378,
2003.
233. Steven Orla Kimbrough, Ming Lu, David Harlan Wood, and D.J. Wu, “Exploring a Two-Population Genetic Algorithm”,
in Erick Cantú-Paz et al. (editors), Genetic and Evolutionary Computation—GECCO 2003. Proceedings, Part I, pp.
1148–1159, Springer. Lecture Notes in Computer Science Vol. 2723, July 2003.
234. P.M. Pawar and R. Ganguli, “Genetic Fuzzy System for Damage Detection in Beams and Helicopter Rotor Blades”,
Computer Methods in Applied Mechanics and Engineering, Vol. 192, Nos. 16–18, pp. 2031–2057, 2003.
235. Dragos Arotaritei and Mircea Gh. Negoita, “Optimization of Recurrent NN by GA with Variable Length Genotype”,
in Bob McKay and John S. Slaney (eds), AI 2002: Advances in Artificial Intelligence, 15th Australian Joint Conference
on Artificial Intelligence and Applications, Springer, Lecture Notes in Computer Science, Vol. 2557, pp. 681–692, 2002.
236. Eduardo Fernández and Jorge Navarro, “A Genetic Search for Exploiting a Fuzzy Preference Model of Portfolio Problems
with Public Projects”, Annals of Operations Research, Vol. 117, Nos. 1–4, pp. 191–213, November 2002.
237. A. Kurpati, S. Azarm and J. Wu, “Constraint handling improvements for multiobjective genetic algorithms”, Structural
and Multidisciplinary Optimization, Vol. 23, No. 3, pp. 204–213, April 2002.
238. Marco Farina, Alessandro Bramanti and Paolo Di Barba, “A GRS Method for Pareto-Optimal Front Identification in
Electromagnetic Synthesis”, IEE Proceedings—Science, Measurement and Technology, Vol. 149, No. 5, pp. 207–213,
September 2002.
239. B. Fazlollahi and R. Vahidov, “A Method for Generation of Alternatives by Decision Support Systems”, Journal of
Management Information Systems, Vol. 18, No. 2, pp. 229–250, Fall 2001.
240. H. Schmidt and G. Thierauf, “A combined heuristic optimization technique”, Advances in Engineering Software, Vol.
36, No. 1, pp. 11–19, January 2005.
241. Q.S. Ren, J. Zeng and F.H. Qi, “History information based optimization of additively decomposed function with constraints”, Computational and Information Science, Proceedings, Springer-Verlag, Lecture Notes in Computer Science
Vol. 3314, pp. 359–364, 2004.
242. A. Amirjanov, “A changing range genetic algorithm”, International Journal for Numerical Methods in Engineering, Vol.
61, No. 15, pp. 2660–2674, December 21, 2004.
243. M.G. Sahab, A.F. Ashour and V.V. Toropov, “A hybrid genetic algorithm for reinforced concrete flat slab buildings”,
Computers & Structures, Vol. 83, Nos. 8–9, pp. 551–559, March 2005.
244. T.P. Runarsson and X. Yao, “Search biases in constrained evolutionary optimization”, IEEE Transactions on Systems,
Man, and Cybernetics Part C—Applications and Reviews, Vol. 35, No. 2, pp. 233–243, May 2005.
245. A. Amirjanov, “The development of a changing range genetic algorithm”, Computer Methods in Applied Mechanics and
Engineering, Vol. 195, Nos. 19–22, pp. 2495–2508, 2006.
246. H.H. Nguyen and C.W. Chan, “Applications of artificial intelligence for optimization of compressor scheduling”, Engineering Applications of Artificial Intelligence, Vol. 19, No. 2, pp. 113–126, March 2006.
155
247. P. Chootinan and A. Chen, “Constraint handling in genetic algorithms using a gradient-based repair method”, Computers
& Operations Research, Vol. 33, No. 8, pp. 2263–2281, August 2006.
248. A. Amirjanov and K. Sobolev, “Optimal proportioning of concrete aggregates using a self-adaptive genetic algorithm”,
Computers and Concrete, Vol. 2, No. 5, pp. 411–421, October 2005.
249. M. Liu, S.A. Burns and Y.K. Wen, “Genetic algorithm based construction-conscious minimum weight design of seismic
steel moment-resisting frames”, Journal of Structural Engineering–ASCE, Vol. 132, No. 1, pp. 50–58, January 2006.
250. D.J. Barrett, M.J. Hill, L.B. Hutley, J. Beringer, J.H. Xu, G.D. Cook, J.O. Carter and R.J. Williams, “Prospects
for improving savanna biophysical models by using multiple-constraints model-data assimilation methods”, Australian
Journal of Botany, Vol. 53, No. 7, pp. 689–714, 2005.
251. A. Amirjanov and K. Sobolev, “Genetic algorithm for cost optimization of modified multi-component binders”, Building
and Environment, Vol. 41, No. 2, pp. 195–203, February 2006.
252. Tetsuyuki Takahama and Setsuko Sakai, “Constrained Optimization by Applying the α Constrained Method to the
Nonlinear Simplex Method With Mutations”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 5, pp.
437–451, October 2005.
253. Lauren Clevenger, Lauren Ferguson and William E. Hart, “Filter-Based Evolutionary Algorithm for Constrained Optimization”, Evolutionary Computation, Vol. 13, No. 3, pp. 329–352, Fall 2005.
254. S. Rajasekaran, “Optimal laminate sequence of non-prismatic thin-walled composite spatial members of generic section”,
Composite Structures, Vol. 70, No. 2, pp. 200-211, September 2005.
255. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
256. Sangameswar Venkatraman and Gary G. Yen, “A Generic Framework for Constrained Optimization Using Genetic
Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 4, August 2005
257. N.D. Lagaros, D.C. Charmpis and M. Papadrakakis, “An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization”, Computer Methods in Applied Mechanics and Engineering,
Vol. 194, Nos. 30–33, pp. 3374–3393, 2005.
258. J.H. Lee, G.H. Kim and Y.S. Park, “A geometry constraint handling technique for stiffener layout optimization problem”,
Journal of Sound and Vibration, Vol. 285, Nos. 1–2, pp. 101–120, July 6, 2005.
259. M. Andrea Rodrı́guez and Mary Carmen Jarur, “A Genetic Algorithm for Searching Spatial Configurations”, IEEE
Transactions on Evolutionary Computation, Vol. 9, No. 3, pp. 252–270, June 2005.
260. S. Rajasekaran, “Optimal mix for high performance concrete by evolution strategies combined with neural networks”,
Indian Journal of Engineering and Material Sciences, Vol. 13, No. 1, pp. 7–17, February 2006.
261. L.J. Li, Z.B. Huang, F. Liu and Q.H. Wu, “A heuristic particle swarm optimizer for optimization of pin connected
structures”, Computers & Structures, Vol. 85, Nos. 7–8, pp. 340–349, April 2007.
262. Jochen Till, Guido Sand, Maren Urselmann and Sebastian Engell, “A hybrid evolutionary algorithm for solving two-stage
stochastic integer programs in chemical batch scheduling”, Computers & Chemical Engineering, Vol. 31, Nos. 5–6, pp.
630–647, May-June 2007.
263. Saeed Parsa and Omid Bushehrian, “Genetic clustering with constraints”, Journal of Research and Practice in Information Technology, Vol. 39, No. 1, pp. 47–60, February 2007.
264. X. Blasco, M. Martinez, J.M. Herrero, C. Ramos and J. Sanchis, “Model-based predictive control of greenhouse climate
for reducing energy and water consumption”, Computers and Electronics in Agriculture, Vol. 55, No. 1, pp. 49–70,
January 2007.
265. E.S. Kameshki and M.P. Saka, “Optimum geometry design of nonlinear braced domes using genetic algorithm”, Computers & Structures, Vol. 85, Nos. 1–2, pp. 71–79, January 2007.
266. A.N. Martinez-Garcia and J. Anderson, “Carnico-ICSPEA2 - A metaheuristic co-evolutionary navigator for a complex
co-evolutionary farming system”, European Journal of Operational Research, Vol. 179, No. 3, pp. 634–655, June 16,
2007.
267. Qie He and Ling Wang, “An effective co-evolutionary particle swarm optimization for constrained engineering design
problems”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 1, pp. 89–99, February 2007.
268. Zixing Cai and Yong Wang, “A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 658–675, December 2006.
269. George G. Dimopoulos, “Mixed-variable engineering optimization based on evolutionary and social metaphors”, Computer Methods in Applied Mechanics and Engineering, Vol. 196, Nos. 4–6, pp. 803–817, 2007.
270. Haiyan Lu and Weiqi Chen, “Dynamic-objective particle swarm optimization for constrained optimization problems”,
Journal of Combinatorial Optimization, Vol. 12, No. 4, pp. 409–419, December 2006.
156
271. Philip Hingston, Luigi Barone, Simon Huband and Lyndon While, “Multi-level Ranking for Constrained Multi-objective
Evolutionary Optimisation”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós,
L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference,
pp. 563–572, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
272. Tetsuyuki Takahama, Setsuko Sakai and Noriyuki Iwane, “Constrained optimization by the constrained hybrid algorithm of particle swarm optimization and genetic algorithm”, in S. Zhang and R. Jarvis (editors), AI 2005: Advances
in Artificial Intelligence, Springer-Verlag, pp. 389–400, Lecture Notes in Artificial Intelligence Vol. 3809, 2005.
273. S. Sreeram, A.S. Kumar, M. Rahman and M.T. Zaman, “Optimization of cutting parameters in micro end milling operations in dry cutting condition using genetic algorithms”, International Journal of Advanced Manufacturing Technology,
Vol. 30, Nos. 11–12, pp. 1030–1039, October 2006.
274. D. Salazar, C.M. Rocco and B.J. Galvan, “Optimization of constrained multiple-objective reliability problems using
evolutionary algorithms”, Reliability Engineering & System Safety, Vol. 91, No. 9, pp. 1057–1070, September 2006.
275. A.C. Zecchin, A.R. Simpson, H.R. Maier, M. Leonard, A.J. Roberts and M.J. Berrisford, “Application of two ant colony
optimisation algorithms to water distribution system optimisation”, Mathematical and Computer Modelling, Vol. 44,
Nos. 5–6, pp. 451–468, September 2006.
276. A. Konak, D.W. Coit and A.E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial”, Reliability
Engineering & System Safety, Vol. 91, No. 9, pp. 992–1007, September 2006.
277. A. Amirjanov and K. Sobolev, “Fractal properties of Apollonian packing of spherical particles”, Modelling and Simulation
in Materials Science and Engineering, Vol. 14, No. 4, pp. 789–798, June 2006.
278. A.R. Hedar and M. Fukushima, “Derivative-free filter simulated annealing method for constrained continuous global
optimization”, Journal of Global Optimization, Vol. 35, No. 4, pp. 521–549, August 2006.
279. Ilya Tyapin and Geir Hovland, “Kinematic and Elastostatic Design Optimisation of the 3-DOF Gantry-Tau Parallel
Kinematic Manipulator”, Modeling Identification and Control, Vol. 30, No. 2, pp. 39–56, 2009.
280. Min Gan, Hui Peng, Xiaoyan Peng, Xiaohong Chen and Garba Inoussa, “An adaptive decision maker for constrained
evolutionary optimization”, Applied Mathematics and Computation, Vol. 215, No. 12, pp. 4172–4184, February 15,
2010.
281. C.Y. Chung, C.H. Liang, K.P. Wong and X.Z. Duan, “Hybrid algorithm of differential evolution and evolutionary
programming for optimal reactive power flow”, IET Generation Transmission & Distribution, Vol. 4, No. 1, pp. 84–93,
January 2010.
282. Adil Amirjanov, “The dynamics of a changing range genetic algorithm”, International Journal for Numerical Methods
in Engineering, Vol. 81, No. 7, pp. 892–909, February 12, 2010.
283. M.A. Valdebenito, H.J. Pradlwarter and G.I. Schueller, “The role of the design point for calculating failure probabilities
in view of dimensionality and structural nonlinearities”, Structural Safety, Vol. 32, No. 2, pp. 101–111, 2010.
284. Francisco J. Martinez, Fernando Gonzalez-Vidosa, Antonio Hospitaler and Victor Yepes, “Heuristic optimization of RC
bridge piers with rectangular hollow sections”, Computers & Structures, Vol. 88, Nos. 5-6, pp. 375–386, March 2010.
285. A. Kaveh, B. Farahmand Azar, A. Hadidi, F. Rezazadeh Sorochi and S. Talatahari, “Performance-based seismic design
of steel frames using ant colony optimization”, Journal of Constructional Steel Research, Vol. 66, No. 4, pp. 566–574,
April 2010.
286. Souma Chowdhury and George S. Dulikravich, “Improvements to single-objective constrained predator-prey evolutionary
optimization algorithm”, Structural and Multidisciplinary Optimization, Vol. 41, No. 4, pp. 541–554, April 2010.
287. A. Rama Mohan Rao and P.P. Shyju, “A Meta-Heuristic Algorithm for Multi-Objective Optimal Design of Hybrid
Laminate Composite Structures”, Computer-Aided Civil and Infrastructure Engineering, Vol. 25, No. 3, pp. 149–170,
April 2010.
288. Quan Yuan and Feng Qian, “A hybrid genetic algorithm for twice continuously differentiable NLP problems”, Computers
& Chemical Engineering, Vol. 34, No. 1, pp. 36–41, January 11, 2010.
289. Manuel Barros, Jorge Guilherme and Nuno Horta, “Analog circuits optimization based on evolutionary computation
techniques”, Integration–The VLSI Journal, Vol. 43, No. 1, pp. 136–155, January 2010.
290. Lixin Tang and Ping Yan, “Particle Swarm Optimization Algorithm for a Batching Problem in the Process Industry”,
Industrial & Engineering Chemistry Research, Vol. 48, No. 20, pp. 9186–9194, October 21, 2009.
291. Ricardo Perera and Francisco B. Varona, “Flexural and Shear Design of FRP Plated RC Structures Using a Genetic
Algorithm”, Journal of Structural Engineering–ASCE, Vol. 135, No. 11, pp. 1418–1429, November 2009.
292. S. Rajasekaran and J. Sakthi Chitra, “Ant colony optimisation of spatial steel structures under static and earthquake
loading”, Civil Engineering and Environmental Systems, Vol. 26, No. 4, pp. 339–354, 2009.
293. Nizar Bel Hadj Ali, Mohamed Sellami, Anne-Francoise Cutting-Decelle and Jean-Claude Mangin, “Multi-stage production cost optimization of semi-rigid steel frames using genetic algorithms”, Engineering Structures, Vol. 31, No. 11, pp.
2766–2778, November 2009.
157
294. Hui Liu, Zixing Cai and Yong Wang, “Hybridizing particle swarm optimization with differential evolution for constrained
numerical and engineering optimization”, Applied Soft Computing, Vol. 10, No. 2, pp. 629–640, March 2010.
295. G. Venter and R.T. Haftka, “Constrained particle swarm optimization using a bi-objective formulation”, Structural and
Multidisciplinary Optimization, Vol. 40, Nos. 1-6, pp. 65–76, January 2010.
296. Leandro dos Santos Coelho, “Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1676–1683, March 2010.
297. Pudjo Sukarno, Deni Saepudin, Silvya Dewi, Edy Soewono, Kuntjoro Adji Sidarto and Agus Yodi Gunawan, “Optimization of Gas Injection Allocation in a Dual Gas Lift Well System”, Journal of Energy Resources Technology–Transactions
of the ASME, Vol. 131, No. 3, Article number 033101, September 2009.
298. Adil Amirjanov, “The Dynamics of a Changing Range Genetic Algorithm under Stabilizing Selection”, International
Journal of Modern Physics C, Vol. 20, No. 7, pp. 1063–1079, July 2009.
299. Li-Chiu Chang and Fi-John Chang, “Multi-objective evolutionary algorithm for operating parallel reservoir system”,
Journal of Hydrology, Vol. 377, Nos. 1-2, pp. 12–20, October 20, 2009.
300. G. Sand, J. Till, T. Tometzki, M. Urselmann, S. Engell and M. Emmerich, “Engineered versus standard evolutionary
algorithms: A case study in batch scheduling with recourse”, Computers & Chemical Engineering, Vol. 32, No. 11, pp.
2706–2722, November 24, 2008.
301. Leihong Li, Vitali V. Volovoi and Dewey H. Hodges, “Cross-sectional design of composite rotor blades”, Journal of the
American Helicopter Society, Vol. 53, No. 3, pp. 240–251, July 2008.
302. M.M. Ali and Z. Kajee-Bagdadi, “A local exploration-based differential evolution algorithm for constrained global optimization”, Applied Mathematics and Computation, Vol. 208, No. 1, pp. 31–48, February 1, 2009.
303. Min Wook Kang, Paul Schonfeld and Ning Yang, “Prescreening and Repairing in a Genetic Algorithm for Highway
Alignment Optimization”, Computer-Aided Civil and Infrastructure Engineering, Vol. 24, No. 2, pp. 109–119, 2009.
304. O. Feyzioglu and H. Pierreval, “Hybrid organization of functional departments and manufacturing cells in the presence
of imprecise data”, International Journal of Production Research, Vol. 47, No. 2, pp. 343–368, 2009.
305. Leonaldo Badia, Alessio Botta and Luciano Lenzin, “A genetic approach to joint routing and link scheduling for wireless
mesh networks”, Ad Hoc Networks, Vol. 7, No. 4, pp. 654–664, June 2009.
306. Ke Tang, Yi Mei and Xin Yao, “Memetic Algorithm With Extended Neighborhood Search for Capacitated Arc Routing
Problems”, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, pp. 1151–1166, October 2009.
307. Yuanping Gu, Xianbin Cao and Jun Zhang, “Constraint Handling Based Multiobjective Evolutionary Algorithm for
Aircraft Landing Scheduling”, International Journal of Innovative Computing Information and Control, Vol. 5, No. 8,
pp. 2229–2238, August 2009.
308. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique for
constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,
pp. 1501–1534, March 2009.
309. Yibo Hu, “Hybrid-Fitness Function Evolutionary Algorithm Based on Simplex Crossover and PSO Mutation for Constrained Optimization Problems”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 23, No.
1, pp. 115–127, February 2009.
310. W. Paszkowicz, “Properties of a genetic algorithm equipped with a dynamic penalty function”, Computational Materials
Science, Vol. 45, No. 1, pp. 77–83, March 2009.
311. Yi Mei, Ke Tang and Xin Yao, “A Global Repair Operator for Capacitated Arc Routing Problem”, IEEE Transactions
on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 39, No. 3, pp. 723–734, June 2009.
312. Jose A. Egea, Eva Balsa-Canto, Maria Sonia G. Garcia and Julio R. Banga, “Dynamic Optimization of Nonlinear
Processes with an Enhanced Scatter Search Method”, Industrial and Engineering Chemistry Research, Vol. 48, No. 9,
pp. 4388–4401, May 6, 2009.
313. Pieterjan Demarcke, Hendrik Rogier, Roald Goossens and Peter De Jaeger, “Beamforming in the Presence of Mutual
Coupling Based on Constrained Particle Swarm Optimization”, IEEE Transactions on Antennas and Propagation, Vol.
57, No. 6, pp. 1655–1666, June 2009.
314. Ricardo Perera and Javier Vique, “Strut-and-tie modelling of reinforced concrete beams using genetic algorithms optimization”, Construction and Building Materials, Vol. 23, No. 8, pp. 2914–2925, August 2009.
315. M. Fesanghary and M.M. Ardehali, “A novel meta-heuristic optimization methodology for solving various types of
economic dispatch problem”, Energy, Vol. 34, No. 6, pp. 757–766, June 2009.
316. A. Kaveh and S. Talatahari, “A particle swarm ant colony optimization for truss structures with discrete variables”,
Journal of Constructional Steel Research, Vol. 65, Nos. 8–9, pp. 1558–1568, August-September 2009.
317. Rosario Toscano and Patrick Lyonnet, “Heuristic Kalman Algorithm for Solving Optimization Problems”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 39, No. 5, pp. 1231–1244, October 2009.
158
318. Adil Amirjanov, “The Performance of Genetic Algorithm with Adjustment of a Search Space”, International Journal of
Modern Physics C, Vol. 20, No. 4, pp. 565–583, April 2009.
319. Tetsuyuki Takahama and Setsuko Sakai, “Fast and Stable Constrained Optimization by the −constrained Differential
Evolution”, Pacific Journal of Optimization, Vol. 5, No. 2, pp. 261–282, May 2009.
320. O. Hasancebi, S. Carbas, E. Dogan, F. Erdal and M.P. Saka, “Performance evaluation of metaheuristic search techniques
in the optimum design of real size pin jointed structures”, Computers & Structures, Vol. 87, Nos. 5-6, pp. 284–302,
March 2009.
321. N.R. Srinivasa Raghavan and M. Venkataramana, “Parallel processor scheduling for minimizing total weighted tardiness
using ant colony optimization”, International Journal of Advanced Manufacturing Technology, Vol. 41, Nos. 9–10, pp.
986–996, April 2009.
322. C.M. Chan, L.M. Zhang and Jenny T.M. Ng, “Optimization of Pile Groups Using Hybrid Genetic Algorithms”, Journal
of Geotechnical and Geoenvironmental Engineering, Vol. 134, No. 4, pp. 497–505, April 2009.
323. Abu S. S. M. Barkat Ullah, Ruhul Sarker, David Cornforth and Chris Lokan, “AMA: a new approach for solving
constrained real-valued optimization problems”, Soft Computing, Vol. 13, Nos. 8-9, pp. 741–762, July 2009.
324. Igor V. Maslov and Izidor Gertner, “Multi-sensor fusion: an Evolutionary algorithm approach”, Information Fusion,
Vol. 7, No. 3, pp. 304–330, September 2006.
325. Javier Causa, Gorazd Karer, Alfredo Nunez, Doris Saez, Igor Skrjanc and Borut Zupancic, “Hybrid fuzzy predictive
control based on genetic algorithms for the temperature control of a batch reactor”, Computers & Chemical Engineering,
Vol. 32, No. 12, pp. 3254–3263, December 22, 2008.
326. Biruk Tessema and Gary G. Yen, “An Adaptive Penalty Formulation for Constrained Evolutionary Optimization”, IEEE
Transactions on Systems, Man, and Cybernetics Part A—Systems and Humans, Vol. 39, No. 3, pp. 565–578, May 2009.
327. K. Vijayalakshmi and S. Radhakrishnan, “Artificial immune based hybrid GA for QoS based multicast routing in large
scale networks (AISMR)”, Computer Communications, Vol. 31, No. 17, pp. 3984–3994, November 20, 2008.
• Efrén Mezura Montes and Carlos A. Coello Coello, “A Simple Multi-Membered Evolution Strategy to Solve
Constrained Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 1, pp.
1–17, February 2005.
1. Haipeng Kong, Li Ni and Yuzhong Shen, “Adaptive double chain quantum genetic algorithm for constrained optimization
problems”, Chinese Journal of Aeronautics, Vol. 28, No. 1, pp. 214–228, February 2015.
2. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics inspired optimization
(OIO)”, Computer-Aided Design, Vol. 63, pp. 52–71, June 2015.
3. Cui Chenggang, Yang Xiaofei and Gao Tingyu, “A Self-adaptive Interior Penalty Based Differential Evolution Algorithm
for Constrained Optimization”, in Ying Tan, Yuhui Shi and Carlos A. Coello Coello (editors), Advances in Swarm
Intelligence, 5th International Conference, ICSI 2014, pp. 309–318, Springer. Lecture Notes in Computer Science Vol.
8795, Hefei, China, October 17-20, 2014, ISBN 978-3-319-11896-3.
4. Chengyong Si, Jing An, Tian Lan, Thomas Ussmuller, Lei Wang and Qidi Wu, “On the equality constraints tolerance
of Constrained Optimization Problems”, Theoretical Computer Science, Vol. 551, pp. 55–65, September 25, 2014.
5. Rommel G. Regis, “Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization
Using Radial Basis Functions”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 326–347, June
2014.
6. Milan Tuba and Nebojsa Bacanin, “Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems”, Neurocomputing, Vol. 143, pp. 197–207, November 2, 2014.
7. Ivona Brajevic and Milan Tuba, “An upgraded artificial bee colony (ABC) algorithm for constrained optimization
problems”, Journal of Intelligent Manufacturing, Vol. 24, No. 4, pp. 729–740, August 2013.
8. Hong Li and Li Zhang, “A discrete hybrid differential evolution algorithm for solving integer programming problems”,
Engineering Optimization, Vol. 46, No. 9, pp. 1238–1268, September 2, 2014.
9. Manoj Kumar Dhadwal, Sung Nam Jung and Chang Joo Kim, “Advanced particle swarm assisted genetic algorithm for
constrained optimization problems”, Computational Optimization and Applications, Vol. 58, No. 3, pp. 781–806, July
2014.
10. Rommel G. Regis, “Constrained optimization by radial basis function interpolation for high-dimensional expensive blackbox problems with infeasible initial points”, Engineering Optimization, Vol. 46, No. 2, pp. 218–243, February 1, 2014.
11. Gexiang Zhang, Jixiang Cheng, Marian Gheorghe and Qi Meng, “A hybrid approach based on differential evolution
and tissue membrane systems for solving constrained manufacturing parameter optimization problems”, Applied Soft
Computing, Vol. 13, No. 3, pp. 1528–1542, March 2013.
159
12. Paul Pitiot, Michel Aldanondo and Elise Vareilles, “Concurrent product configuration and process planning: Some
optimization experimental results”, Computers in Industry, Vol. 65, No. 4, pp. 610–621, May 2014.
13. Mazdak Shokrian and Karen Ann High, “Application of a multi objective multi-leader particle swarm optimization
algorithm on NLP and MINLP problems”, Computers & Chemical Engineering, Vol. 60, pp. 57–75, January 10, 2014.
14. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Engineering optimization by means of an improved constrained differential evolution”, Computer Methods in Applied Mechanics and Engineering, Vol. 268, pp. 884–904, January 1,
2014.
15. Xiangtao Li and Minghao Yin, “Self-adaptive constrained artificial bee colony for constrained numerical optimization”,
Neural Computing & Applications, Vol. 24, Nos. 3-4, pp. 723–734, March 2014.
16. Wen Long, Ximing Liang, Yafei Huang and Yixiong Chen, “A hybrid differential evolution augmented Lagrangian
method for constrained numerical and engineering optimization”, Computer-Aided Design, Vol. 45, No. 12, pp. 1562–
1574, December 2013.
17. Xiangtong Kong, Haibin Ouyang and Xiaoxue Piao, “A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization”, Soft Computing, Vol. 17, No. 12, pp. 2293–2309, December 2013.
18. Ilhem Boussaid, Amitava Chatterjee, Patrick Siarry and Mohamed Ahmed-Nacer, “ Biogeography-based optimization
for constrained optimization problems”, Computers & Operations Research, Vol. 39, No. 12, pp. 3293–3304, December
2012.
19. Dervis Karaboga and Bahriye Akay, “A modified Artificial Bee Colony (ABC) algorithm for constrained optimization
problems”, Applied Soft Computing, Vol. 11, No. 3, pp. 3021–3031, April 2011.
20. Sanyou Zeng, Yang Yang, Yulong Shi, Xianqiang Yang, Bo Xiao, Song Gao, Danping Yu and Zu Yan, “A micro niche evolutionary algorithm with lower-dimensional-search crossover for optimisation problems with constraints”, International
Journal of Bio-Inspired Computation, Vol. 1, No. 3, pp. 177–185, 2009.
21. Miguel G. Villarreal-Cervantes, Carlos A. Cruz-Villar, Jaime Alvarez-Gallegos and Edgar A. Portilla-Flores, “Robust
Structure-Control Design Approach for Mechatronic Systems”, IEEE-ASME Transactions on Mechatronics, Vol. 18,
No. 5, pp. 1592–1601, October 2013.
22. Issam Mazhoud, Khaled Hadj-Hamou, Jean Bigeon and Patrice Joyeux, “Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism”, Engineering Applications of Artificial Intelligence, Vol. 26,
No. 4, pp. 1263–1273, April 2013.
23. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
24. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
25. LiCheng Jiao, Lin Li, RongHua Shang, Fang Liu and Rustam Stolkin, “A novel selection evolutionary strategy for
constrained optimization”, Information Sciences, Vol. 239, pp. 122–141, August 1, 2013.
26. Trung Thanh Nguyen and Xin Yao, “Continuous Dynamic Constrained Optimization—The Challenges”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 6, pp. 769–786, December 2012.
27. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “Self-adaptive differential evolution incorporating a heuristic
mixing of operators”, Computational Optimization and Applications, Vol. 54, No. 3, pp. 771–790, April 2013.
28. M.M. Ali and W.X. Zhu, “A penalty function-based differential evolution algorithm for constrained global optimization”,
Computational Optimization and Applications, Vol. 54, No. 3, pp. 707–739, April 2013.
29. Guanbo Jia, Yong Wang, Zixing Cai and Yaochu Jin, “An improved (µ + λ)-constrained differential evolution for
constrained optimization”, Information Sciences, Vol. 222, pp. 302–322, February 10, 2013.
30. Bahriye Akay and Dervis Karaboga, “Artificial bee colony algorithm for large-scale problems and engineering design
optimization”, Journal of Intelligent Manufacturing, Vol. 23, No. 4, pp. 1001–1014, August 2012.
31. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “An Improved Self-Adaptive Differential Evolution Algorithm
for Optimization Problems”, IEEE Transactions on Industrial Informatics, Vol. 9, No. 1, pp. 89–99, February 2013.
32. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
33. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “On an evolutionary approach for constrained optimization
problem solving”, Applied Soft Computing, Vol. 12, No. 10, pp. 3208–3227, October 2012.
34. Matej Crepinsek, Shih-Hsi Liu and Luka Mernik, “A note on teaching-learning-based optimization algorithm”, Information Sciences, Vol. 212, pp. 79–93, December 1, 2012.
160
35. Layak Ali, Samrat L. Sabat and Siba K. Udgata, “Particle swarm optimisation with stochastic ranking for constrained
numerical and engineering benchmark problems”, International Journal of Bio-Inspired Computation, Vol. 4, No. 3, pp.
155–166, 2012.
36. Miin-Tsair Su, Chin-Teng Lin, Sheng-Chih Hsu, Dong-Lin Li, Cheng-Jiang Lin and Cheng-Hung Chen, “Nonlinear
System Control Using Functional-Link-Based Neuro-Fuzzy Network Model Embedded with Modified Particle Swarm
Optimizer”, International Journal of Fuzzy Systems, Vol. 14, No. 1, pp. 97–109, March 2012.
37. Nebojsa Bacanin and Milan Tuba, “Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved
with Genetic Operators”, Studies in Informatics and Control, Vol. 21, No. 2, pp. 137–146, June 2012.
38. Ali Wagdy Mohamed and Hegazy Zaher Sabry, “Constrained optimization based on modified differential evolution
algorithm”, Information Sciences, Vol. 194, pp. 171–208, July 1, 2012.
39. Xiangtao Hu, Yong’an Huang, Zhouping Yin and Youlun Xiong, “Optimization-based model of tunneling-induced distributed loads acting on the shield periphery”, Automation in Construction, Vol. 24, pp. 138–148, July 2012.
40. Sanghoun Oh, Chang Wook Ahn and Moongu Jeon, “Effective Constraints Based Evolutionary Algorithm for Constrained Optimization Problems”, International Journal of Innovative Computing Information and Control, Vol. 8, No.
6, pp. 3997–4014, June 2012.
41. Jia-qing Zhao, Ling Wang, Pan Zeng and Wen-hui Fan, “An effective hybrid genetic algorithm with flexible allowance
technique for constrained engineering design optimization”, Expert Systems with Applications, Vol. 39, No. 5, pp.
6041–6051, April 2012.
42. Amir Hossein Gandomi, Xin-She Yang, Siamak Talatahari and Suash Deb, “Coupled eagle strategy and differential
evolution for unconstrained and constrained global optimization”, Computers & Mathematics with Applications, Vol.
63, No. 1, pp. 191–200, January 2012.
43. Yong Wang and Zixing Cai, “Combining Multiobjective Optimization with Differential Evolution to Solve Constrained
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 117–134, February
2012.
44. Yong Wang and Zixing Cai, “A hybrid multi-swarm particle swarm optimization to solve constrained optimization
problems”, Frontiers of Computer Science in China, Vol. 3, No. 1, pp. 38–52, March 2009.
45. Ali Husseinzadeh Kashan, “An efficient algorithm for constrained global optimization and application to mechanical
engineering design: League championship algorithm (LCA)”, Computer-Aided Design, Vol. 43, No. 12, pp. 1769–1792,
December 2011.
46. Eduardo G. Carrano, Elizabeth F. Wanner and Ricardo H.C. Takahashi, “A Multicriteria Statistical Based Comparison
Methodology for Evaluating Evolutionary Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 15, No.
6, pp. 848–870, December 2011.
47. Sanghoun Oh, Yaochu Jin and Moongu Jeon, “Approximate Models for Constraint Functions in Evolutionary Constrained Optimization”, International Journal of Innovative Computing Information and Control, Vol. 7, No. 11, pp.
6585–6603, November 2011.
48. Alexandre Morin, Per Eilif Wahl and Mona Molnvik, “Using evolutionary search to optimise the energy consumption for
natural gas liquefaction”, Chemical Engineering Research & Design, Vol. 89, No. 11A, pp. 2428–2441, November 2011.
49. Felipe Alexander Vargas Bazan, Edison Castro Patres de Lima, Marcos Queija de Siqueira, Elizabeth Frauches Netto
Siqueira and Carlos Alberto Duarte de Lemos, “A methodology for structural analysis and optimization of riser connection joints ”, Applied Ocean Research, Vol. 33, No. 4, pp. 344–365, October 2011.
50. Jianyong Chen, Qiuzhen Lin and LinLin Shen, “An Immune-Inspired Evolution Strategy for Constrained Optimization
Problems”, International Journal on Artificial Intelligence Tools, Vol. 20, No. 3, pp. 549–561, June 2011.
51. Gianni Ciofani, Pier Nicola Sergi, Jacopo Carpaneto and Silvestre Micera, “A hybrid approach for the control of axonal
outgrowth: preliminary simulation results”, Medical & Biological Engineering & Computing, Vol. 49, No. 2, pp. 163–170,
February 2011.
52. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “Multi-operator based evolutionary algorithms for solving
constrained optimization problems”, Computers & Operations Research, Vol. 38, No. 12, pp. 1877–1896, December
2011.
53. R.V. Rao, V.J. Savsani and D.P. Vakharia, “Teaching-learning-based optimization: A novel method for constrained
mechanical design optimization problems”. Computer-Aided Design, Vol. 43, No. 3, pp. 303–315, March 2011.
54. Yong Wang and Zixing Cai, “Constrained Evolutionary Optimization by Means of (µ + λ)-Differential Evolution and
Improved Adaptive Trade-Off Model”, Evolutionary Computation, Vol. 19, No. 2, 249–285, Summer 2011.
55. Zhenxiao Gao, Tianyuan Xiao and Wenhui Fan, “Hybrid differential evolution and Nelder-Mead algorithm with reoptimization”, Soft Computing, Vol. 15, No. 3, pp. 581–594, March 2011.
56. Haiping Ma and Dan Simon, “Blended biogeography-based optimization for constrained optimization”, Engineering
Applications of Artificial Intelligence, Vol. 24, No. 3, pp. 517–525, April 2011.
161
57. Hong Li, Yong-Chang Jiao and Li Zhang, “Hybrid differential evolution with a simplified quadratic approximation for
constrained optimization problems”, Engineering Optimization, Vol. 43, No. 2, pp. 115–134, 2011.
58. Ling Wang and Ling-Po Li, “Fixed-Structure H-infinity Controller Synthesis Based on Differential Evolution with Level
Comparison”, IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 120–129, February 2011.
59. Rammohan Mallipeddi and Ponnuthurai N. Suganthan, “Ensemble of Constraint Handling Techniques”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 4, pp. 561–579, August 2010.
60. Stephanus Daniel Handoko, Chee Keong Kwoh and Yew-Soon Ong, “Feasibility Structure Modeling: An Effective
Chaperone for Constrained Memetic Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 5,
pp. 740–758, October 2010.
61. Sung Soo Kim, Il-Hwan Kim, V. Mani and Hyung Jun Kim, “Real-coded genetic algorithm for machining condition
optimization”, International Journal of Advanced Manufacturing Technology, Vol. 38, No. 9-10, pp. 884–895, September
2008.
62. Wenxing Zhu and M.M. Ali, “Solving nonlinearly constrained global optimization problem via an auxiliary function
method”, Journal of Computational and Applied Mathematics, Vol. 230, No. 2, pp. 491–503, August 15, 2009.
63. Guo-liang Mo and Ming-hua Wu, “Designing Bezier surfaces minimizing the L-2-norm of the Gaussian curvature”,
Journal of the Zhejiang University–Science A, Vol. 8, No. 1, pp. 142–148, January 2007.
64. Soorathep Kheawhom, “Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical
engineering optimization problem”, Journal of Industrial and Engineering Chemistry, Vol. 16, No. 4, pp. 620–628, July
25, 2010.
65. Qiaoling Wang, Xiao-Zhi Gao and Changhong Wang, “An Adaptive Bacterial Foraging Algorithm for Constrained
Optimization”, International Journal of Innovative Computing Information and Control, Vol. 6, No. 8, pp. 3585–3593,
August 2010.
66. Ling Wang and Ling-po Li, “An effective differential evolution with level comparison for constrained engineering design”,
Structural and Multidisciplinary Optimization, Vol. 41, No. 6, pp. 947–963, June 2010.
67. Cheng-gang Cui, Yan-jun Li and Tie-jun Wu, “A relative feasibility degree based approach for constrained optimization
problems”, Journal of Zhejiang University–Science C–Computers & Electronics, Vol. 11, No. 4, pp. 249–260, April
2010.
68. Jinhua Wang and Zeyong Yin, “A ranking selection-based particle swarm optimizer for engineering design optimization
problems”, Structural and Multidisciplinary Optimization, Vol. 37, No. 2, pp. 131–147, December 2008.
69. Dan Simon, “Biogeography-Based Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, pp.
702–713, December 2008.
70. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
71. Sushil Kumar and R. Naresh, “Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling
problem”, International Journal of Electrical Power & Energy Systems, Vol. 29, No. 10, pp. 738–747, December 2007.
72. Ehab Z. Elfeky, Ruhul A. Sarker and Daryl L. Essam, “Analyzing the simple ranking and selection process for constrained
evolutionary optimization”, Journal of Computer Science and Technology, Vol. 23, No. 1, pp. 19–34, January 2008.
73. Min Zhang, Wenjian Luo and Xufa Wang, “Differential evolution with dynamic stochastic selection for constrained
optimization”, Information Sciences, Vol. 178, No. 15, pp. 3043–3074, August 1, 2008.
74. Yong Wang, Zixing Cai, Yuren Zhou and Wei Zeng, “An Adaptive Tradeoff Model for Constrained Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 80–92, February 2008.
75. Elizabeth F. Wanner, Ricardo H.C. Takahashi, Frederico G. Guimaraes and Jaime A. Ramirez, “Hybrid genetic algorithms using quadratic local search operators”, COMPEL-The International Journal for Computation and Mathematics
in Electrical and Electronic Engineering, Vol. 26, No. 3, pp. 773–787, 2007.
76. Yong Wang, Hui Liu, Zixing Cai and Yuren Zhou, “An orthogonal design based constrained evolutionary optimization
algorithm”, Engineering Optimization, Vol. 39, No. 6, pp. 715–736, September 2007.
77. Pei Yee Ho and Kazuyuki Shimizu, “Evolutionary constrained optimization using an addition of ranking method and a
percentage-based tolerance value adjustment scheme”, Information Sciences, Vol. 177, No. 14, pp. 2985–3004, July 15,
2007.
78. Yong Wang, Zixing Cai, Guanqi Guo and Yuren Zhou, “Multiobjective optimization and hybrid evolutionary algorithm
to solve constrained optimization problems”, IEEE Transactions on Systems, Man and Cybernetics Part B–Cybernetics,
Vol. 37, No. 3, pp. 560–575, June 2007.
79. Felipe Campelo, So Noguchi and Hajime Igarashi, “A new method for the robust design of high field, highly homogenous
superconducting magnets using an immune algorithm”, IEEE Transactions on Applied Applied Superconductivity, Vol.
16, No. 2, pp. 1316–1319, June 2006.
162
80. Yuanpng Guo, Xianbin Cao, Hongzhang Yin and Zeying Tang, “Coevolutionary optimization algorithm with dynamic
sub-population size”, International Journal of Innovative Computing Information and Control, Vol. 2, No. 2, pp.
435–448, April 2007.
81. Yiqing Luo, Xigang Yuan and Yongjian Liu, “An improved PSO algorithm for solving non-convex NLP/MINLP problems
with equality constraints”, Computers & Chemical Engineering, Vol. 31, No. 3, pp. 153–162, January 29, 2007.
82. Ehab Z. Elfeky, Ruhul A. Sarker and Daryl L. Essam, “A simple ranking and selection for constrained evolutionary
optimization”, in Tzai-Der Wang, Xiaodong Li, Shu-Heng Chen, Xufa Wang, Hussein Abbass, Hitoshi Iba, Guoliang
Chen and Xin Yao (editors), Simulated Evolution and Learning, 6th International Conference, SEAL 2006, pp. 537–544,
Springer. Lecture Notes in Computer Science Vol. 4247, Hefei, China, October 2006.
83. Zixing Cai and Yong Wang, “A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 658–675, December 2006.
84. Philip Hingston, Luigi Barone, Simon Huband and Lyndon While, “Multi-level Ranking for Constrained Multi-objective
Evolutionary Optimisation”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós,
L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference,
pp. 563–572, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
85. Felipe Campelo, Frederico G. Guimãraes, Hajime Igarashi, Jaime A. Ramirez and So Noguchi, “A modified immune
network algorithm for multimodal electromagnetic problems”, IEEE Transactions on Magnetics, Vol. 42, No. 4, pp.
1111–1114, April 2006.
86. Hong Li, Yong-Chang Jiao and Yuping Wang, “Integrating the Simplified Interpolation into the Genetic Algorithm for
Constrained Optimization Problems”, in Yue Hao et al. (editors), Computational Intelligence and Security. International
Conference, CIS 2005, pp. 247–254, Springer, Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December
2005.
87. Yuping Wang, Dalian Liu, and Yiu-Ming Cheung, “Preference Bi-objective Evolutionary Algorithm for Constrained
Optimization”, in Yue Hao et al. (editors), Computational Intelligence and Security. International Conference, CIS
2005, pp. 184–191, Springer, Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December 2005.
88. J. von Berg and C. Lorenz, “A geometric model of the beating heart”, Methods of Information in Medicine, Vol. 46,
No. 3, pp. 282–286, 2007.
89. Jing Liu and Weicai Zhong, “Constrained Optimization Using Organizational Evolutionary Algorithm”, in Tzai-Der
Wang, Xiaodong Li, Shu-Heng Chen, Xufa Wang, Hussein Abbass, Hitoshi Iba, Guoliang Chen and Xin Yao (editors),
Simulated Evolution and Learning, 6th International Conference, SEAL 2006, pp. 302–309, Springer. Lecture Notes in
Computer Science Vol. 4247, Hefei, China, October 2006.
90. Jing Liu, Weicai Zhong and Licheng Hao, “An organizational evolutionary algorithm for numerical optimization”, IEEE
Transactions on Systems, Man and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 1052–1064, August 2007.
91. Fu-zhuo Huang, Ling Wang and Qie He, “An effective co-evolutionary differential evolution for constrained optimization”,
Applied Mathematics and Computation, Vol. 186, No. 1, pp. 340–356, March 1, 2007.
92. A.R. Yildiz and F. Ozturk, “Hybrid enhanced genetic algorithm to select optimal machining parameters in turning
operation”, Proceedings of the Institution of Mechanical Engineers Part B–Journal of Engineering Manufacture, Vol.
220, No. 12, pp. 2041–2053, December 2006.
93. Qie He and Ling Wang, “An effective co-evolutionary particle swarm optimization for constrained engineering design
problems”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 1, pp. 89–99, February 2007.
94. A.R. Hedar and M. Fukushima, “Derivative-free filter simulated annealing method for constrained continuous global
optimization”, Journal of Global Optimization, Vol. 35, No. 4, pp. 521–549, August 2006.
95. Min Gan, Hui Peng, Xiaoyan Peng, Xiaohong Chen and Garba Inoussa, “An adaptive decision maker for constrained
evolutionary optimization”, Applied Mathematics and Computation, Vol. 215, No. 12, pp. 4172–4184, February 15,
2010.
96. K.P. Anagnostopoulos and G. Mamanis, “A portfolio optimization model with three objectives and discrete variables”,
Computers & Operations Research, Vol. 37, No. 7, pp. 1285–1297, July 2010.
97. Hui Liu, Zixing Cai and Yong Wang, “Hybridizing particle swarm optimization with differential evolution for constrained
numerical and engineering optimization”, Applied Soft Computing, Vol. 10, No. 2, pp. 629–640, March 2010.
98. Dong Xie, Zhe Luo and Fan Yu, “The computing of the optimal power consumption for semi-track air-cushion vehicle
using hybrid generalized extremal optimization”, Applied Mathematical Modelling, Vol. 33, No. 6, pp. 2831–2844, June
2009.
99. Xiaoli Kou, Sanyang Liu, Jianke Zhang and Wei Zheng, “Co-evolutionary particle swarm optimization to solve constrained optimization problems”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 1776–1784,
June 2009.
163
100. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique for
constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,
pp. 1501–1534, March 2009.
101. Yibo Hu, “Hybrid-Fitness Function Evolutionary Algorithm Based on Simplex Crossover and PSO Mutation for Constrained Optimization Problems”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 23, No.
1, pp. 115–127, February 2009.
102. Pieterjan Demarcke, Hendrik Rogier, Roald Goossens and Peter De Jaeger, “Beamforming in the Presence of Mutual
Coupling Based on Constrained Particle Swarm Optimization”, IEEE Transactions on Antennas and Propagation, Vol.
57, No. 6, pp. 1655–1666, June 2009.
103. Tetsuyuki Takahama and Setsuko Sakai, “Fast and Stable Constrained Optimization by the −constrained Differential
Evolution”, Pacific Journal of Optimization, Vol. 5, No. 2, pp. 261–282, May 2009.
104. Biruk Tessema and Gary G. Yen, “An Adaptive Penalty Formulation for Constrained Evolutionary Optimization”, IEEE
Transactions on Systems, Man, and Cybernetics Part A—Systems and Humans, Vol. 39, No. 3, pp. 565–578, May 2009.
• Carlos A. Coello Coello and Nareli Cruz Cortés, “Solving Multiobjective Optimization Problems using an
Artificial Immune System”, Genetic Programming and Evolvable Machines, Vol. 6, No. 2, pp. 163–190,
June 2005.
1. Xiaoguang He, Cai Dai and Zehua Chen, “Many-Objective Optimization Using Adaptive Differential Evolution with a
New Ranking Method”, Mathematical Problems in Engineering, Article Number: 259473, 2014.
2. Cai Dai, Yuping Wang and Miao Ye, “A new evolutionary algorithm based on contraction method for many-objective
optimization problems”, Applied Mathematics and Computation, Vol. 245, pp. 191–205, October 15, 2014.
3. Yuan Yuan and Hua Xu, “Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms”, IEEE Transactions
on Automation Science and Engineering, Vol. 12, No. 1, pp. 336–353, January 2015.
4. Kangning Huang, Xiaoping Liu, Xia Li, Jiayong Liang and Shenjing He, “An improved artificial immune system for
seeking the Pareto front of land-use allocation problem in large areas”, International Journal of Geographical Information
Science, Vol. 27, No. 5, pp. 922–946, May 1, 2013.
5. Ullah Saif, Zailin Guan, Weiqi Liu, Chaoyong Zhang and Baoxi Wang, “Pareto based artificial bee colony algorithm for
multi objective single model assembly line balancing with uncertain task times”, Computers & Industrial Engineering,
Vol. 76, pp. 1–15, October 2014.
6. Hongbin Pu, Da-Wen Sun, Ji Ma, Dan Liu and Mohammed Kamruzzaman, “Hierarchical variable selection for predicting
chemical constituents in lamb meats using hyperspectral imaging”, Journal of Food Engineering, Vol. 143, pp. 44–52,
December 2014.
7. Hossein Karshenas, Roberto Santana, Concha Bielza and Pedro Larrañaga, “Multiobjective Estimation of Distribution
Algorithm Based on Joint Modeling of Objectives and Variables”, IEEE Transactions on Evolutionary Computation,
Vol. 18, No. 4, pp. 519–542, August 2014.
8. Ruochen Liu, Chenlin Ma, Fei He, Wenping Ma and Licheng Jiao, “Reference direction based immune clone algorithm
for many-objective optimization”, Frontiers of Computer Science, Vol. 8, No. 4, pp. 642–655, August 2014.
9. A.A. Mousa and E.E. Elattar, “Best Compromise Alternative to EELD Problem using Hybrid Multiobjective Quantum
Genetic Algorithm”, Applied Mathematics & Information Sciences, Vol. 8, No. 6, pp. 2889–2902, November 2012.
10. Wenliang Wang, “Design of nonpolarizing antireflection coating by using multiobjective optimization algorithm”, Optik,
Vol. 124, No. 16, pp. 2482–2486, 2013.
11. Jiajia Chen, Yongsheng Ding, Yaochu Jin and Kuangrong Hao, “A Synergetic Immune Clonal Selection Algorithm Based
Multi-Objective Optimization Method for Carbon Fiber Drawing Process”, Fibers and Polymers, Vol. 14, No. 10, pp.
1722–1730, October 2013.
12. B. Srinivasa Rao and K. Vaisakh, “Multi-objective adaptive Clonal selection algorithm for solving environmental/economic
dispatch and OPF problems with load uncertainty”, International Journal of Electrical Power & Energy Systems, Vol.
53, pp. 390–408, December 2013.
13. Karthik Sindhya, Kaisa Miettinen and Kalyanmoy Deb, “A Hybrid Framework for Evolutionary Multi-objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 4, pp. 495–511, August 2013.
14. Fang Liu, Si-feng Zhu, Zheng-yi Chai, Yu-tao Qi and Jian-she Wu, “Immune optimization algorithm for solving vertical
handoff decision problem in heterogeneous wireless network”, Wireless Networks, Vol. 19, No. 4, pp. 507–516, May
2013.
15. Junwan Liu, Zhoujun Li, Xiaohua Hu, Yiming Chen and E.K. Park, “Dynamic biclustering of microarray data by
multi-objective immune optimization”, BMC Genomics, Vol. 12, Supplement: 2, Article Number: S11, July 27, 2011.
164
16. Erik Cuevas, Valentin Osuna-Enciso, Daniel Zaldivar, Marco Perez-Cisneros and Humberto Sossa, “Multithreshold
Segmentation Based on Artificial Immune Systems”, Mathematical Problems in Engineering, Article Number: 874761,
2012.
17. Yutao Qi, Fang Liu, Meiyun Liu, Maoguo Gong and Licheng Jiao, “Multi-objective immune algorithm with Baldwinian
learning”, Applied Soft Computing, Vol. 12, No. 8, pp. 2654–2674, August 2012.
18. Arnaud Zinflou, Caroline Gagne and Marc Gravel, “GISMOO: A new hybrid genetic/immune strategy for multipleobjective optimization”, Computers & Operations Research, Vol. 39, No. 9, pp. 1951–1968, September 2012.
19. Ronghua Shang, Licheng Jiao, Fang Liu and Wenping Ma, “A Novel Immune Clonal Algorithm for MO Problems”,
IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 35–50, February 2012.
20. Andre B. de Carvalho and Aurora Pozo, “Measuring the convergence and diversity of CDAS Multi-Objective Particle
Swarm Optimization Algorithms: A study of many-objective problems”, Neurocomputing, Vol. 75, No. 1, pp. 43–51,
January 1, 2012.
21. H. Li and D. Landa-Silva, “An Adaptive Evolutionary Multi-Objective Approach Based on Simulated Annealing”,
Evolutionary Computation, Vol. 19, No. 4, pp. 561–595, Winter 2011.
22. Erik Cuevas, Valentin Osuna-Enciso, Fernando Wario, Daniel Zaldivar and Marco Perez-Cisneros, “Automatic multiple
circle detection based on artificial immune systems”, Expert Systems with Applications, Vol. 39, No. 1, pp. 713–722,
January 2012.
23. Xinchao Zhao, Guoli Liu, Huqiu Liu, Guoshuai Zhao and Shaozhang Niu, “A New Clonal Selection Immune Algorithm with Perturbation Guiding Search and Non-uniform Hypermutation ”, International Journal of Computational
Intelligence Systems, Vol. 3, Suplement 1, pp. 1–17, December 2010.
24. Ruochen Liu, Licheng Jiao, Yangyang Li ang Jing Liu, “An immune memory clonal algorithm for numerical and combinatorial optimization”, Frontiers of Computer Science in China, Vol. 4, No. 4, pp. 536–559, December 2010.
25. Zhuhong Zhang and Shuqu Qian, “Artificial immune system in dynamic environments solving time-varying non-linear
constrained multi-objective problems”, Soft Computing, Vol. 15, No. 7, pp. 1333–1349, July 2011.
26. Qian Li, Linyan Sun and Liang Bao, “Enhanced index tracking based on multi-objective immune algorithm”, Expert
Systems with Applications, Vol. 38, No. 5, pp. 6101–6106, May 2011.
27. Jui-Yu Wu, “Solving Constrained Global Optimization via Artificial Immune System”, International Journal on Artificial
Intelligence Tools, Vol. 20, No. 1, pp. 1–27, February 2011.
28. Thiago Quirino, Miroslav Kubat and Nicholas J. Bryan, “Instinct-Based Mating in Genetic Algorithms Applied to the
Tuning of 1-NN Classifiers”, IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 12, pp. 1724–1737,
December 2010.
29. Guilherme P. Coelho, Ana Estela A. da Silva and Fernando J. Von Zuben, “An immune-inspired multi-objective approach
to the reconstruction of phylogenetic trees”, Neural Computing & Applications, Vol. 19, No. 8, pp. 1103–1132, November
2010.
30. Aldo Canova and Fabio Freschi, “Multiobjective design optimization and Pareto front analysis of a radial eddy current
coupler”, International Journal of Applied Electromagnetics and Mechanics, Vol. 32, No. 4, pp. 219–236, 2010.
31. Jianyong Chen, Qiuzhen Lin and Qinbin Hu, “Application of Novel Clonal Algorithm in Multiobjective Optimization”,
International Journal of Information Technology & Decision Making, Vol. 9, No. 2, pp. 239–266, March 2010.
32. Kerim Guney and Bilal Babayigit, “Amplitude-only pattern nulling of linear antenna arrays with the use of an immune
algorithm”, International Journal of RF and Microwave Computer-Aided Engineering, Vol. 18, No. 5, pp. 397–409,
September 2008.
33. Elizabeth F. Wanner, Frederico G. Guimarães, Ricardo H.C. Takahashi and Peter J. Fleming, “Local Search with
Quadratic Approximations into Memetic Algorithms for Optimization with Multiple Criteria”, Evolutionary Computation, Vol. 16, No. 2, pp. 185–224, Summer 2008.
34. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjective immune algorithm with nondominated
neighbor-based selection”, Evolutionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.
35. Kerim Guney, B. Babayigit and A. Akdagli, “Position only pattern nulling of linear antenna array by using a clonal
selection algorithm (CLONALG)”, Electrical Engineering, Vol. 90, No. 2, pp. 147–153, December 2007.
36. K.C. Tan, C.K. Goh, A.A. Mamun and E.Z. Ei, “An evolutionary artificial immune system for multi-objective optimization”, European Journal of Operational Research, Vol. 187, No. 2, pp. 371–392, June 1, 2008.
37. R. Tavakkoli-Moghaddam, A.R. Rahimi-Vahed and A.H. Mirzaei, “Solving a multi-objective no-wait flow shop scheduling
problem with an immune algorithm”, International Journal of Advanced Manufacturing Technology, Vol. 36, Nos. 9–10,
pp. 969–981, April 2008.
38. K. Guney, B. Babayigit and A. Akdagli, “Interference suppression of linear antenna arrays by phase-only control using
a clonal selection algorithm”, Journal of the Franklin Institute–Engineering and Applied Mathematics, Vol. 345, No. 3,
pp. 254–266, May 2008.
165
39. Reza Tavakkoli-Moghaddam, Alireza Rahimi-Vahed and Ali Hossein Mirzaei, “A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean
tardiness”, Information Sciences, Vol. 177, No. 22, pp. 5072–5090, November 15, 2007.
40. Ashish Ahuja, Sanjoy Das and Anil Pahwa, “An AIS-ACO hybrid approach for multi-objective distribution system
reconfiguration”, IEEE Transactions on Power Systems, Vol. 22, No. 3, pp. 1101–1111, August 2007.
41. Sanjoy Das, Balasubramaniam Natarajan, Daniel Stevens and Praveen Koduru, “Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principle”, Applied Soft Computing, Vol. 8, No. 1, pp.
788–797, January 2008.
42. Frederico G. Guimaraes, Reinaldo M. Palhares, Felipe Campelo and Hajime Igarashi, “Design of mixed H-2/H infinity
control systems using algorithms inspired by the immune system”, Information Sciences, Vol. 177, No. 20, pp. 4368–
4386, October 15, 2007.
43. Jongsoo Lee and Hyuk Park, “Constrained minimization utilizing GA based pattern recognition of immune system”,
Journal of Mechanical Science and Technology, Vol. 21, No. 5, pp. 779–788, May 2007.
44. Zhuhong Zhang, “Immune optimization algorithm for constrained nonlinear multiobjective optimization problems”,
Applied Soft Computing, Vol. 7, No. 3, pp. 840–857, June 2007.
45. Xiaoning Shen and Weili Hu, “MONEP: A multi-objective non-uniform evolutionary programming algorithm”, Dynamics
of Continuous Discrete and Impulsive Systems–Series B–Applications & Algorithms, Vol. 13, pp. 888–892, Part 2,
December 2006.
46. A. Akdagli, K. Guney and B. Babayigit, “Clonal selection algorithm for design of reconfigurable antenna array with
discrete phase shifters”, Journal of Electromagnetic Waves and Applications, Vol. 21, No. 2, pp. 215–227, 2007.
47. A.R. Yildiz and F. Ozturk, “Hybrid enhanced genetic algorithm to select optimal machining parameters in turning
operation”, Proceedings of the Institution of Mechanical Engineers Part B–Journal of Engineering Manufacture, Vol.
220, No. 12, pp. 2041–2053, December 2006.
48. Kerim Guney, Ali Akdagli and Bilal Babayigit, “Shaped-beam pattern synthesis of linear antenna arrays with the use
of a clonal selection algorithm”, Neural Network World, Vol. 16, No. 6, pp. 489–501, 2006.
49. Jun Chen and Mahdi Mahfouf, “A population adaptive based immune algorithm for solving multi-objective optimization
problems”, in Hughes Bersini and Jorge Carneiro (editors), Artificial Immune Systems, 5th International Conference,
ICARIS 2006, Proceedings, pp. 280–293, Springer-Verlag, Lecture Notes in Computer Science Vol. 4163, Oeiras,
Portugal, September 2006.
50. Guilherme P. Coelho and Fernando Von Zuben, “Omni-aiNet: An immune-inspired approach for omni optimization”,
Artificial Immune Systems, Proceedings, pp. 294–308, Springer-Verlag, Lecture Notes in Computer Science Vol. 4163,
2006.
51. P.A. Castillo, M.G. Arenas, J.J. Merelo, V.M. Rivas and G. Romero, “Multiobjective Optimization of Ensembles of
Multilayer Perceptrons for Pattern Classification”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke,
Juan J. Merelo-Guervós, L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX,
9th International Conference, pp. 453–462, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland,
September 2006.
52. Fabio Freschi and Maurizio Repetto, “VIS: an artificial immune network for multi-objective optimization”, Engineering
Optimization, Vol. 38, No. 8, pp. 975–996, December 2006.
53. H.W. Dai, Z. Tang, Y. Yang and H. Tamura, “Affinity based lateral interaction artificial immune system”, IEICE
Transactions on Information and Systems, Vol. E89D, No. 4, pp. 1515–1524, April 2006.
54. Deepti Chafekar, Liang Shi, Khaled Rasheed and Jiang Xuan, “Multiobjective GA Optimization Using Reduced Models”,
IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 35, No. 2, pp. 261–265,
May 2005.
55. S. Meshoul, K. Mahdi and M. Batouche, “A quantum inspired evolutionary framework for multi-objective optimization”,
in Progress in Artificial Intelligence, Proceedings, pp. 190–201, Springer, Lecture Notes in Artificial Intelligence, Vol.
3808, 2005.
56. Maoguo Gong, Licheng Jiao, Lining Zhang and Haifeng Du, “Immune Secondary Response and Clonal Selection Inspired
Optimizers”, Progress in Natural Science, Vol. 19, No. 2, pp. 237–253, February 2009.
57. Ramin Halavati and Saeed Bagher Shouraki, “Symbiotic Artificial Immune System”, Soft Computing, Vol. 13, No. 6,
pp. 565–575, April 2009.
58. All Riza Yildiz, “A Novel Hybrid Immune Algorithm for Global Optimization in Design and Manufacturing”, Robotics
and Computer-Integrated Manufacturing, Vol. 25, No. 2, pp. 261–270, April 2009.
59. Maoguo Gong, Licheng Jiao, Jie Yang and Fang Liu, “Lamarckian Learning in Clonal Selection Algorithm for Numerical
Optimization”, International Journal on Artificial Intelligence Tools, Vol. 19, No. 1, pp. 19–37, February 2010.
166
60. Jianyong Chen, Qiuzhen Lin and Zhen Ji, “A hybrid immune multiobjective optimization algorithm”, European Journal
of Operational Research, Vol. 204, No. 2, pp. 294–302, July 16, 2010.
61. J.H. Ang, K.C. Tan and A.A. Mamun, “An evolutionary memetic algorithm for rule extraction”, Expert Systems with
Applications, Vol. 37, No. 2, pp. 1302–1315, March 2010.
62. Zhi-Hua Hu, “A multiobjective immune algorithm based on a multiple-affinity model”, European Journal of Operational
Research, Vol. 202, No. 1, pp. 60–72, April 1, 2010.
63. E. Soury, A.H. Behravesh, E. Rouhani Esfahani and A. Zolfaghari, “Design, optimization and manufacturing of woodplastic composite pallet”, Materials & Design, Vol. 30, No. 10, pp. 4183–4191, December 2009.
64. Jiaquan Gao and Jun Wang, “WBMOAIS: A novel artificial immune system for multiobjective optimization”, Computers
& Operations Research, Vol. 37, No. 1, pp. 50–61, January 2010.
65. MaoGuo Gong, LiCheng Jiao, WenPing Ma and HaiFeng Du, “Multiobjective optimization using an immunodominance
and clonal selection inspired algorithm”, Science in China Series F–Information Sciences, Vol. 51, No. 8, pp. 1064–1082,
August 2008.
66. H. Park, N.-S. Kwak and J. Lee, “A method of multiobjective optimization using a genetic algorithm and an artificial
immune system”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical Engineering
Science, Vol. 223, No. 5, pp. 1243–1252, May 2009.
67. Wenping Ma, Licheng Jiao and Maoguo Gong, “Immunodominance and clonal selection inspired multiobjective clustering”, Progress in Natural Science, Vol. 19, No. 6, pp. 751–758, June 10, 2009.
• Carlos A. Coello Coello, “Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems”,
Computers in Industry, Vol. 41, No. 2, pp. 113–127, January 2000.
1. Seyedali Mirjalili and Andrew Lewis, “Adaptive gbest-guided gravitational search algorithm”, Neural Computing &
Applications, Vol. 25, Nos. 7-8, December 2014.
2. Hamid Salimi, “Stochastic Fractal Search: A powerful metaheuristic algorithm”, Knowledge-based Systems, Vol. 75, pp.
1–18, February 2015.
3. Haipeng Kong, Li Ni and Yuzhong Shen, “Adaptive double chain quantum genetic algorithm for constrained optimization
problems”, Chinese Journal of Aeronautics, Vol. 28, No. 1, pp. 214–228, February 2015.
4. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics inspired optimization
(OIO)”, Computer-Aided Design, Vol. 63, pp. 52–71, June 2015.
5. Neha S. Patankar, Anand J. Kulkarni, Kang Tai, T.D. Ghate and A.R. Parvate, “Multi-criteria probability collectives”,
International Journal of Bio-Inspired Computation, Vol. 6, No. 6, pp. 369–383, 2014.
6. Rodrigo Ribeiro de Lucena, Juliana Souza Baioco, Beatriz Souza Leite Pires de Lima, Carl Horst Albrecht and Breno
Pinheiro Jacob, “Optimal design of submarine pipeline routes by genetic algorithm with different constraint handling
techniques”, Advances in Engineering Software, Vol. 76, pp. 110–124, October 2014.
7. Zhenzhou Hu, Xinye Cai and Zhun Fan, “An improved memetic algorithm using ring neighborhood topology for constrained optimization”, Soft Computing, Vol. 18, No. 10, pp. 2023–2041, October 2014.
8. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.
53, No. 4, pp. 1168–1183, July 2014.
9. Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis, “Grey Wolf Optimizer”, Advances in Engineering
Software, Vol. 69, pp. 46–61, March 2014.
10. Qing Liu, Tomohiro Odaka, Jousuke Kuroiwa, Haruhiko Shirai and Hisakazu Ogura, “A New Artificial Fish Swarm
Algorithm for the Multiple Knapsack Problem”, IEICE Transactions on Information and Systems, Vol. E97D, No. 3,
pp. 455–468, March 2014.
11. Harish Garg, “Solving Structural Engineering Design Optimization Problems using an Artificial Bee Colony Algorithm”,
Journal of Industrial and Management Optimization, Vol. 10, No. 3, pp. 777–794, July 2014.
12. Erik Cuevas and Miguel Cienfuegos, “A new algorithm inspired in the behavior of the social-spider for constrained
optimization”, Expert Systems with Applications, Vol. 41, No. 2, pp. 412–425, February 1, 2014.
13. Liang Bai, Junyan Wang, Yongheng Jiang and Dexian Huang, “Improved Hybrid Differential Evolution-Estimation of
Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems”, Chinese Journal
of Chemical Engineering, Vol. 20, No. 6, pp. 1074–1080, December 2012.
14. Chunjiang Zhang, Xinyu Li, Liang Gao and Qing Wu, “An improved electromagnetism-like mechanism algorithm for
constrained optimization”, Expert Systems with Applications, Vol. 40, No. 14, pp. 5621–5634, October 15, 2013.
15. Syeda Darakhshan Jabeen, “Split and Discard Strategy: A New Approach for Constrained Global Optimization”,
International Journal of Artificial Intelligence Tools, Vol. 22, No. 4, Article Number: 1350023, August 2013.
167
16. Yu Guo, Wen’an Yang, Wenhe Liao and Shiwen Gao, “Economic Design of (X)over-bar & S Control Charts Based
on Taguchi’s Loss Function and Its Optimization”, Chinese Journal of Mechanical Engineering, Vol. 25, No. 3, pp.
576–586, May 2012.
17. Vinicius Veloso de Melo and Grazieli Luiza Costa Carosio, “Evaluating differential evolution with penalty function to
solve constrained engineering problems”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7860–7863, July 2012.
18. Xin-She Yang and Amir Hossein Gandomi, “Bat algorithm: a novel approach for global engineering optimization”,
Engineering Computations, Vol. 29, Nos. 5-6, pp. 464–483, 2012.
19. A. Kaveh and S. Talatahari, “Hybrid charged system search and particle swarm optimization for engineering design
problems”, Engineering Computations, Vol. 28, Nos. 3-4, pp. 423–440, 2011.
20. Xin-She Yang, “Firefly algorithm, stochastic test functions and design optimisation”, International Journal of Bioinspired Computation, Vol. 2, No. 2, pp. 78–84, 2010.
21. A. Kaveh, Mohammad A. Motie Share and M. Moslehi, “Magnetic charged system search: a new meta-heuristic algorithm
for optimization”, Acta Mechanica, Vol. 224, No. 1, pp. 85–107, January 2013.
22. Yongquan Zhou, Guo Zhou and Junl Zhang, “A Hybrid Glowworm Swarm Optimization Algorithm for Constrained
Engineering Design Problems”, Applied Mathematics & Information Sciences, Vol. 7, No. 1, pp. 379–388, January
2013.
23. Issam Mazhoud, Khaled Hadj-Hamou, Jean Bigeon and Patrice Joyeux, “Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism”, Engineering Applications of Artificial Intelligence, Vol. 26,
No. 4, pp. 1263–1273, April 2013.
24. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
25. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
26. E. Sidiropoulos and P. Tolikas, “Genetic algorithms and cellular automata in aquifer management”, Applied Mathematical
Modelling, Vol. 32, No. 4, pp. 617–640, April 2008.
27. M. Tamer Ayvaz, Ali Haydar Kayhan, Huseyin Ceylan and Gurhan Gurarslan, “Hybridizing the harmony search algorithm with a spreadsheet ’Solver’ for solving continuous engineering optimization problems”, Engineering Optimization,
Vol. 41, No. 12, pp. 1119–1144, 2009.
28. Rafael S. Parpinelli, Fabio R. Teodoro, Heitor S. Lopes, “A comparison of swarm intelligence algorithms for structural
engineering optimization”, International Journal for Numerical Methods in Engineering, Vol. 91, No. 6, pp. 666–684,
August 10, 2012.
29. A. Kaveh and M. Ahangaran, “Social Harmony Search Algorithm for Continuous Optimization”, Iranian Journal of
Science and Technology-Transactions of Civil Engineering, Vol. 36, No. C2, pp. 121–137, August 2012.
30. Ali Riza Yildiz, “Hybrid Taguchi-Harmony Search Algorithm for Solving Engineering Optimization Problems”, International Journal of Industrial Engineering Theory, Applications and Practice, Vol. 15, No. 3, pp. 286–293, 2008.
31. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Cuckoo search algorithm: a metaheuristic approach to
solve structural optimization problems”, Engineering with Computers, Vol. 29, No. 1, pp. 17–35, January 2013.
32. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
33. Wenxing Xu, Zhiqiang Geng, Qunxiong Zhu and Xiangbai Gu, “A piecewise linear chaotic map and sequential quadratic
programming based robust hybrid particle swarm optimization”, Information Sciences, Vol. 218, pp. 85–102, January
1, 2013.
34. Vivek Kumar Mehta and Bhaskar Dasgupta, “A constrained optimization algorithm based on the simplex search
method”, Engineering Optimization, Vol. 44, No. 5, pp. 537–550, 2012.
35. Xin-She Yang and Suash Deb, “Two-stage eagle strategy with differential evolution”, International Journal of BioInspired Computation, Vol. 4, No. 1, pp. 1–5, 2012.
36. Musrrat Ali, Millie Pant, Ajith Abraham and Chang Wook Ahn, “Swarm Directions Embedded Differential Evolution
for Faster Convergence of Global Optimization Problems”, International Journal on Artificial Intelligence Tools, Vol.
21, No. 3, Article Number: 1240013, June 2012.
37. Layak Ali, Samrat L. Sabat and Siba K. Udgata, “Particle swarm optimisation with stochastic ranking for constrained
numerical and engineering benchmark problems”, International Journal of Bio-Inspired Computation, Vol. 4, No. 3, pp.
155–166, 2012.
168
38. Sanghoun Oh, Chang Wook Ahn and Moongu Jeon, “Effective Constraints Based Evolutionary Algorithm for Constrained Optimization Problems”, International Journal of Innovative Computing Information and Control, Vol. 8, No.
6, pp. 3997–4014, June 2012.
39. He Xu, X.Z. Gao, Gao-liang Peng, Kai Xue and Yulin Ma, “Prototype optimization of reconfigurable mobile robots
based on a modified Harmony Search method”, Transactions of the Institute of Measurement and Control, Vol. 34, Nos.
2-3, pp. 334–360, April-May 2012.
40. Hadi Sarvari and Kamran Zamanifar, “Improvement of harmony search algorithm by using statistical analysis”, Artificial
Intelligence Review, Vol. 37, No. 3, pp. 181–215, March 2012.
41. Ana Maria A.C. Rocha and Edite M.G.P. Fernandes, “Numerical study of augmented Lagrangian algorithms for constrained global optimization”, Optimization, Vol. 60, Nos. 10–11, pp. 1359–1378, 2011.
42. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Mixed variable structural optimization using Firefly
Algorithm”, Computers & Structures, Vol. 89, Nos. 23-24, pp. 2325–2336, December 2011.
43. Payam Ashtari and Farshid Barzegar, “Accelerating fuzzy genetic algorithm for the optimization of steel structures”,
Structural and Multidisciplinary Optimization, Vol. 45, No. 2, pp. 275–285, February 2012.
44. Kazuaki Masuda and Kenzo Kurihara, “A constrained global optimization method based on multi-objective particle
swarm optimization”, Electronics and Communications in Japan, Vol. 95, No. 1, pp. 43–54, January 2012.
45. Ali Husseinzadeh Kashan, “An efficient algorithm for constrained global optimization and application to mechanical
engineering design: League championship algorithm (LCA)”, Computer-Aided Design, Vol. 43, No. 12, pp. 1769–1792,
December 2011.
46. Sanghoun Oh, Yaochu Jin and Moongu Jeon, “Approximate Models for Constraint Functions in Evolutionary Constrained Optimization”, International Journal of Innovative Computing Information and Control, Vol. 7, No. 11, pp.
6585–6603, November 2011.
47. Sungho Mun and Yoon-Ho Cho, “Modified harmony search optimization for constrained design problems”, Expert Systems with Applications, Vol. 39, No. 1, pp. 419–423, January 2012.
48. J.C. Inostroza and V.H. Hinojosa, “Short-term scheduling solved with a particle swarm optimiser”, IET Generation
Transmission & Distribution, Vol. 5, No. 11, pp. 1091–1104, November 2011.
49. Satoshi Kitayama, Masao Arakawa and Koetsu Yamazaki, “Sequential Approximate Optimization using Radial Basis
Function network for engineering optimization”, Optimization and Engineering, Vol. 12, No. 4, pp. 535–557, December
2011.
50. F. Jolai, J. Razmi and N.K.M. Rostami, “A fuzzy goal programming and meta heuristic algorithms for solving integrated
production: distribution planning problem”, Central European Journal of Operations Research, Vol. 19, No. 4, pp. 547–
569, December 2011.
51. Kezong Tang, Jingyu Yang, Haiyan Chen and Shang Gao, “Improved genetic algorithm for nonlinear programming
problems”, Journal of Systems Engineering and Electronics, Vol. 22, No. 3, pp. 540–546, June 2011.
52. Eric Beaser, Jennifer K. Schwartz, Caleb B. Bell, III and Edward I. Solomon, “Hybrid Genetic Algorithm with an
Adaptive Penalty Function for Fitting Multimodal Experimental Data: Application to Exchange-Coupled Non-Kramers
Binuclear Iron Active Sites”, Journal of Chemical Information and Modeling, Vol. 51, No. 9, pp. 2164–2173, September
2011.
53. Xiang Li and Gang Du, “Inequality constraint handling in genetic algorithms using a boundary simulation method”,
Computers & Operations Research, Vol. 39, No. 3, pp. 521–540, March 2012.
54. Hamidreza Modares and Mohammad-Bagher Naghibi Sistani, “Solving nonlinear optimal control problems using a hybrid
IPSO-SQP algorithm”, Engineering Applications of Artificial Intelligence, Vol. 24, No. 3, pp. 476–484, April 2011.
55. M. Hadi Mashinchi, Mehmet A. Orgun and Witold Pedrycz, “Hybrid optimization with improved tabu search”, Applied
Soft Computing, Vol. 11, No. 2, pp. 1993–2006, March 2011.
56. Chunping Hu and Xuefeng Yan, “An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation”, Chinese Journal of Chemical Engineering, Vol. 17, No.
2, pp. 232–240, April 2009.
57. Wen-an Yang, Yu Guo and Wen-he Liao, “Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm”, International Journal of Advanced Manufacturing Technology, Vol. 54, Nos. 1-4, pp. 45–57, April
2011.
58. Dexuan Zou, Haikuan Liu, Liqun Gao and Steven Li, “Directed searching optimization algorithm for constrained optimization problems”, Expert Systems with Applications, Vol. 38, No. 7, pp. 8716–8723, July 2011.
59. Ke-Zong Tang, Ting-Kai Sun and Jing-Yu Yang, “An improved genetic algorithm based on a novel selection strategy for
nonlinear programming problems”, Computers & Chemical Engineering, Vol. 35, No. 4, pp. 615–621, April 2011.
169
60. Satoshi Kitayama, Masao Arakawa and Koetsu Yamazaki, “Differential evolution as the global optimization technique
and its application to structural optimization”, Applied Soft Computing, Vol. 11, No. 4, pp. 3792–3803, June 2011.
61. Y. Sun, Z. Wang, G. Qi and B.J. van Wyk, “Chaotic particle swarm optimization with neural network structure and its
application”, Engineering Optimization, Vol. 43, No. 1, pp. 19–37, January-March 2011.
62. Ali Mohammad Nezhad and Hashem Mahlooji, “A revised particle swarm optimization based discrete Lagrange multipliers method for nonlinear programming problems”, Computers & Operations Research, Vol. 38, No. 8, pp. 1164–1174,
August 2011.
63. Lei Gao and Atakelty Hailu, “Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems”, International Journal of Computational Intelligence Systems, Vol. 3, No. 6, pp. 832–842, December
2010.
64. Giordano Tomassetti, “A cost-effective algorithm for the solution of engineering problems with particle swarm optimization”, Engineering Optimization, Vol. 42, No. 5, pp. 471–495, 2010.
65. R. Toscano and P. Lyonnet, “A new heuristic approach for non-convex optimization problems”, Information Sciences,
Vol. 180, No. 10, pp. 1955–1966, May 15, 2010.
66. Efren Mezura-Montes, Mariana Miranda-Varela and Rubi del Carmen Gomez-Ramon, “Differential evolution in constrained numerical optimization: An empirical study”, Information Sciences, Vol. 180, No. 22, pp. 4223–4262, November
15, 2010.
67. A. Kaveh and S. Talatahari, “An improved ant colony optimization for constrained engineering design problems”,
Engineering Computations, Vol. 27, Nos. 1-2, pp. 155–182, 2010.
68. Xiao-Zhi Gao, Xiaolei Wang, Seppo Jari Ovaska and He Xu, “A Modified Harmony Search Method in Constrained
Optimization”, International Journal of Innovative Computing Information and Control, Vol. 6, No. 9, pp. 4235–4247,
September 2010.
69. Majid Jaberipour and Esmaile Khorram, “Two improved harmony search algorithms for solving engineering optimization
problems”, Communications in Nonlinear Science and Numerical Simulation, Vol. 15, No. 11, pp. 3316–3331, November
2010.
70. A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search”, Acta Mechanica, Vol.
213, Nos. 3-4, pp. 267–289, September 2010.
71. T.-H. Kim, I. Maruta and T. Sugie, “A simple and efficient constrained particle swarm optimization and its application
to engineering design problems”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical
Engineering Science, Vol. 224, No. C2, pp. 389–400, 2010.
72. Ali Haydar Kayhan, Huseyin Ceylan, M. Tamer Ayvaz and Gurhan Gurarslan, “PSOLVER: A new hybrid particle swarm
optimization algorithm for solving continuous optimization problems”, Expert Systems with Applications, Vol. 37, No.
10, pp. 6798–6808, October 2010.
73. Ioannis G. Tsoulos, “Solving constrained optimization problems using a novel genetic algorithm”, Applied Mathematics
and Computation, Vol. 208, No. 1, pp. 273–283, February 1, 2009.
74. Ali Riza Yildiz, “A novel particle swarm optimization approach for product design and manufacturing”, International
Journal of Advanced Manufacturing Technology, Vol. 40, Nos. 5–6, pp. 617–628, January 2009.
75. Erwie Zahara and Yi-Tung Kao, “Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained
engineering design problems”, Expert Systems with Applications, Vol. 36, No. 2, pp. 3880–3886, Part 2, March 2009.
76. Hai Shen, Yunlong Zhu, Ben Niu and Q.H. Wu, “An improved group search optimizer for mechanical design optimization
problems”, Progress in Natural Science, Vol. 19, No. 1, pp. 91–97, January 10, 2009.
77. Jinhua Wang and Zeyong Yin, “A ranking selection-based particle swarm optimizer for engineering design optimization
problems”, Structural and Multidisciplinary Optimization, Vol. 37, No. 2, pp. 131–147, December 2008.
78. Salam Nema, John Goulermas, Graham Sparrow and Phil Cook, “A Hybrid Particle Swarm Branch-and-Bound (HPB)
Optimizer for Mixed Discrete Nonlinear Programming”, IEEE Transactions on Systems, Man, and Cybernetics–Part A:
Systems and Humans, Vol. 38, No. 6, pp. 1411–1424, November 2008.
79. M.H. Afshar, “Penalty adapting ant algorithm: application to pipe network optimization”, Engineering Optimization,
Vol. 40, No. 10, pp. 969–987, October 2008.
80. M. Fesanghary, M. Mahdavi, M. Minary-Jolandan and Y. Alizadeh, “Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems”, Computer Methods in Applied Mechanics and
Engineering, Vol. 197, Nos. 33–40, pp. 3080–3091, 2008.
81. Leandro dos Santos Coelho, “A quantum particle swarm optimizer with chaotic mutation operator”, Chaos Solitons &
Fractals, Vol. 37, No. 5, pp. 1409–1418, September 2008.
82. Vedat Togan and Ayse T. Daloglu, “An improved genetic algorithm with initial population strategy and self-adaptive
member grouping”, Computers & Structures, Vol. 86, Nos. 11–12, pp. 1204–1218, June 2008.
170
83. Simone Puzzi and Alberto Carpinteri, “A double-multiplicative dynamic penalty approach for constrained evolutionary
optimization”, Structural and Multidisciplinary Optimization, Vol. 35, No. 5, pp. 431–445, May 2008.
84. Leandro dos Santos Coelho and Viviana Cocco Mariani, “Use of chaotic sequences in a biologically inspired algorithm
for engineering design optimization”, Expert Systems with Applications, Vol. 34, No. 3, pp. 1905–1913, April 2008.
85. A. Ponsich, C. Azzaro-Pantel, S. Domenech and L. Pibouleau, “Constraint handling strategies in Genetic Algorithms
application to optimal batch plant design”, Chemical Engineering and Processing, Vol. 47, No. 3, pp. 420–434, March
2008.
86. Jenn-Long Liu and Jiann-Horng Lin, “Evolutionary computation of unconstrained and constrained problems using a
novel momentum-type particle swarm optimization”, Engineering Optimization, Vol. 39, No. 3, pp. 287–305, April
2007.
87. M. Mahdavi, M. Fesanghary and E. Damangir, “An improved harmony search algorithm for solving optimization problems”, Applied Mathematics and Computation, Vol. 188, No. 2, pp. 1567–1579, May 15, 2007.
88. Fu-zhuo Huang, Ling Wang and Qie He, “An effective co-evolutionary differential evolution for constrained optimization”,
Applied Mathematics and Computation, Vol. 186, No. 1, pp. 340–356, March 1, 2007.
89. Qie He and Ling Wang, “A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization”,
Applied Mathematics and Computation, Vol. 186, No. 2, pp. 1407–1422, March 15, 2007.
90. R.F. Coelho and P. Bouillard, “A multicriteria evolutionary algorithm for mechanical design optimization with expert
rules”, International Journal for Numerical Methods in Engineering, Vol. 62, No. 4, pp. 516–536, January 28, 2005.
91. S. He, E. Prempain and Q.H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems”,
Engineering Optimization, Vol. 36, No. 5, pp. 585–605, October 2004.
92. J.S. Cui and Z.Q. Sun, “Model-based visual hand posture tracking for guiding a dexterous robotic hand”, Optics
Communications, Vol. 235, Nos. 4–6, pp. 311–318, May 15 2004.
93. A.C.C. Lemonge and H.J.C. Barbosa, “An adaptive penalty scheme for genetic algorithms in structural optimization”,
International Journal for Numerical Methods in Engineering, Vol. 59, No. 5, pp. 703–736, February 7, 2004.
94. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical design with constraints and preferences: application
to a purge valve”, Computer Methods in Applied Mechanics and Engineering, Vol. 192, Nos. 39–40, pp. 4355–4378,
2003.
95. Pruettha Nanakorn & K. Meesomklin, “An adaptive penalty function in genetic algorithms for structural design optimization”, Computers and Structures, Vol. 79, Nos. 29–30, pp. 2527–2539, November 2001.
96. P. Chootinan and A. Chen, “Constraint handling in genetic algorithms using a gradient-based repair method”, Computers
& Operations Research, Vol. 33, No. 8, pp. 2263–2281, August 2006.
97. L. Zhang, L. Wang and D.Z. Zheng, “An adaptive genetic algorithm with multiple operators for flowshop scheduling”,
International Journal of Advanced Manufacturing Technology, Vol. 27, Nos. 5–6, pp. 580–587, January 2006.
98. L. Wang, “A hybrid genetic algorithm-neural network strategy for simulation optimization”, Applied Mathematics and
Computation, Vol. 170, No. 2, pp. 1329–1343, November 15, 2005.
99. K.E. Parsopoulos and M.N. Vrahatis, “Unified Particle Swarm Optimization for solving constrained engineering optimization problems”, Advances in Natural Computation, Pt. 3, Proceedings, Springer, pp. 582–591, Lecture Notes in
Computer Science Vol. 3612, 2005.
100. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
101. Sangameswar Venkatraman and Gary G. Yen, “A Generic Framework for Constrained Optimization Using Genetic
Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 9, No. 4, August 2005
102. Jenn-long Liu, “Novel orthogonal simulated annealing with fractional factorial analysis to solve global optimization
problems”, Engineering Optimization, Volume 37, No. 5, pp. 499–519, July 2005.
103. K.S. Lee and Z.W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search
theory and practice”, Computer Methods in Applied Mechanics and Engineering, Vol. 194, Nos. 36–38, pp. 3902–3933,
2005.
104. Tetsuyuki Takahama, Setsuko Sakai and Noriyuki Iwane, “Constrained optimization by the constrained hybrid algorithm of particle swarm optimization and genetic algorithm”, in S. Zhang and R. Jarvis (editors), AI 2005: Advances
in Artificial Intelligence, Springer-Verlag, pp. 389–400, Lecture Notes in Artificial Intelligence Vol. 3809, 2005.
105. B. Bochenek and P. Forys, “Structural optimization for post-buckling behavior using particle swarms”, Structural and
Multidisciplinary Optimization, Vol. 32, No. 6, pp. 521–531, December 2006.
106. Qie He and Ling Wang, “An effective co-evolutionary particle swarm optimization for constrained engineering design
problems”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 1, pp. 89–99, February 2007.
171
107. George G. Dimopoulos, “Mixed-variable engineering optimization based on evolutionary and social metaphors”, Computer Methods in Applied Mechanics and Engineering, Vol. 196, Nos. 4–6, pp. 803–817, 2007.
108. Ana Maria A.C. Rocha and Edite M.G.P. Fernandes, “Hybridizing the electromagnetism-like algorithm with descent
search for solving engineering design problems”, International Journal of Computer Mathematics, Vol. 86, Nos. 10-11,
pp. 1932–1946, 2009.
109. Quan Yuan and Feng Qian, “A hybrid genetic algorithm for twice continuously differentiable NLP problems”, Computers
& Chemical Engineering, Vol. 34, No. 1, pp. 36–41, January 11, 2010.
110. Satoshi Kitayama, Koetsu Yamazaki and Masao Arakawa, “Adaptive range particle swarm optimization”, Optimization
and Engineering, Vol. 10, No. 4, pp. 575–597, December 2009.
111. Satoshi Kitayama, Keiichiro Yasuda and Koetsu Yamazaki, “Integrative Optimization by RBF Network and Particle
Swarm Optimization”, Electronics and Communications in Japan, Vol. 92, No. 12, pp. 31–42, December 2009.
112. Lixin Tang and Ping Yan, “Particle Swarm Optimization Algorithm for a Batching Problem in the Process Industry”,
Industrial & Engineering Chemistry Research, Vol. 48, No. 20, pp. 9186–9194, October 21, 2009.
113. Leandro dos Santos Coelho, “Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems”, Expert Systems with Applications, Vol. 37, No. 2, pp. 1676–1683, March 2010.
114. Ralf Ostermark, “A fuzzy vector valued KNN-algorithm for automatic outlier detection”, Applied Soft Computing, Vol.
9, No. 4, pp. 1263–1272, September 2009.
115. Xiaoli Kou, Sanyang Liu, Jianke Zhang and Wei Zheng, “Co-evolutionary particle swarm optimization to solve constrained optimization problems”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 1776–1784,
June 2009.
116. Mahamed G.H. Omran and Ayed Salman, “Constrained optimization using CODEQ”, Chaos, Solitons & Fractals, Vol.
42, No. 2, pp. 662–668, October 30, 2009.
117. W. Paszkowicz, “Properties of a genetic algorithm equipped with a dynamic penalty function”, Computational Materials
Science, Vol. 45, No. 1, pp. 77–83, March 2009.
118. Pieterjan Demarcke, Hendrik Rogier, Roald Goossens and Peter De Jaeger, “Beamforming in the Presence of Mutual
Coupling Based on Constrained Particle Swarm Optimization”, IEEE Transactions on Antennas and Propagation, Vol.
57, No. 6, pp. 1655–1666, June 2009.
119. Rosario Toscano and Patrick Lyonnet, “Heuristic Kalman Algorithm for Solving Optimization Problems”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 39, No. 5, pp. 1231–1244, October 2009.
• Carlos A. Coello Coello and Efrén Mezura Montes, “Constraint-Handling in Genetic Algorithms Through
the Use of Dominance-based Tournament Selection”, Advanced Engineering Informatics, Vol. 16, No. 3, pp.
193–203, July 2002.
1. Minggang Dong, Ning Wang, Xiaohui Cheng and Chuanxian Jiang, “Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems”, Mathematical Problems in Engineering, Article Number:
617905, 2014.
2. Seyedali Mirjalili and Andrew Lewis, “Adaptive gbest-guided gravitational search algorithm”, Neural Computing &
Applications, Vol. 25, Nos. 7-8, December 2014.
3. Hamid Salimi, “Stochastic Fractal Search: A powerful metaheuristic algorithm”, Knowledge-based Systems, Vol. 75, pp.
1–18, February 2015.
4. Haipeng Kong, Li Ni and Yuzhong Shen, “Adaptive double chain quantum genetic algorithm for constrained optimization
problems”, Chinese Journal of Aeronautics, Vol. 28, No. 1, pp. 214–228, February 2015.
5. Mostafa Z. Ali and Noor H. Awad, “A novel class of niche hybrid Cultural Algorithms for continuous engineering
optimization”, Information Sciences, Vol. 267, pp. 158–190, May 20, 2014.
6. Neha S. Patankar, Anand J. Kulkarni, Kang Tai, T.D. Ghate and A.R. Parvate, “Multi-criteria probability collectives”,
International Journal of Bio-Inspired Computation, Vol. 6, No. 6, pp. 369–383, 2014.
7. Rommel G. Regis, “Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization
Using Radial Basis Functions”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 326–347, June
2014.
8. Zhenzhou Hu, Xinye Cai and Zhun Fan, “An improved memetic algorithm using ring neighborhood topology for constrained optimization”, Soft Computing, Vol. 18, No. 10, pp. 2023–2041, October 2014.
9. Jinn-Tsong Tsai, “Improved differential evolution algorithm for nonlinear programming and engineering design problems”, Neurocomputing, Vol. 148, pp. 628–640, January 19, 2015.
10. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.
53, No. 4, pp. 1168–1183, July 2014.
172
11. Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis, “Grey Wolf Optimizer”, Advances in Engineering
Software, Vol. 69, pp. 46–61, March 2014.
12. Marco Montemurro, Angela Vincenti and Paolo Vannucci, “The Automatic Dynamic Penalisation method (ADP) for
handling constraints with genetic algorithms”, Computer Methods in Applied Mechanics and Engineering, Vol. 256, pp.
70–87, April 1, 2013.
13. G. Kanagaraj, S.G. Ponnambalam and N. Jawahar, “A hybrid cuckoo search and genetic algorithm for reliabilityredundancy allocation problems”, Computers & Industrial Engineering, Vol. 66, No. 4, pp. 1115–1124, December
2013.
14. Harish Garg, “Solving Structural Engineering Design Optimization Problems using an Artificial Bee Colony Algorithm”,
Journal of Industrial and Management Optimization, Vol. 10, No. 3, pp. 777–794, July 2014.
15. Jianqiao Chen, Yuanfu Tang and Xiaoxu Huang, “Application of Surrogate Based Particle Swarm Optimization to the
Reliability-Based Robust Design of Composite Pressure Vessels”, Acta Mechanica Solida Sinica, Vol. 26, No. 5, pp.
480–490, October 2013.
16. Wen Long, Ximing Liang, Yafei Huang and Yixiong Chen, “A hybrid differential evolution augmented Lagrangian
method for constrained numerical and engineering optimization”, Computer-Aided Design, Vol. 45, No. 12, pp. 1562–
1574, December 2013.
17. Liang Bai, Junyan Wang, Yongheng Jiang and Dexian Huang, “Improved Hybrid Differential Evolution-Estimation of
Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems”, Chinese Journal
of Chemical Engineering, Vol. 20, No. 6, pp. 1074–1080, December 2012.
18. Chunjiang Zhang, Xinyu Li, Liang Gao and Qing Wu, “An improved electromagnetism-like mechanism algorithm for
constrained optimization”, Expert Systems with Applications, Vol. 40, No. 14, pp. 5621–5634, October 15, 2013.
19. Syeda Darakhshan Jabeen, “Split and Discard Strategy: A New Approach for Constrained Global Optimization”,
International Journal of Artificial Intelligence Tools, Vol. 22, No. 4, Article Number: 1350023, August 2013.
20. Yu Guo, Wen’an Yang, Wenhe Liao and Shiwen Gao, “Economic Design of (X)over-bar & S Control Charts Based
on Taguchi’s Loss Function and Its Optimization”, Chinese Journal of Mechanical Engineering, Vol. 25, No. 3, pp.
576–586, May 2012.
21. A. Kaveh and S. Talatahari, “Hybrid charged system search and particle swarm optimization for engineering design
problems”, Engineering Computations, Vol. 28, Nos. 3-4, pp. 423–440, 2011.
22. A. Kaveh, Mohammad A. Motie Share and M. Moslehi, “Magnetic charged system search: a new meta-heuristic algorithm
for optimization”, Acta Mechanica, Vol. 224, No. 1, pp. 85–107, January 2013.
23. Yongquan Zhou, Guo Zhou and Junl Zhang, “A Hybrid Glowworm Swarm Optimization Algorithm for Constrained
Engineering Design Problems”, Applied Mathematics & Information Sciences, Vol. 7, No. 1, pp. 379–388, January
2013.
24. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
25. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
26. LiCheng Jiao, Lin Li, RongHua Shang, Fang Liu and Rustam Stolkin, “A novel selection evolutionary strategy for
constrained optimization”, Information Sciences, Vol. 239, pp. 122–141, August 1, 2013.
27. Guanbo Jia, Yong Wang, Zixing Cai and Yaochu Jin, “An improved (µ + λ)-constrained differential evolution for
constrained optimization”, Information Sciences, Vol. 222, pp. 302–322, February 10, 2013.
28. A. Kaveh and M. Ahangaran, “Social Harmony Search Algorithm for Continuous Optimization”, Iranian Journal of
Science and Technology-Transactions of Civil Engineering, Vol. 36, No. C2, pp. 121–137, August 2012.
29. Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi, “Cuckoo search algorithm: a metaheuristic approach to
solve structural optimization problems”, Engineering with Computers, Vol. 29, No. 1, pp. 17–35, January 2013.
30. Junwan Liu, Zhoujun Li, Xiaohua Hu, Yiming Chen and E.K. Park, “Dynamic biclustering of microarray data by
multi-objective immune optimization”, BMC Genomics, Vol. 12, Supplement: 2, Article Number: S11, July 27, 2011.
31. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
32. Wenxing Xu, Zhiqiang Geng, Qunxiong Zhu and Xiangbai Gu, “A piecewise linear chaotic map and sequential quadratic
programming based robust hybrid particle swarm optimization”, Information Sciences, Vol. 218, pp. 85–102, January
1, 2013.
173
33. Junwan Liu, Zhoujun Li, Xiaohua Hu, Yiming Chen and Feifei Liu, “Multi-objective dynamic population shuffled frogleaping biclustering of microarray data”, BMC Genomics, Vol. 13, Supplement: 3, Article Number: S6, June 11, 2012.
34. Maghshoud Amiri and Ali Mohtashami, “Buffer allocation in unreliable production lines based on design of experiments,
simulation, and genetic algorithm”, International Journal of Advanced Manufacturing Technology, Vol. 62, Nos. 1-4,
pp. 371–383, September 2012.
35. Ali Wagdy Mohamed and Hegazy Zaher Sabry, “Constrained optimization based on modified differential evolution
algorithm”, Information Sciences, Vol. 194, pp. 171–208, July 1, 2012.
36. Sanghoun Oh, Chang Wook Ahn and Moongu Jeon, “Effective Constraints Based Evolutionary Algorithm for Constrained Optimization Problems”, International Journal of Innovative Computing Information and Control, Vol. 8, No.
6, pp. 3997–4014, June 2012.
37. Jia-qing Zhao, Ling Wang, Pan Zeng and Wen-hui Fan, “An effective hybrid genetic algorithm with flexible allowance
technique for constrained engineering design optimization”, Expert Systems with Applications, Vol. 39, No. 5, pp.
6041–6051, April 2012.
38. He Xu, X.Z. Gao, Gao-liang Peng, Kai Xue and Yulin Ma, “Prototype optimization of reconfigurable mobile robots
based on a modified Harmony Search method”, Transactions of the Institute of Measurement and Control, Vol. 34, Nos.
2-3, pp. 334–360, April-May 2012.
39. S.O. Degertekin, “Improved harmony search algorithms for sizing optimization of truss structures”, Computers & Structures, Vol. 92-93, pp. 229–241, February 2012.
40. Kalyanmoy Deb and Amit Saha, “Multimodal Optimization Using a Bi-Objective Evolutionary Algorithm”, Evolutionary
Computation, Vol. 20, No. 1, pp. 27–62, Spring 2012.
41. Yong Wang and Zixing Cai, “Combining Multiobjective Optimization with Differential Evolution to Solve Constrained
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 117–134, February
2012.
42. Reza Farshbaf Zinati and Mohammad Reza Razfar, “Constrained optimum surface roughness prediction in turning of
X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm”,
International Journal of Advanced Manufacturing Technology, Vol. 58, Nos. 1-4, pp. 93–107, January 2012.
43. Sanghoun Oh, Yaochu Jin and Moongu Jeon, “Approximate Models for Constraint Functions in Evolutionary Constrained Optimization”, International Journal of Innovative Computing Information and Control, Vol. 7, No. 11, pp.
6585–6603, November 2011.
44. F. Jolai, J. Razmi and N.K.M. Rostami, “A fuzzy goal programming and meta heuristic algorithms for solving integrated
production: distribution planning problem”, Central European Journal of Operations Research, Vol. 19, No. 4, pp. 547–
569, December 2011.
45. Kezong Tang, Jingyu Yang, Haiyan Chen and Shang Gao, “Improved genetic algorithm for nonlinear programming
problems”, Journal of Systems Engineering and Electronics, Vol. 22, No. 3, pp. 540–546, June 2011.
46. Xiang Li and Gang Du, “Inequality constraint handling in genetic algorithms using a boundary simulation method”,
Computers & Operations Research, Vol. 39, No. 3, pp. 521–540, March 2012.
47. Moslem Kazemi, Gary G. Wang, Shahryar Rahnamayan and Kamal Gupta, “Metamodel-Based Optimization for Problems With Expensive Objective and Constraint Functions”, Journal of Mechanical Design, Vol. 133, No. 1, Article
Number: 014505, January 2011.
48. Rommel G. Regis, “Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box
objective and constraint functions”, Computers & Operations Research, Vol. 38, No. 5, pp. 837–853, May 2011.
49. Dexuan Zou, Haikuan Liu, Liqun Gao and Steven Li, “A novel modified differential evolution algorithm for constrained
optimization problems”, Computers & Mathematics with Applications, Vol. 61, No. 6, pp. 1608–1623, March 2011.
50. Dexuan Zou, Haikuan Liu, Liqun Gao and Steven Li, “Directed searching optimization algorithm for constrained optimization problems”, Expert Systems with Applications, Vol. 38, No. 7, pp. 8716–8723, July 2011.
51. Ke-Zong Tang, Ting-Kai Sun and Jing-Yu Yang, “An improved genetic algorithm based on a novel selection strategy for
nonlinear programming problems”, Computers & Chemical Engineering, Vol. 35, No. 4, pp. 615–621, April 2011.
52. Zhuhong Zhang and Shuqu Qian, “Artificial immune system in dynamic environments solving time-varying non-linear
constrained multi-objective problems”, Soft Computing, Vol. 15, No. 7, pp. 1333–1349, July 2011.
53. Salam Nema, John Y. Goulermas, Graham Sparrow and Paul Helman, “A hybrid cooperative search algorithm for
constrained optimization”, Structural and Multidisciplinary Optimization, Vol. 43, No. 1, pp. 107–119, January 2011.
54. Zhenxiao Gao, Tianyuan Xiao and Wenhui Fan, “Hybrid differential evolution and Nelder-Mead algorithm with reoptimization”, Soft Computing, Vol. 15, No. 3, pp. 581–594, March 2011.
55. Kuo-Ming Lee, Jinn-Tsong Tsai, Tung-Kuan Liu and Jyh-Horng Chou, “Improved genetic algorithm for mixed-discretecontinuous design optimization problems”, Engineering Optimization, Vol. 42, No. 10, pp. 927–941, October 2010.
174
56. Lei Gao and Atakelty Hailu, “Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems”, International Journal of Computational Intelligence Systems, Vol. 3, No. 6, pp. 832–842, December
2010.
57. R. Toscano and P. Lyonnet, “A new heuristic approach for non-convex optimization problems”, Information Sciences,
Vol. 180, No. 10, pp. 1955–1966, May 15, 2010.
58. A. Kaveh and S. Talatahari, “An improved ant colony optimization for constrained engineering design problems”,
Engineering Computations, Vol. 27, Nos. 1-2, pp. 155–182, 2010.
59. Soorathep Kheawhom, “Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical
engineering optimization problem”, Journal of Industrial and Engineering Chemistry, Vol. 16, No. 4, pp. 620–628, July
25, 2010.
60. Xiao-Zhi Gao, Xiaolei Wang, Seppo Jari Ovaska and He Xu, “A Modified Harmony Search Method in Constrained
Optimization”, International Journal of Innovative Computing Information and Control, Vol. 6, No. 9, pp. 4235–4247,
September 2010.
61. Majid Jaberipour and Esmaile Khorram, “Two improved harmony search algorithms for solving engineering optimization
problems”, Communications in Nonlinear Science and Numerical Simulation, Vol. 15, No. 11, pp. 3316–3331, November
2010.
62. A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search”, Acta Mechanica, Vol.
213, Nos. 3-4, pp. 267–289, September 2010.
63. Ling Wang and Ling-po Li, “An effective differential evolution with level comparison for constrained engineering design”,
Structural and Multidisciplinary Optimization, Vol. 41, No. 6, pp. 947–963, June 2010.
64. T.-H. Kim, I. Maruta and T. Sugie, “A simple and efficient constrained particle swarm optimization and its application
to engineering design problems”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical
Engineering Science, Vol. 224, No. C2, pp. 389–400, 2010.
65. Ali Haydar Kayhan, Huseyin Ceylan, M. Tamer Ayvaz and Gurhan Gurarslan, “PSOLVER: A new hybrid particle swarm
optimization algorithm for solving continuous optimization problems”, Expert Systems with Applications, Vol. 37, No.
10, pp. 6798–6808, October 2010.
66. Ting-Yu Chen and Yi-Liang Cheng, “Data-mining assisted structural optimization using the evolutionary algorithm and
neural network”, Engineering Optimization, Vol. 42, No. 3, pp. 205–222, March 2010.
67. Varvara G. Asouti and Kyriakos C. Giannakoglou, “Aerodynamic optimization using a parallel asynchronous evolutionary
algorithm controlled by strongly interacting demes”, Engineering Optimization, Vol. 41, No. 3, pp. 241–257, March
2009.
68. Ali Riza Yildiz, “A novel particle swarm optimization approach for product design and manufacturing”, International
Journal of Advanced Manufacturing Technology, Vol. 40, Nos. 5–6, pp. 617–628, January 2009.
69. Erwie Zahara and Yi-Tung Kao, “Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained
engineering design problems”, Expert Systems with Applications, Vol. 36, No. 2, pp. 3880–3886, Part 2, March 2009.
70. Severino F. Galán and Ole J. Mengshoel, “Constraint Handling Using Tournament Selection: Abductive Inference in
Partly Deterministic Bayesian Networks”, Evolutionary Computation, Vol. 17, No. 1, pp. 55–88, Spring 2009.
71. Hai Shen, Yunlong Zhu, Ben Niu and Q.H. Wu, “An improved group search optimizer for mechanical design optimization
problems”, Progress in Natural Science, Vol. 19, No. 1, pp. 91–97, January 10, 2009.
72. Salam Nema, John Goulermas, Graham Sparrow and Phil Cook, “A Hybrid Particle Swarm Branch-and-Bound (HPB)
Optimizer for Mixed Discrete Nonlinear Programming”, IEEE Transactions on Systems, Man, and Cybernetics–Part A:
Systems and Humans, Vol. 38, No. 6, pp. 1411–1424, November 2008.
73. Wen-Fung Leong and Gary G. Yen, “PSO-Based Multiobjective Optimization with Dynamic Population Size and Adaptive Local Archives”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 38, No. 5, pp.
1270–1293, October 2008.
74. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
75. Tien-Tung Chung and Chia-Sheng Shih, “Structural optimization using genetic algorithms with fuzzy rule-based systems”, Journal of the Chinese Society of Mechanical Engineering, Vol. 28, No. 5, pp. 523–532, October 2007.
76. Kusum Deep and Dipti, “A self-organizing migrating genetic algorithm for constrained optimization”, Applied Mathematics and Computation, Vol. 198, No. 1, pp. 237–250, April 15, 2008.
77. A. Ponsich, C. Azzaro-Pantel, S. Domenech and L. Pibouleau, “Constraint handling strategies in Genetic Algorithms
application to optimal batch plant design”, Chemical Engineering and Processing, Vol. 47, No. 3, pp. 420–434, March
2008.
175
78. Yong Wang, Zixing Cai, Yuren Zhou and Wei Zeng, “An Adaptive Tradeoff Model for Constrained Evolutionary Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 1, pp. 80–92, February 2008.
79. A. Ponsich, I. Touche, C. Azzaro-Pantel, M. Dayde, S. Domenech and L. Pibouleau, “Performance analysis of optimization methods in PSE applications - Mathematical programming versus grid-based multi-parametric genetic algorithms”,
Chemical Engineering Research & Design, Vol. 85, No. A6, pp. 815–824, June 2007.
80. Yong Wang, Hui Liu, Zixing Cai and Yuren Zhou, “An orthogonal design based constrained evolutionary optimization
algorithm”, Engineering Optimization, Vol. 39, No. 6, pp. 715–736, September 2007.
81. Yeh-Liang Hsu and Tzu-Chi Liu, “Developing a fuzzy proportional–derivative controller optimization engine for engineering design optimization problems”, Engineering Optimization, Vol. 39, No. 6, pp. 679–700, September 2007.
82. M. Mahdavi, M. Fesanghary and E. Damangir, “An improved harmony search algorithm for solving optimization problems”, Applied Mathematics and Computation, Vol. 188, No. 2, pp. 1567–1579, May 15, 2007.
83. Samya Elaoud, Jacques Teghem and Bassem Bouaziz, “Genetic algorithms to solve the cover printing problem”, Computers & Operations Research, Vol. 34, No. 11, pp. 3346–3361, November 2007.
84. Akira Oyama, Koji Shimoyama and Kozo Fujii, “New constraint-handling method for multi-objective and multiconstraint evolutionary optimization”, Transactions of the Japan Society for Aeronautical and Space Sciences, Vol.
50, No. 167, pp. 56–62, May 2007.
85. Yong Wang, Zixing Cai, Guanqi Guo and Yuren Zhou, “Multiobjective optimization and hybrid evolutionary algorithm
to solve constrained optimization problems”, IEEE Transactions on Systems, Man and Cybernetics Part B–Cybernetics,
Vol. 37, No. 3, pp. 560–575, June 2007.
86. Fu-zhuo Huang, Ling Wang and Qie He, “An effective co-evolutionary differential evolution for constrained optimization”,
Applied Mathematics and Computation, Vol. 186, No. 1, pp. 340–356, March 1, 2007.
87. Zhuhong Zhang, “Immune optimization algorithm for constrained nonlinear multiobjective optimization problems”,
Applied Soft Computing, Vol. 7, No. 3, pp. 840–857, June 2007.
88. Antonin Ponsich, Catherine Azzaro-Pantel, Serge Domenech and Luc Pibouleau, “Mixed-integer nonlinear programming
optimization strategies for batch plant design problems”, Industrial & Engineering Chemistry Research, Vol. 46, No. 3,
pp. 854–863, January 31, 2007.
89. Qie He and Ling Wang, “An effective co-evolutionary particle swarm optimization for constrained engineering design
problems”, Engineering Applications of Artificial Intelligence, Vol. 20, No. 1, pp. 89–99, February 2007.
90. Zixing Cai and Yong Wang, “A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 658–675, December 2006.
91. George G. Dimopoulos, “Mixed-variable engineering optimization based on evolutionary and social metaphors”, Computer Methods in Applied Mechanics and Engineering, Vol. 196, Nos. 4–6, pp. 803–817, 2007.
92. I. Karen, A.R. Yildiz, N. Kaya, N. Öztürk and F. Öztürk, “Hybrid approach for genetic algorithm and Taguchi’s method
based design optimization in the automotive industry”, International Journal of Production Research, Vol. 44, No. 22,
pp. 4897–4914, November 15, 2006.
93. A. Konak, D.W. Coit and A.E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial”, Reliability
Engineering & System Safety, Vol. 91, No. 9, pp. 992–1007, September 2006.
94. A.R. Hedar and M. Fukushima, “Derivative-free filter simulated annealing method for constrained continuous global
optimization”, Journal of Global Optimization, Vol. 35, No. 4, pp. 521–549, August 2006.
95. Ling Wang and Fang Tang, “NN-based GA for engineering optimization”, in Fuliang Yin, Jun Wang, Chengan Guo
(editors), Advances in Neural Networks—ISNN 2004: International Symposium on Neural Networks, Part 1, Springer,
Lecture Notes in Computer Science, Vol. 3173, pp. 448–453, August 2004.
96. A.C.C. Lemonge and H.J.C. Barbosa, “An adaptive penalty scheme for genetic algorithms in structural optimization”,
International Journal for Numerical Methods in Engineering, Vol. 59, No. 5, pp. 703–736, February 7, 2004.
97. L.J. Cui and D.C. Sheng, “Genetic algorithms in probabilistic finite element analysis of geotechnical problems”, Computers and Geotechnics, Vol. 32, No. 8, pp. 555–563, 2005.
98. Y. Hong, Q.S. Ren, J. Zeng and Y. Zhang, “Search space filling and shrinking based to solve constraint optimization
problems”, Advances in Intelligent Computing, Part 1, Proceedings, Springer, pp. 986–994, Lecture Notes in Computer
Science Vol. 3644, 2005.
99. L. Wang, “A hybrid genetic algorithm-neural network strategy for simulation optimization”, Applied Mathematics and
Computation, Vol. 170, No. 2, pp. 1329–1343, November 15, 2005.
100. W.M. Wang, H. Rivard and R. Zmeureanu, “An object-oriented framework for simulation-based green building design
optimization with genetic algorithms”, Advanced Engineering Informatics, Vol. 19, No. 1, pp. 5–23, January 2005.
101. Khadiza Tahera, Raafat N. Ibrahim and Paul B. Lochert, “GADYM - A Novel Genetic Algorithm in Mechanical Design
Problems”, Journal of Universal Computer Science, Vol. 14, No. 15, pp. 2566–2581, 2008.
176
102. Zhi Kong, Liqun Gao, Lifu Wang, Yanfeng Ge and Steven Li, “On an Adaptive Harmony Search Algorithm”, International Journal of Innovative Computing Information and Control, Vol. 5, No. 9, pp. 2551–2560, September 2009.
103. Ali Riza Yildiz, “A new design optimization framework based on immune algorithm and Taguchi’s method”, Computers
in Industry, Vol. 60, No. 8, pp. 613–620, October 2009.
104. O. Baez Senties, C. Azzaro-Pantel, L. Pibouleau and S. Domenech, “A Neural Network and a Genetic Algorithm for
Multiobjective Scheduling of Semiconductor Manufacturing Plants”, Industrial & Engineering Chemistry Research, Vol.
48, No. 21, pp. 9546–9555, November 4, 2009.
105. Ying Yu, Xiaochun Yu and Yongsheng Li, “Novel Discrete Particle Swarm Optimization Based on Huge Value Penalty
for Solving Engineering Problem”, Chinese Journal of Mechanical Engineering, Vol. 22, No. 3, pp. 410–418, June 2009.
106. Yong Wang, Zixing Cai and Yuren Zhou, “Accelerating adaptive trade-off model using shrinking space technique for
constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol. 77, No. 11,
pp. 1501–1534, March 2009.
107. Wanfeng Shang, Shengdun Zhao and Yajing Shen, “A flexible tolerance genetic algorithm for optimal problems with
nonlinear equality constraints”, Advanced Engineering Informatics, Vol. 23, No. 3, pp. 253–264, July 2009.
108. Rosario Toscano and Patrick Lyonnet, “Heuristic Kalman Algorithm for Solving Optimization Problems”, IEEE Transactions on Systems, Man, and Cybernetics Part B–Cybernetics, Vol. 39, No. 5, pp. 1231–1244, October 2009.
• Carlos A. Coello Coello, Alan D. Christiansen and Arturo Hernández Aguirre, “Use of Evolutionary Techniques to Automate the Design of Combinational Circuits”, International Journal of Smart Engineering
System Design, Vol. 2, No. 4, pp. 299–314, June 2000.
1. S. Karakatic, V. Podgorelec and M. Hericko, “Optimization of Combinational Logic Circuits with Genetic Programming”,
Elektronika Ir Elektrotechnika, Vol. 19, No. 7, pp. 86–89, 2013.
2. Wei Wang, Feng Xiao, Xuhui Zeng, Jing Yao, Yuchi Ming and Jiuping Ding, “Optimal Estimation of Ion-Channel
Kinetics from Macroscopic Currents”, PLOS One, Vol. 7, No. 4, Article Number: e35208, April 20, 2012.
3. Guoliang He, Naixue Xiong, Laurence T. Yang, Tai-hoon Kim, Ching Hsien Hsu, Yuanxiang Li and Ting Hu, “Evolvable
hardware design based on a novel simulated annealing in an embedded system”, Concurrency and Computation–Practice
& Experience, Vol. 24, No. 4, pp. 354–370, March 25, 2012.
4. Adam Slowik, “Influence of chromosome coding scheme on increasing of evolutionary design effectiveness of combinational
digital circuits”, Przeglad Electrotechniczny, Vol. 86, No. 7, pp. 172–174, 2010.
5. Z.Y. Wang, B.X. Shi and E. Zhao, “Bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic
algorithm”, Computer Communications, Vol. 24, Nos. 7–8, pp. 685–692, April 1, 2001.
6. Adam Slowik and Michal Bialko, “Design and Optimization of Combinational Digital Circuits Using Modified Evolutionary Algorithm”, in Leszek Rutkowski, Jörg H. Siekmann, Ryszard Tadeusiewicz and Lotfi A. Zadeh (Editors), Artificial
Intelligence and Soft Computing - ICAISC 2004, 7th International Conference. Proceedings, Springer. Lecture Notes in
Computer Science Vol. 3070, pp. 468–473, Zakopane, Poland, June 2004.
7. A.T. Haghighat, K. Faez, M. Dehghan, A. Mowlaei and Y. Ghahremani, “GA-based heuristic algorithms for bandwidthdelay-constrained least-cost multicast routing”, Computer Communications, Vol. 27, No. 1, pp. 111–127, January 1,
2004.
8. Tatiana Kalganova, “An Extrinsic Function-Level Evolvable Hardware Approach”, Genetic Programming. European
Conferece, EuroGP 2000, Riccardo Poli, Wolfgang Banzhaf, William B. Langdon, Julian Miller, Peter Nordin & Terence
C. Fogarty (Eds.), Springer, Berlin, pp. 60–75, April 2000.
9. Sin Man Cheang, Kin Hong Lee and Kwong Sak Leung, “Applying Genetic Parallel Programming to Synthesize Combinational Logic Circuits”, IEEE Transactions on Evolutionary Computation, Vol. 11, No. 4, pp. 503–520, August
2007.
10. Shuguang Zhao, Licheng Jiao and Jun Zhao, “Multi-objective Evolutionary Design and Knowledge Discovery of Logic
Circuits with an Improved Genetic Algorithm”, in Yue Hao et al. (editors), Computational Intelligence and Security.
International Conference, CIS 2005, pp. 273–278, Springer, Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an,
China, December 2005.
11. Emanuele Stomeo, Tatiana Kalganova and Cyrille Lambert, “Generalized Disjunction Decomposition for Evolvable
Hardware”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 36, No. 5, pp. 1024–
1043, October 2006.
• Carlos A. Coello Coello and Arturo Hernández Aguirre, “Design of Combinational Logic Circuits through
an Evolutionary Multiobjective Optimization Approach”, AIEDAM–Artificial Intelligence for Engineering,
Design, Analysis and Manufacture, Vol. 16, No. 1, pp. 39–53, January 2002.
177
1. Mehdi Anjomshoa, Ali Mahani and Salahedin Sadeghifard, “A new automated design and optimization method of CMOS
logic circuits based on Modified Imperialistic Competitive Algorithm”, Applied Soft Computing, Vol. 21, pp. 423–432,
August 2014.
2. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
3. Guoliang He, Naixue Xiong, Laurence T. Yang, Tai-hoon Kim, Ching Hsien Hsu, Yuanxiang Li and Ting Hu, “Evolvable
hardware design based on a novel simulated annealing in an embedded system”, Concurrency and Computation–Practice
& Experience, Vol. 24, No. 4, pp. 354–370, March 25, 2012.
4. C.K. Goh, K.C. Tan, C.Y. Cheong and Y.S. Ong, “An investigation on noise-induced features in robust evolutionary
multi-objective optimization”, Expert Systems with Applications, Vol. 37, No. 8, pp. 5960–5980, August 2010.
5. K.M. Saridakis and A.J. Dentsoras, “Soft computing in engineering design - A review”, Advanced Engineering Informatics, Vol. 22, No. 2, pp. 202–221, April 2008.
6. C. K. Goh and K. C. Tan, “An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization”,
IEEE Transactions on Evolutionary Computation, Vol. 11, No. 3, pp. 354–381, June 2007.
7. Ashwin Gurnani, Scott Ferguson, Kemper Lewis and Joseph Donndelinger, “A constraint-based approach to feasibility
assessment in preliminary design”, AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing,
Vol. 20, No. 4, pp. 351–367, Fall 2006.
8. Dimo Brockhoff and Eckart Zitzler, “Are All Objectives Necessary? On Dimensionality Reduction in Evolutionary
Multiobjective Optimization”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervós,
L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature - PPSN IX, 9th International Conference,
pp. 533–542, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, September 2006.
9. P.W. Moore and G.K. Venayagamoorthy, “Evolving digital circuits using hybrid particle swarm optimization and differential evolution”, International Journal of Neural Systems, Vol. 16, No. 3, pp. 163–177, June 2006.
10. Giovani Gomez Estrada, “A Note on Designing Logic Circuits Using SAT”, in Andy M. Tyrell, Pauline C. Haddow
and Jim Torresen (Eds), Evolvable Systems: From Biology to Hardware. 5th International Conference, ICES 2003, pp.
410–421, Springer, Lecture Notes in Computer Science, Vol. 2606, Trondheim, Norway, March 2003.
11. Chih-Yung Chen and Rey-Chue Hwang, “A new variable topology for evolutionary hardware design”, Expert Systems
with Applications, Vol. 36, No. 1, pp. 634–642, January 2009.
12. Dimo Brockhoff and Eckart Zitzler, “Objective Reduction in Evolutionary Multiobjective Optimization: Theory and
Applications”, Evolutionary Computation, Vol. 17, No. 2, pp. 135–166, Summer 2009.
• Carlos A. Coello Coello and Alan D. Christiansen, “A Simple Genetic Algorithm for the design of reinforced
concrete beams”, Engineering with Computers, Vol. 13, No. 4, pp. 185–196, 1997.
1. Alfonso Carbonell, Victor Yepes and Fernando Gonzalez-Vidosa, “Automatic design of concrete vaults using iterated
local search and extreme value estimation”, Latin American Journal of Solids and Structures, Vol. 9, No. 6, pp. 675–689,
2012.
2. Francisco J. Martinez-Martin, Fernando Gonzalez-Vidosa, Antonio Hospitaler and Victor Yepes, “Multi-objective optimization design of bridge piers with hybrid heuristic algorithms”, Journal of Zhejiang University–Science A, Vol. 13,
No. 6, pp. 420–432, June 2012.
3. Charles V. Camp and Farah Huq, “CO2 and cost optimization of reinforced concrete frames using a big bang-big crunch
algorithm”, Engineering Structures, Vol. 48, pp. 363–372, March 2013.
4. Jose V. Marti, Fernando Gonzalez-Vidosa, Victor Yepes and Julian Alcala, “Design of prestressed concrete precast road
bridges with hybrid simulated annealing”, Engineering Structures, Vol. 48, pp. 342–352, March 2013.
5. Hasan Tahsin Ozturk and Ahmet Durmusa, “Optimum cost design of RC columns using artificial bee colony algorithm”,
Structural Engineering and Mechanics, Vol. 45, No. 5, pp. 643–654, March 10, 2013.
6. Francisco J. Martinez-Martin, Fernando Gonzalez-Vidosa, Antono Hospitaler and Victor Yepes, “A parametric study of
optimum tall piers for railway bridge viaducts”, Structural Engineering and Mechanics, Vol. 45, No. 6, pp. 723–740,
March 25, 2013.
7. C. Torres-Machi, V. Yepes, J. Alcala and E. Pellicer, “Optimization of high-performance concrete structures by variable
neighborhood search”, International Journal of Civil Engineering, Vol. 11, No. 2A, pp. 90–99, June 2013.
8. M.M. Jahjouh, M.H. Arafa and M.A. Alqedra, “Artificial Bee Colony (ABC) algorithm in the design optimization of
RC continuous beams”, Structural and Multidisciplinary Optimization, Vol. 47, No. 6, pp. 963–979, June 2013.
9. Aleksandar Milajic, Aleksandar Prokic, Dejan Beljakovic and Goran Pejicic, “Quantitative method for evaluating applicability of designed reinforcement pattern”, Tehnicki Vjesnik–Technical Gazette, Vol. 22, No. 1, pp. 119–124, February
2015.
178
10. Charles V. Camp and Alper Akin, “Design of Retaining Walls Using Big Bang-Big Crunch Optimization”, Journal of
Structural Engineering–ASCE, Vol. 138, No. 3, pp. 438–448, March 2012.
11. Charles V. Camp and Andrew Assadollahi, “CO (2) and cost optimization of reinforced concrete footings using a hybrid
big bang-big crunch algorithm”, Structural and Multidisciplinary Optimization, Vol. 48, No. 2, pp. 411–426, August
2013.
12. H.T. Ozturk, Ay. Durmus and Ah. Durmus, “Optimum design of a reinforced concrete beam using artificial bee colony
algorithm”, Computers and Concrete, Vol. 10, No. 3, pp. 295–306, September 2012.
13. M. El Semelawy, A.O. Nassef and A.A. El Damatty, “Design of prestressed concrete flat slab using modern heuristic
optimization techniques”, Expert Systems with Applications, Vol. 39, No. 5, pp. 5758–5766, April 2012.
14. F.J. Martinez, F. Gonzalez-Vidosa and A. Hospitaler, “A parametric study of piers for motorway bridge viaducts”,
Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingenierı́a, Vol. 27, No. 3, pp. 236–250, 2011.
15. A. Carbonell, V. Yepes and F. Gonzalez-Vidosa, “Global best local search applied to the economic design of reinforced
concrete vaults”, Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingenierı́a, Vol. 27, No. 3, pp.
227–235, 2011.
16. Francisco Martinez, Fernando Gonzalez-Vidosa, Antonio Hospitaler and Julian Alcala, “Design of tall bridge piers by
ant colony optimization”, Engineering Structures, Vol. 33, No. 8, pp. 2320–2329, August 2011.
17. Alfonso Carbonell, Fernando Gonzalez-Vidosa and Victor Yepes, “Design of reinforced concrete road vaults by heuristic
optimization”, Advances in Engineering Software, Vol. 42, No. 4, pp. 151–159, April 2011.
18. Cristian Perea, Victor Yepes, Julian Alcala, Antonio Hospitaler and Fernando Gonzalez-Vidosa, “A parametric study of
optimum road frame bridges by threshold acceptance”, Indian Journal of Engineering and Materials Sciences, Vol. 17,
No. 6, pp. 427–437, December 2010.
19. Ignacio Paya-Zaforteza, Victor Yepes, Fernando Gonzalez-Vidosa and Antonio Hospitaler, “On the Weibull cost estimation of building frames designed by simulated annealing”, Meccanica, Vol. 45, No. 5, pp. 693–704, October 10,
2010.
20. Jose V. Marti and Fernando Gonzalez-Vidosa, “Design of prestressed concrete precast pedestrian bridges by heuristic
optimization”, Advances in Engineering Software, Vol. 41, Nos. 7-8, pp. 916–922, July-August 2010.
21. Ignacio Paya, Victor Yepes, Fernando Gonzalez-Vidosa and Antonio Hospitaler, “Multiobjective optimization of concrete
frames by simulated annealing”, Computer-Aided Civil and Infrastructure Engineering, Vol. 23, No. 8, pp. 596–610,
November 2008.
22. Cristian Perea, Julian Alcala, Victor Yepes, Fernando Gonzalez-Vidosa and Antonio Hospitaler, “Design of reinforced
concrete bridge frames by heuristic optimization”, Advances in Engineering Software, Vol. 39, No. 8, pp. 676–688,
August 2008.
23. Victor Yepes, Julian Alcala, Cristian Perea and Fernando Gonzalez-Vidosa, “A parametric study of optimum earthretaining walls by simulated annealing”, Engineering Structures, Vol. 30, No. 3, pp. 821–830, March 2008.
24. D.F. Jones, S.K. Mirrazavi, and M. Tamiz, “Multi-objective meta-heuristics: An overview of the current state-of-the-art”,
European Journal of Operational Research, Vol. 137, No. 1, pp. 1–9, February 2002.
25. M.N.S. Hadi & Y. Arfiadi, “Optimum rigid pavement design by genetic algorithms”, Computers and Structures, Vol. 79,
No. 17, pp. 1617–1624, July 2001.
26. V. Govindaraj and J.V. Ramasamy, “Optimum detailed design of reinforced concrete continuous beams using genetic
algorithms”, Computers & Structures, Vol. 84, Nos. 1–2, pp. 34–48, December 2005.
27. Francisco J. Martinez, Fernando Gonzalez-Vidosa, Antonio Hospitaler and Victor Yepes, “Heuristic optimization of RC
bridge piers with rectangular hollow sections”, Computers & Structures, Vol. 88, Nos. 5-6, pp. 375–386, March 2010.
28. Ignacio Paya-Zaforteza, Victor Yepes, Antonio Hospitaler and Fernando Gonzalez-Vidosa, “CO2-optimization of reinforced concrete frames by simulated annealing”, Engineering Structures, Vol. 31, No. 7, pp. 1501–1508, July 2009.
• Carlos A. Coello Coello, Alan D. Christiansen and Arturo Hernández Aguirre, “Using a New GA-Based
Multiobjective Optimization Technique for the Design of Robot Arms”, Robotica, Vol. 16, No. 4, pp.
401–414, 1998.
1. Robin Chhabra and M. Reza Emami, “A holistic approach to concurrent engineering and its application to robotics”,
Concurrent Engineering–Research and Applications, Vol. 22, No. 1, pp. 48–61, March 2014.
2. B.K. Rout and R.K. Mittal, “Optimal design of manipulator parameter using evolutionary optimization techniques”,
Robotica, Vol. 28, pp. 381–395, Part 3, May 2010.
3. M. Walker and R.E. Smith, “A technique for the multiobjective optimisation of laminated composite structures using
genetic algorithms and finite element analysis”, Composite Structures, Vol. 62, No. 1, pp. 123–128, October 2003.
179
4. S. Ranji Ranjithan, S. Kishan Chetan and Harish K. Dakshina, “Constraint Method-Based Evolutionary Algorithm
(CMEA) for Multiobjective Optimization”, en Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello
& David Corne (Eds.), First International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag,
Zurich, Suiza, pp. 299–313, Marzo de 2001.
5. D.F. Jones, S.K. Mirrazavi, and M. Tamiz, “Multi-objective meta-heuristics: An overview of the current state-of-the-art”,
European Journal of Operational Research, Vol. 137, No. 1, pp. 1–9, February 2002.
6. L.A. Wilson and M.D. Moore, “Cross-pollinating parallel genetic algorithms for multiobjective search and optimization”,
International Journal of Foundations of Computer Science, Vol. 16, No. 2, pp. 261–280, April 2005.
7. A. Meghdari, H.N. Pishkenari, A.L. Gaskarimahalle, S.H. Mahboobi and R. Karimi, “A novel approach for optimal
design of a rover mechanism”, Journal of Intelligent & Robotic Systems, Vol. 44, No. 4, pp. 291–312, December 2005.
8. B.K. Rout and R.K. Mittal, “Optimal manipulator parameter tolerance selection using evolutionary optimization technique”, Engineering Applications of Artificial Intelligence, Vol. 21, No. 4, pp. 509–524, June 2008.
9. B.K. Rout and R.K. Mittal, “Optimal manipulator tolerance design using hybrid evolutionary optimization technique”,
International Journal of Robotics & Automation, Vol. 22, No. 4 pp. 263-271, 2007.
10. B.K. Rout and R.K. Mittal, “Simultaneous selection of optimal parameters and tolerance of manipulator using evolutionary optimization technique”, Structural and Multidisciplinary Optimization, Vol. 40, Nos. 1-6, pp. 513–528, January
2010.
• Carlos A. Coello Coello and Alan D. Christiansen, “Two New GA-based methods for multiobjective optimization”, Civil Engineering and Environmental Systems, Vol. 15, No. 3, pp. 207–243, 1998.
1. Fabien Tricoire, “Multi-directional local search”, Computers & Operations Research, Vol. 39, No. 12, pp. 3089–3101,
December 2012.
2. J.R. Jimenez-Octavio, O. Lopez-Garcia, E. Pilot and A. Carnicero, “Coupled electromechanical optimization of power
transmission”, CMES-Computer Modeling in Engineering & Sciences, Vol. 25, No. 2, pp. 81–97, February 2008.
3. Karl Doerner, Walter J. Gutjahr, Richard F. Hartl, Christine Strauss and Christian Stummer, “Pareto Ant Colony
Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection”, Annals of Operations Research, Vol.
131 Nos. 1–4, pp. 79–99, October 2004.
4. D.F. Jones, S.K. Mirrazavi, and M. Tamiz, “Multi-objective meta-heuristics: An overview of the current state-of-the-art”,
European Journal of Operational Research, Vol. 137, No. 1, pp. 1–9, February 2002.
5. Matthias Ehrgott and Xavier Gandibleux, “A Survey and Annotated Bibliography of Multiobjective Combinatorial
Optimization”, OR Spektrum, Vol. 22, pp. 425–460, 2000.
6. R. Kicinger, T. Arciszewski and K. De Jong, “Evolutionary Computation and Structural Design: A Survey of the
State-of-the-art”, Computers & Structures, Vol. 83, Nos. 23–24, pp. 1943–1978, September 2005.
7. N. Ozturk, A.R. Yildiz, N. Kaya and F. Ozturk, “Neuro-genetic design optimization framework to support the integrated
robust design optimization process in CE”, Concurrent Engineering–Research and Applications, Vol. 14, No. 1, pp. 5–16,
March 2006.
8. Po-Wen Chiu and Christina L. Bloebaum, “Hyper-Radial Visualization (HRV) method with range-based preferences for
multi-objective decision making”, Structural and Multidisciplinary Optimization, Vol. 40, Nos. 1-6, pp. 97–115, January
2010.
• Carlos A. Coello Coello, Alan D. Christiansen and Arturo Hernández Aguirre, “Towards Automated Evolutionary Design of Combinational Circuits”, Computers and Electrical Engineering. An International Journal,
Vol. 27, No. 1, pp. 1–28, January 2001.
1. S. Karakatic, V. Podgorelec and M. Hericko, “Optimization of Combinational Logic Circuits with Genetic Programming”,
Elektronika Ir Elektrotechnika, Vol. 19, No. 7, pp. 86–89, 2013.
2. Mehdi Anjomshoa, Ali Mahani and Salahedin Sadeghifard, “A new automated design and optimization method of CMOS
logic circuits based on Modified Imperialistic Competitive Algorithm”, Applied Soft Computing, Vol. 21, pp. 423–432,
August 2014.
3. J. Wang, Q.S. Chen and C.H. Lee, “Design and implementation of a virtual reconfigurable architecture for different
applications of intrinsic evolvable hardware”, IET Computers and Digital Techniques, Vol. 2, No. 5, pp. 386–400,
September 2008.
4. N. Nedjah and L.D. Mourelle, “A comparison of two circuit representations for evolutionary digital circuit design”, in
Innovations in Applied Artificial Intelligence, Springer-Verlag, Lecture Notes in Artificial Intelligence, Vol. 3029, pp.
594–604, 2004.
180
5. N. Nedjah and L.D. Mourelle, “Evolvable hardware using genetic programming”, Intelligent Data Engineering and
Automated Learning, Springer, Lecture Notes in Computer Science, Vol. 2690, pp. 321–328, 2003.
6. Igor Baradavka and Tatiana Kalganova, “Assembling Strategies in Extrinsic Evolvable Hardware with Bidirectional
Incremental Evolution”, in Conor Ryan, Terence Soule, Maarten Keijzer, Edward Tsang, Riccardo Poli and Ernesto
Costa (eds.), Proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, pp. 276–285, Springer,
Lecture Notes in Computer Science, Vol. 2610, April 2003.
7. N. Nedjah and L.D.M. Mourelle, “Pareto-optimal hardware for digital circuits using SPEA”, in Innovations in Applied
Artificial Intelligence, Springer-Verlag, Lecture Notes in Artificial Intelligence Vol. 3533, pp. 594–604, 2005.
8. Emanuele Stomeo, Tatiana Kalganova and Cyrille Lambert, “Generalized Disjunction Decomposition for Evolvable
Hardware”, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 36, No. 5, pp. 1024–
1043, October 2006.
9. W. Pedrycz, M. Reformat and K.W. Li, “OR/AND neurons and the development of interpretable logic models”, IEEE
Transactions on Neural Networks, Vol. 17, No. 3, pp. 636–658, May 2006.
10. Houjun Liang, Wenjian Luo and Xufa Wang, “A three-step decomposition method for the evolutionary design of sequential logic circuits”, Genetic Programming and Evolvable Machines, Vol. 10, No. 3, pp. 231–262, September 2009.
11. Chih-Yung Chen and Rey-Chue Hwang, “A new variable topology for evolutionary hardware design”, Expert Systems
with Applications, Vol. 36, No. 1, pp. 634–642, January 2009.
• Carlos A. Coello Coello and Gregorio Toscano Pulido, “Multiobjective Structural Optimization using a MicroGenetic Algorithm”, Structural and Multidisciplinary Optimization, Vol. 30, No. 5, pp. 388–403, November
2005.
1. Ali Sadollah, Hadi Eskandar and Joong Hoon Kim, “Water cycle algorithm for solving constrained multi-objective
optimization problems”, Applied Soft Computing, Vol. 27, pp. 279–298, February 2015.
2. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A multi-objective evolutionary algorithm-based ensemble optimizer
for feature selection and classification with neural network models”, Neurocomputing, Vol. 125, pp. 217–228, February
11, 2014.
3. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
4. K. Lakshmi and A. Rama Mohan Rao, “Multi-objective optimal design of laminate composite shells and stiffened shells”,
Structural Engineering and Mechanics, Vol. 43, No. 6, pp. 771–794, September 25, 2012.
5. Panos G. Georgopoulos, Alan F. Sasso, Sastry S. Isukapalli, Paul J. Lioy, Daniel A. Vallero, Miles Okino and Larry Reiter,
“Reconstructing population exposures to environmental chemicals from biomarkers: Challenges and opportunities”,
Journal of Exposure Science and Environmental Epidemiology, Vol. 19, No. 2, pp. 149–171, February 2009.
6. Wenyin Gong, Zhihua Cai and Li Zhu, “An efficient multiobjective differential evolution algorithm for engineering
design”, Structural and Multidisciplinary Optimization, Vol. 38, No. 2, pp. 137–157, April 2009.
7. Ali R. Yildiz, Nursel Ozturk, Necmettin Kaya and Ferruh Ozturk, “Hybrid multi-objective shape design optimization
using Taguchi’s method and genetic algorithm”, Structural and Multidisciplinary Optimization, Vol. 34, No. 4, pp.
317–332, October 2007.
8. Andras Szollos, Miroslav Smid and Jaroslav Hajek, “Aerodynamic optimization via multi-objective micro-genetic algorithm with range adaptation, knowledge-based reinitialization, crowding and epsilon-dominance”, Advances in Engineering Software, Vol. 40, No. 6, pp. 419–430, June 2009.
• Carlos A. Coello Coello, Rosa Laura Zavala Gutiérrez, Benito Mendoza Garcı́a and Arturo Hernández
Aguirre, “Automated Design of Combinational Logic Circuits using the Ant System”, Engineering Optimization, Vol. 34, No. 2, pp. 109–127, March 2002.
1. Giovani Gomez Estrada, “A Note on Designing Logic Circuits Using SAT”, in Andy M. Tyrell, Pauline C. Haddow
and Jim Torresen (Eds), Evolvable Systems: From Biology to Hardware. 5th International Conference, ICES 2003, pp.
410–421, Springer, Lecture Notes in Computer Science, Vol. 2606, Trondheim, Norway, March 2003.
2. Jenn-Long Liu, “Rank-based ant colony optimization applied to dynamic traveling salesman problems”, Engineering
Optimization, Vol. 37, No. 8, pp. 831–847, December 2005.
3. Yongqing Zhang, Xiang Huang, Xu Jiang and Shaobin Huang, “Modified Ant Colony Optimization Algorithm and its
Application in Variable Selection of QSAR of Polychlorinated Organic Compouds”, Journal of Theoretical & Computational Chemistry, Vol. 8, No. 5, pp. 783–798, October 2009.
4. Kwee Kim Lim, Yew-Soon Ong, Meng Hiot Lim, Xianshun Chen and Amit Agarwal, “Hybrid ant colony algorithms for
path planning in sparse graphs”, Soft Computing, Vol. 12, No. 10, pp. 981–994, August 2008.
181
• Ricardo Landa Becerra and Carlos A. Coello Coello, “Cultured differential evolution for constrained optimization”, Computer Methods in Applied Mechanics and Engineering, Vol. 195, Nos. 33–36, pp. 4303–4322,
July 1, 2006.
1. Min-Yuan Cheng and Nhat-Duc Hoang, “Groutability Estimation of Grouting Processes with Microfine Cements Using
an Evolutionary Instance-Based Learning Approach”, Journal of Computing in Civil Engineering, Vol. 28, No. 4, Article
Number: 04014014, July 2014.
2. Neha S. Patankar, Anand J. Kulkarni, Kang Tai, T.D. Ghate and A.R. Parvate, “Multi-criteria probability collectives”,
International Journal of Bio-Inspired Computation, Vol. 6, No. 6, pp. 369–383, 2014.
3. Amir H. Gandomi, “Interior search algorithm (ISA): A novel approach for global optimization”, ISA Transactions, Vol.
53, No. 4, pp. 1168–1183, July 2014.
4. Gexiang Zhang, Jixiang Cheng, Marian Gheorghe and Qi Meng, “A hybrid approach based on differential evolution
and tissue membrane systems for solving constrained manufacturing parameter optimization problems”, Applied Soft
Computing, Vol. 13, No. 3, pp. 1528–1542, March 2013.
5. Wenyin Gong, Zhihua Cai and Dingwen Liang, “Engineering optimization by means of an improved constrained differential evolution”, Computer Methods in Applied Mechanics and Engineering, Vol. 268, pp. 884–904, January 1,
2014.
6. S.U. Khan, I.M. Qureshi, F. Zaman, B. Shoaib, A. Naveed and A. Basit, “Correction of Faulty Sensors in Phased Array
Radars Using Symmetrical Sensor Failure Technique and Cultural Algorithm with Differential Evolution”, Scientific
World Journal, Article Number: 852539, 2014.
7. Wen Long, Ximing Liang, Yafei Huang and Yixiong Chen, “A hybrid differential evolution augmented Lagrangian
method for constrained numerical and engineering optimization”, Computer-Aided Design, Vol. 45, No. 12, pp. 1562–
1574, December 2013.
8. Shijun Zhai and Ting Jiang, “ Target detection and classification by measuring and processing bistatic UWB radar
signal”, Measurement, Vol. 47, pp. 547–557, January 2014.
9. Chao Ma, Jijian Lian and Junna Wang, “ Short-term optimal operation of Three-gorge and Gezhouba cascade hydropower
stations in non-flood season with operation rules from data mining”, Energy Conversion and Management, Vol. 65, pp.
616–627, January 2013.
10. Matej Crepinsek, Shih-Hsi Liu and Marjan Mernik, “Exploration and Exploitation in Evolutionary Algorithms: A
Survey”, ACM Computing Surveys, Vol. 45, No. 3, Article Number: 35, June 2013.
11. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
12. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
13. M.M. Ali and W.X. Zhu, “A penalty function-based differential evolution algorithm for constrained global optimization”,
Computational Optimization and Applications, Vol. 54, No. 3, pp. 707–739, April 2013.
14. Guanbo Jia, Yong Wang, Zixing Cai and Yaochu Jin, “An improved (µ + λ)-constrained differential evolution for
constrained optimization”, Information Sciences, Vol. 222, pp. 302–322, February 10, 2013.
15. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
16. Yang Sun, Lingbo Zhang and Xhingsheng Gu, “A hybrid co-evolutionary cultural algorithm based on particle swarm
optimization for solving global optimization problems”, Neurocomputing, Vol. 98, pp. 76–89, December 3, 2012.
17. Matej Crepinsek, Shih-Hsi Liu and Luka Mernik, “A note on teaching-learning-based optimization algorithm”, Information Sciences, Vol. 212, pp. 79–93, December 1, 2012.
18. Ali Wagdy Mohamed and Hegazy Zaher Sabry, “Constrained optimization based on modified differential evolution
algorithm”, Information Sciences, Vol. 194, pp. 171–208, July 1, 2012.
19. Amir Hossein Gandomi, Xin-She Yang, Siamak Talatahari and Suash Deb, “Coupled eagle strategy and differential
evolution for unconstrained and constrained global optimization”, Computers & Mathematics with Applications, Vol.
63, No. 1, pp. 191–200, January 2012.
20. Glauber Souto dos Santos, Luiz Guilherme Luvizotto, Viviana Cocco Mariani and Leandro dos Santos Coelho, “Least
squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification
of a thermal process”, Expert Systems with Applications, Vol. 39, No. 5, pp. 4805–4812, April 2012.
21. Yong Wang and Zixing Cai, “Combining Multiobjective Optimization with Differential Evolution to Solve Constrained
Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 16, No. 1, pp. 117–134, February
2012.
182
22. A. Slowik, “Application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude
characteristics and finite bit word length”, Bulletin of the Polish Academy of Sciences–Technical Sciences, Vol. 59, No.
2, pp. 125–135, June 2011.
23. Radovan R. Bulatovic and Stevan R. Dordevic, “Control of the optimum synthesis process of a four-bar linkage whose
point on the working member generates the given path”, Applied Mathematics and Computation, Vol. 217, No. 23, pp.
9765–9778, August 1, 2011.
24. Adam Slowik, “Application of an Adaptive Differential Evolution Algorithm With Multiple Trial Vectors to Artificial
Neural Network Training”, IEEE Transactions on Industrial Electronics, Vol. 58, No. 8, pp. 3160–3167, August 2011.
25. R.V. Rao, V.J. Savsani and D.P. Vakharia, “Teaching-learning-based optimization: A novel method for constrained
mechanical design optimization problems”. Computer-Aided Design, Vol. 43, No. 3, pp. 303–315, March 2011.
26. Yong Wang and Zixing Cai, “Constrained Evolutionary Optimization by Means of (µ + λ)-Differential Evolution and
Improved Adaptive Trade-Off Model”, Evolutionary Computation, Vol. 19, No. 2, 249–285, Summer 2011.
27. Yi-nan Guo, Jian Cheng, Yuan-yuan Cao and Yong Lin, “A novel multi-population cultural algorithm adopting knowledge migration”, Soft Computing, Vol. 15, No. 5, pp. 897–905, May 2011.
28. Zhenxiao Gao, Tianyuan Xiao and Wenhui Fan, “Hybrid differential evolution and Nelder-Mead algorithm with reoptimization”, Soft Computing, Vol. 15, No. 3, pp. 581–594, March 2011.
29. Swagatam Das and Ponnuthurai Nagaratnam Suganthan, “Differential Evolution: A Survey of the State-of-the-Art”,
IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, pp. 27–54, February 2011.
30. Angela Vincenti, Mohammad Reza Ahmadian and Paolo Vannucci, “BIANCA: a genetic algorithm to solve hard combinatorial optimisation problems in engineering”, Journal of Global Optimization, Vol. 48, No. 3, pp. 399–421, November
2010.
31. Efren Mezura-Montes, Mariana Miranda-Varela and Rubi del Carmen Gomez-Ramon, “Differential evolution in constrained numerical optimization: An empirical study”, Information Sciences, Vol. 180, No. 22, pp. 4223–4262, November
15, 2010.
32. Hui Qin, Jianzhong Zhou, Youlin Lu, Yinghai Li and Yongchuan Zhang, “Multi-objective Cultured Differential Evolution
for Generating Optimal Trade-offs in Reservoir Flood Control Operation”, Water Resources Management, Vol. 24, No.
11, pp. 2611–2632, September 2010.
33. Chun-Yin Wu and Ko-Ying Tseng, “A nonlinear interval-based optimization method with local-densifying approximation
technique”, Structural and Multidisciplinary Optimization, Vol. 42, No. 4, pp. 575–590, October 2010.
34. Ali Haydar Kayhan, Huseyin Ceylan, M. Tamer Ayvaz and Gurhan Gurarslan, “PSOLVER: A new hybrid particle swarm
optimization algorithm for solving continuous optimization problems”, Expert Systems with Applications, Vol. 37, No.
10, pp. 6798–6808, October 2010.
35. Ioannis G. Tsoulos, “Solving constrained optimization problems using a novel genetic algorithm”, Applied Mathematics
and Computation, Vol. 208, No. 1, pp. 273–283, February 1, 2009.
36. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
37. A. Slowik and A. Bialko, “Design of IIR digital filters with non-standard characteristics using differential evolution
algorithm”, Bulletin of the Polish Academy of Sciences–Technical Sciences, Vol. 55, No. 4, pp. 359–363, December
2007.
38. Erwie Zahara and Chia-Hsin Hu, “Solving constrained optimization problems with hybrid particle swarm optimization”,
Engineering Optimization, Vol. 40, No. 11, pp. 1031–1049, November 2008.
39. S.Y. Chong and M. Tremayne, “Combined optimization using cultural and differential evolution: application to crystal
structure solution from powder diffraction data”, Chemical Communications, Vol. 39, pp. 4078–4080, 2006.
40. Hui Liu, Zixing Cai and Yong Wang, “Hybridizing particle swarm optimization with differential evolution for constrained
numerical and engineering optimization”, Applied Soft Computing, Vol. 10, No. 2, pp. 629–640, March 2010.
41. Rajkumar Roy, Srichand Hinduja and Roberto Teti, “Recent advances in engineering design optimisation: Challenges
and future trends”, CIRP Annals-Manufacturing Technology, Vol. 57, No. 2, pp. 697–715, 2008.
42. Zhun Fan, Jinchao Liu, Torben Sorensen and Pan Wang, “Improved Differential Evolution Based on Stochastic Ranking
for Robust Layout Synthesis of MEMS Components”, IEEE Transactions on Industrial Electronics, Vol. 56, No. 4, pp.
937–948, April 2009.
43. Leandro dos Santos Coelho, Rodrigo Clemente Thom Souza, Viviana Cocco Mariani, “Improved differential evolution
approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems”, Mathematics and Computers in Simulation, Vol. 79, No. 10, pp. 3136–3147, June 2009.
183
• Carlos A. Coello Coello, Filiberto Santos Hernández and Francisco Alonso Farrera, “Optimal Design of
Reinforced Concrete Beams using Genetic Algorithms”, Expert Systems with Applications, Vol. 12, No. 1,
pp. 101–108, January 1997.
1. Anan Nimtawat and Pruettha Nanakorn, “A genetic algorithm for beam-slab layout design of rectilinear floors”, Engineering Structures, Vol. 32, No. 11, pp. 3488–3500, November 2010.
2. Mustafa Kaya, “The effects of two new crossover operators on genetic algorithm performance”, Applied Soft Computing,
Vol. 11, No. 1, pp. 881–890, January 2011.
3. K.M. Zhao & J.K. Lee, “Generation of cyclic stress-strain curves for sheet metals”, Journal of Engineering Materials
and Technology—Transactions of the ASME, Vol. 123, No. 4, pp. 391–397, October 2001.
4. V.C. de Castilho, M.D. Nicoletti and M.K. El Debs, “An investigation of the use of three selection-based genetic
algorithm families when minimizing the production cost of hollow core slabs”, Computer Methods in Applied Mechanics
and Engineering, Vol. 194, Nos. 45–47, pp. 4651–4667, 2005.
5. M.A. Abido, “Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem”, IEEE Transactions on
Evolutionary Computation, Vol. 10, No. 3, pp. 315–329, June 2006.
6. M. Nehdi and T. Greenough, “Modeling shear capacity of RC slender beams without stirrups using genetic algorithms”,
Smart Structures and Systems, Vol. 3, No. 1, pp. 51–68, January 2007.
7. Vanessa Cristina de Castilho, Mounir Khalil El Debs and Maria do Carmo Nicoletti, “Using a modified genetic algorithm
to minimize the production costs for slabs of precast prestressed concrete joists”, Engineering Applications of Artificial
Intelligence, Vol. 20, No. 4, pp. 519–530, June 2007.
8. Anan Nimtawat and Pruettha Nanakorn, “Automated layout design of beam-slab floors using a genetic algorithm”,
Computers & Structures, Vol. 87, Nos. 21-22, pp. 1308–1330, November 2009.
• Carlos A. Coello Coello and Ricardo Landa Becerra, “Efficient Evolutionary Optimization through the use
of a Cultural Algorithm”, Engineering Optimization, Vol. 36, No. 2, pp. 219–236, April 2004.
1. Haipeng Kong, Li Ni and Yuzhong Shen, “Adaptive double chain quantum genetic algorithm for constrained optimization
problems”, Chinese Journal of Aeronautics, Vol. 28, No. 1, pp. 214–228, February 2015.
2. Neha S. Patankar, Anand J. Kulkarni, Kang Tai, T.D. Ghate and A.R. Parvate, “Multi-criteria probability collectives”,
International Journal of Bio-Inspired Computation, Vol. 6, No. 6, pp. 369–383, 2014.
3. Rommel G. Regis, “Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization
Using Radial Basis Functions”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 326–347, June
2014.
4. Zhenzhou Hu, Xinye Cai and Zhun Fan, “An improved memetic algorithm using ring neighborhood topology for constrained optimization”, Soft Computing, Vol. 18, No. 10, pp. 2023–2041, October 2014.
5. Chunjiang Zhang, Xinyu Li, Liang Gao and Qing Wu, “An improved electromagnetism-like mechanism algorithm for
constrained optimization”, Expert Systems with Applications, Vol. 40, No. 14, pp. 5621–5634, October 15, 2013.
6. Yongquan Zhou, Guo Zhou and Junl Zhang, “A Hybrid Glowworm Swarm Optimization Algorithm for Constrained
Engineering Design Problems”, Applied Mathematics & Information Sciences, Vol. 7, No. 1, pp. 379–388, January
2013.
7. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
8. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
9. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
10. Ali Wagdy Mohamed and Hegazy Zaher Sabry, “Constrained optimization based on modified differential evolution
algorithm”, Information Sciences, Vol. 194, pp. 171–208, July 1, 2012.
11. Jia-qing Zhao, Ling Wang, Pan Zeng and Wen-hui Fan, “An effective hybrid genetic algorithm with flexible allowance
technique for constrained engineering design optimization”, Expert Systems with Applications, Vol. 39, No. 5, pp.
6041–6051, April 2012.
12. S.O. Degertekin, “Improved harmony search algorithms for sizing optimization of truss structures”, Computers & Structures, Vol. 92-93, pp. 229–241, February 2012.
13. Sungho Mun and Yoon-Ho Cho, “Modified harmony search optimization for constrained design problems”, Expert Systems with Applications, Vol. 39, No. 1, pp. 419–423, January 2012.
184
14. F. Jolai, J. Razmi and N.K.M. Rostami, “A fuzzy goal programming and meta heuristic algorithms for solving integrated
production: distribution planning problem”, Central European Journal of Operations Research, Vol. 19, No. 4, pp. 547–
569, December 2011.
15. Lei Gao and Atakelty Hailu, “Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems”, International Journal of Computational Intelligence Systems, Vol. 3, No. 6, pp. 832–842, December
2010.
16. Angus F.M. Huang, Stephen J.H. Yang, Minhong Wang and Jeffrey J.P. Tsai, “Improving fuzzy knowledge integration
with particle swarmoptimization”, Expert Systems with Applications, Vol. 37, No. 12, pp. 8770–8783, December 2010.
17. Ling Wang and Ling-po Li, “An effective differential evolution with level comparison for constrained engineering design”,
Structural and Multidisciplinary Optimization, Vol. 41, No. 6, pp. 947–963, June 2010.
18. T.-H. Kim, I. Maruta and T. Sugie, “A simple and efficient constrained particle swarm optimization and its application
to engineering design problems”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical
Engineering Science, Vol. 224, No. C2, pp. 389–400, 2010.
19. Ali Haydar Kayhan, Huseyin Ceylan, M. Tamer Ayvaz and Gurhan Gurarslan, “PSOLVER: A new hybrid particle swarm
optimization algorithm for solving continuous optimization problems”, Expert Systems with Applications, Vol. 37, No.
10, pp. 6798–6808, October 2010.
20. Erwie Zahara and Yi-Tung Kao, “Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained
engineering design problems”, Expert Systems with Applications, Vol. 36, No. 2, pp. 3880–3886, Part 2, March 2009.
21. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained optimization based on hybrid evolutionary algorithm
and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol. 37, No. 4, pp. 395–413,
January 2009.
22. Qie He and Ling Wang, “A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization”,
Applied Mathematics and Computation, Vol. 186, No. 2, pp. 1407–1422, March 15, 2007.
23. S.Y. Chong and M. Tremayne, “Combined optimization using cultural and differential evolution: application to crystal
structure solution from powder diffraction data”, Chemical Communications, Vol. 39, pp. 4078–4080, 2006.
• Antonio López Jaimes and Carlos A. Coello Coello, “MRMOGA: A New Parallel Multi-Objective Evolutionary Algorithm Based on the Use of Multiple Resolutions”, Concurrency and Computation: Practice and
Experience, Vol. 19, No. 4, pp. 397–441, March 25, 2007.
1. Yong Zhang, Dun-Wei Gong and Na Gong, “Multi-Objective Optimization Problems Using Cooperative Evolvement
Particle Swarm Optimizer”, Journal of Computational and Theoretical Nanoscience, Vol. 10, No. 3, pp. 655-663, March
2013.
2. Martin Pilat and Roman Neruda, “Aggregate meta-models for evolutionary multiobjective and many-objective optimization”, Neurocomputing, Vol. 116, pp. 392–402, September 20, 2013.
3. Pavel Kroemer, Jan Platos and Vaclav Snasel, “Data Parallel density-based genetic clustering on CUDA Architecture”,
Concurrency and Computation–Practice & Experience, Vol. 26, No. 5, pp. 1097–1112, April 10, 2014.
4. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionary
searches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,
March 2015.
5. Enrique Alba, Gabriel Luque and Sergio Nesmachnow, “Parallel metaheuristics: recent advances and new trends”,
International Transactions in Operational Research, Vol. 20, No. 1, pp. 1–48, January 2013.
6. Yong Zhang, Dun-wei Gong and Zhong-hai Ding, “Handling multi-objective optimization problems with a multi-swarm
cooperative particle swarm optimizer”, Expert Systems with Applications, Vol. 38, No. 11, pp. 13933–13941, October
2011.
7. K. Mitra, “Genetic algorithms in polymeric material production, design, processing and other applications: a review”,
International Materials Review, Vol. 53, No. 5, pp. 275–297, September 2008.
8. Dong-Wook Lee, Sang-Wook Seo and Kwee-Bo Sim, “Online evolution for cooperative behavior in group robot systems”,
International Journal of Control Automation and Systems, Vol. 6, No. 2, pp. 282–287, April 2008.
• Jorge Mendoza, Dario Morales, Rodrigo López, Enrique López, Jean-Claude Vannier and Carlos A. Coello
Coello, “Multi-objective Location of Automatic Voltage Regulators in a Radial Distribution Network Using
a Micro Genetic Algorithm”, IEEE Transactions on Power Systems, Vol. 22, No. 1, pp. 404–411, February
2007.
1. John F. Franco, Marcos J. Rider, Marina Lavorato and Ruben Romero, “A mixed-integer LP model for the optimal
allocation of voltage regulators and capacitors in radial distribution systems”, International Journal of Electrical Power
& Energy Systems, Vol. 48, pp. 123–130, June 2013.
185
2. Yong Tian, Bizhong Xia, Wei Sun, Zhihui Xu and Weiwei Zheng, “Modeling and global maximum power point tracking
for photovoltaic system under partial shading conditions using modified particle swarm optimization algorithm”, Journal
of Renewable and Sustainable Energy, Vol. 6, No. 6, Article Number: 063117, November 2014.
3. Reza Ebrahimi, Mehdi Ehsan and Hassan Nouri, “U-shaped energy loss curves utilization for distributed generation
optimization in distribution networks”, Journal of Zhejiang University-Science C-Computers & Electronics, Vol. 14, No.
11, pp. 887–898, November 2013.
4. Indranil Pan and Saptarsh Das, “Frequency domain design of fractional order PID controller for AVR system using
chaotic multi-objective optimization”, International Journal of Electrical Power & Energy Systems, Vol. 51, pp. 106–
118, October 2013.
5. Choo Jun Tan, Chee Peng Lim and Yu-N Cheah, “A Modified micro Genetic Algorithm for undertaking Multi-Objective
Optimization Problems”, Journal of Intelligent & Fuzzy Systems, Vol. 24, No. 3, pp. 483–495, 2013.
6. R. Ebrahimi, M. Ehsan and H. Nouri, “Effect of Customer Demand Type on Optimization of Distributed Generation
for Minimization of Energy Loss”, International Review of Electrical Engineering-IREE, Part B, Vol. 7, No. 2, pp.
4113–4122, March-April 2012.
7. Taher Niknam, Mohammad Rasoul Narimani and Rasoul Azizipanah-Abarghooee, “A Multi-objective Fuzzy Adaptive
PSO Algorithm for Location of Automatic Voltage Regulators in Radial Distribution Networks”, International Journal
of Control Automation and Systems, Vol. 10, No. 4, pp. 772–777, August 2012.
8. Masato Ishida, Takeshi Nagata, Hiroshi Saiki, Ikuhiko Shimada and Ryousuke Hatano, “A Multiagent-Based Cooperative
Voltage and Reactive Power Control”, Electrical Engineering in Japan, Vol. 181, No. 2, pp. 20–28, November 15, 2012.
9. D. Silas Stephen, M. Devesh Raj and P. Somasundaram, “Solution for Multi-Objective Reactive Power Optimization
Problem Using Fuzzified Particle Swarm Optimization Algorithm”, International Review of Electrical Engineering–IREE,
Vol. 7, No. 1, Part b, pp. 3486–3494, January-February 2012.
10. Jordan Radosavljevic, Miroljub Jevtic and Dardan Klimenta, “Optimal Seasonal Voltage Control in Rural Distribution
Networks with Distributed Generators”, Journal of Electrical Engineering–Elektrotechnicky Casopis, Vol. 61, No. 6, pp.
321–331, November-December 2010.
11. I. Ziari, G. Ledwich and A. Ghosh, “Optimal voltage support mechanism in distribution networks”, IET Generation
Transmission & Distribution, Vol. 5, No. 1, pp. 127–135, January 2011.
12. Benemar Alencar de Souza and Angelo Marcio Formiga de Almeida, “Multiobjective Optimization and Fuzzy Logic
Applied to Planning of the Volt/Var Problem in Distributions Systems”, IEEE Transactions on Power Systems, Vol.
25, No. 3, pp. 1274–1281, August 2010.
13. Takeshi Nagata, Hiroshi Saeki, Masahiro Utatani, Yoshiki Nakachi and Ryousuke Hatano, “Multi-Agent Cooperative
Voltage and Reactive Power Control”, Electrical Engineering in Japan, Vol. 174, No. 1, pp. 25–32, January 15, 2010.
14. M. Varadarajan and K.S. Sworup, “Solving multi-objective optimal power flow Using differential evolution”, IET Generation Transmission & Distribution, Vol. 2, No. 5, pp. 720–730, September 2008.
15. Deependra Singh, Devender Singh and K.S. Verma, “Multiobjective Optimization for DG Planning With Load Models”,
IEEE Transactions on Power Systems, Vol. 24, No. 1, pp. 427–436, February 2009.
• Margarita Reyes-Sierra and Carlos A. Coello Coello, “Multi-Objective Particle Swarm Optimizers: A Survey
of the State-of-the-Art”, International Journal of Computational Intelligence Research, Vol. 2, No. 3, pp.
287–308, 2006.
1. Feizi E. Ashtiani, M.H. Niksokhan and M. Ardestani, “Multi-objective Waste Load Allocation in River System by
MOPSO Algorithm”, International Journal of Environmental Research, Vol. 9, No. 1, pp. 69–76, Winter 2015.
2. Ruby L.V. Moritz, Enrico Reich, Maik Schwarz, Matthias Bernt and Martin Middendorf, “Refined ranking relations for
selection of solutions in multi objective metaheuristics”, European Journal of Operational Research, Vol. 243, No. 2, pp.
454–464, June 1, 2015.
3. Kazuhiro Izui, Takayuki Yamada, Shinji Nishiwaki and Kazuto Tanaka, “Multiobjective optimization using an aggregative gradient-based method”, Structural and Multidisciplinary Optimization, Vol. 51, No. 1, pp. 173–182, January
2015.
4. Yu-Bin Zhong, Yi Xiang and Hai-Lin Liu, “A multi-objective artificial bee colony algorithm based on division of the
searching space”, Applied Intelligence, Vol. 41, No. 4, pp. 987–1011, December 2014.
5. Wang Hu and Gary G. Yen, “Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate
System”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 1–18, February 2015.
6. Mohammad Mortazavi-Naeini, George Kuczera and Lijie Cui, “Efficient multi-objective optimization methods for computationally intensive urban water resources models”, Journal of Hydroinformatics, Vol. 17, No. 1, pp. 36–55, 2015.
186
7. Yong Zhang, Dun-Wei Gong and Na Gong, “Multi-Objective Optimization Problems Using Cooperative Evolvement
Particle Swarm Optimizer”, Journal of Computational and Theoretical Nanoscience, Vol. 10, No. 3, pp. 655-663, March
2013.
8. Ching-Tang Hsieh and Chia-Shing Hu, “Fingerprint Recognition by Multi-objective Optimization PSO Hybrid with
SVM”, Journal of Applied Research and Technology, Vol. 12, No. 6, pp. 1014–1024, December 2014.
9. Ya-zhong Luo and Li-ni Zhou, “Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization”,
Mathematical Problems in Engineering, Article Number: 823659, 2014.
10. Amir Nejat, Pooya Mirzabeygi and Masoud Shariat Panahi, “Airfoil shape optimization using improved Multiobjective
Territorial Particle Swarm algorithm with the objective of improving stall characteristics”, Structural and Multidisciplinary Optimization, Vol. 49, No. 6, pp. 953–967, June 2014.
11. I. Montalvo, J. Izquierdo, R. Perez-Garcia and M. Herrera, “Water Distribution System Computer-Aided Design by
Agent Swarm Optimization”, Computer-Aided Civil and Infrastructure Engineering, Vol. 29, No. 6, pp. 433–448, July
2014.
12. Arup Ratan Bhowmik and A.K. Chakraborty, “Solution of optimal power flow using nondominated sorting multi objective
gravitational search algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 62, pp. 323–334,
November 2014.
13. Wali Khan Mashwani and Abdellah Salhi, “Multiobjective memetic algorithm based on decomposition”, Applied Soft
Computing, Vol. 21, pp. 221–243, August 2014.
14. Mengqi Hu, Jeffery D. Weir and Teresa Wu, “An augmented multi-objective particle swarm optimizer for building cluster
operation decisions”, Applied Soft Computing, Vol. 25, pp. 347–359, December 2014.
15. Rajesh Jha, Prodip Kumar Sen and Nirupam Chakraborti, “Multi-Objective Genetic Algorithms and Genetic Programming Models for Minimizing Input Carbon Rates in a Blast Furnace Compared with a Conventional Analytic Approach”,
Steel Research International, Vol. 85, No. 2, pp. 219–232, February 2014.
16. Ping-Che Hsiao, Tsung-Che Chiang and Li-Chen Fu, “Static and dynamic minimum energy broadcast problem in wireless
ad-hoc networks: A PSO-based approach and analysis”, Applied Soft Computing, Vol. 13, No. 12, pp. 4786–4801,
December 2013.
17. Xinyu Shao, Weiqi Liu, Qiong Liu and Chaoyong Zhang, “Hybrid discrete particle swarm optimization for multi-objective
flexible job-shop scheduling problem”, International Journal of Advanced Manufacturing Technology, Vol. 67, Nos. 9-12,
pp. 2885–2901, August 2013.
18. Kian Sheng Lim, Zuwairie Ibrahim, Salinda Buyamin, Anita Ahmad, Faradila Naim, Kamarul Hawari Ghazali, Norrima Mokhtar, “Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions”,
Scientific World Journal, Article Number: 510763, 2013.
19. Kaveh Khalili-Damghani, Amir-Reza Abtahi and Madjid Tavana, “A new multi-objective particle swarm optimization
method for solving reliability redundancy allocation problems”, Reliability Engineering & System Safety, Vol. 111, pp.
58–75, March 2013.
20. Xin-She Yang, Mehmet Karamanoglu and Xingshi He, “Flower pollination algorithm: A novel approach for multiobjective
optimization”, Engineering Optimization, Vol. 46, No. 9, pp. 1222–1237, September 2, 2014.
21. Shan Cheng, Min-you Chen, Rong-jong Wai and Fang-zong Wang, “Optimal placement of distributed generation units
in distribution systems via an enhanced multi-objective particle swarm optimization algorithm”, Journal of Zhejiang
University–Science C–Computers & Electronics, Vol. 15, No. 4, pp. 300–311, April 2014.
22. Nantiwat Pholdee and Sujin Bureerat, “Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design”, Engineering Optimization, Vol. 46, No. 8, pp. 1032–1051, August 3, 2014.
23. Yu-Jun Zheng, Hai-Feng Ling, Jin-Yun Xue and Sheng-Yong Chen, “Population Classification in Fire Evacuation: A
Multiobjective Particle Swarm Optimization Approach”, IEEE Transactions on Evolutionary Computation, Vol. 18, No.
1, pp. 70–81, February 2014.
24. Kalyanmoy Deb and Nikhil Padhye, “Enhancing performance of particle swarm optimization through an algorithmic
link with genetic algorithms”, Computational Optimization and Applications, Vol. 57, No. 3, pp. 761–794, April 2014.
25. N. Al Moubayed, A. Petrovski and J. McCall, “D2 MOPSO: MOPSO Based on Decomposition and Dominance with
Archiving Using Crowding Distance in Objective and Solution Spaces”, Evolutionary Computation, Vol. 22, No. 1, pp.
47–77, Spring 2014.
26. Roman Stryczek and Boguslaw Pytlak, “Multi-Objective Optimization with Adjusted PSO Method on Example of
Cutting Process of Hardened 18CrMo4 Steel”, Eksploatacja Niezawodnosc–Maintenance and Reliability, No. 1, pp.
236–245, 2014.
27. Zhi-Hui Zhan, Jingjing Li, Jiannong Cao, Jun Zhang, Henry Shu-Hung Chung and Yu-Hui Shi, “Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems”, IEEE
Transactions on Cybernetics, Vol. 43, No. 2, pp. 445–463, April 2013.
187
28. Yukun Bao, Zhongyi Hu and Tao Xiong, “A PSO and pattern search based memetic algorithm for SVMs parameters
optimization”, Neurocomputing, Vol. 117, pp. 98–106, October 6, 2013.
29. Hu Xia, Jian Zhuang and Dehong Yu, “Combining Crowding Estimation in Objective and Decision Space With Multiple
Selection and Search Strategies for Multi-Objective Evolutionary Optimization”, IEEE Transactions on Cybernetics,
Vol. 44, No. 3, pp. 378–393, March 2014.
30. Andre Britto and Aurora Pozo, “Using reference points to update the archive of MOPSO algorithms in Many-Objective
Optimization”, Neurocomputing, Vol. 127, pp. 78–87, March 15, 2014.
31. Eduardo J. Solteiro Pires, Jose A. Tenreiro Machado and Paulo B. de Moura Oliveira, “Entropy Diversity in MultiObjective Particle Swarm Optimization”, Entropy, Vol. 15, No. 12, pp. 5475–5491, December 2013.
32. Ilhern Boussaid, Julien Lepagnot and Patrick Siarry, “A survey on optimization metaheuristics”, Information Sciences,
Vol. 237, pp. 82–117, July 10, 2013.
33. Efrén Mezura-Montes, Edgar A. Portilla-Flores and Betania Hernández-Ocaña, “Optimum synthesis of a four-bar mechanism using the modified bacterial foraging algorithm”, International Journal of Systems Science, Vol. 45, No. 5, pp.
1080–1100, May 4, 2014.
34. Guanghui Wang, Jie Chen, Tao Cai and Bin Xin, “Decomposition-based multi-objective differential evolution particle
swarm optimization for the design of a tubular permanent magnet linear synchronous motor”, Engineering Optimization,
Vol. 45, No. 9, pp. 1107–1127, September 1, 2013.
35. Mengqi Hu, Teresa Wu, and Jeffery D. Weir, “An Adaptive Particle Swarm Optimization With Multiple Adaptive
Methods”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 5, pp. 705–720, October 2013.
36. Xingjuan Cai and Ying Tan, “A study on the effect of upsilon(max) in particle swarm optimisation with high dimension”,
International Journal of Bio-Inspired Computation, Vol. 1, No. 3, pp. 210–216, 2009.
37. Heming Xu, Yinglin Wang and Xin Xu, “Multiobjective Particle Swarm Optimization based on Dimensional Update”,
International Journal on Artificial Intelligence Tools, Vol. 22, No. 3, Article Number: 1350015, June 2013.
38. Yu-Jun Zheng and Sheng-Yong Chen, “Cooperative particle swarm optimization for multiobjective transportation planning”, Applied Intelligence, Vol. 39, No. 1, pp. 202–216, July 2013.
39. Sujin Bureerat and Krit Sriworamas, “Simultaneous topology and sizing optimization of a water distribution network
using a hybrid multiobjective evolutionary algorithm”, Applied Soft Computing, Vol. 13, No. 8, pp. 3693–3702, August
2013.
40. Martin Pilat and Roman Neruda, “Aggregate meta-models for evolutionary multiobjective and many-objective optimization”, Neurocomputing, Vol. 116, pp. 392–402, September 20, 2013.
41. Xin-She Yang and Suash Deb, “Multiobjective cuckoo search for design optimization”, Computers & Operations Research,
Vol. 40, No. 6, pp. 1616–1624, June 2013.
42. S. Ganguly, N.C. Sahoo and D. Das, “Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for
possibilistic planning of electrical distribution systems incorporating distributed generation”, Fuzzy Sets and Systems,
Vol. 213, pp. 47–73, February 16, 2013.
43. Tessa Page, Thi Huynh Nguyen Huong, Lindsey Hilts, Lorena Ramos and Grady Hanrahan, “Biologically driven neural
platform invoking parallel electrophoretic separation and urinary metabolite screening”, Analytical and Bioanalytical
Chemistry, Vol. 403, No. 8, pp. 2367–2375, June 2012.
44. Siwadol Kanyakam and Sujin Bureerat, “Multiobjective Optimization of a Pin-Fin Heat Sink Using Evolutionary Algorithms”, Journal of Electronic Packaging, Vol. 134, No. 2, Article Number: 021008, June 2012.
45. Gang Lei, X.M. Chen, J.G. Zhu, Y.G. Guo, Wei Xu and K.R. Shao, “Multiobjective Sequential Optimization Method for
the Design of Industrial Electromagnetic Devices”, IEEE Transactions on Magnetics, Vol. 48, No. 11, pp. 4538–4541,
November 2012.
46. Vigneshwaran Namasivayam and Jürgen Bajorath, “Multiobjective Particle Swarm Optimization: Automated Identification of Structure-Activity Relationship-Informative Compounds with Favorable Physicochemical Property Distributions”, Journal of Chemical Information and Modeling, Vol. 52, No. 11, pp. 2848–2855, November 2012.
47. Tiago Oliveira Weber and Wilhelmus A.M. Van Noije, “Analog circuit synthesis performing fast Pareto frontier exploration and analysis through 3D graphs”, Analog Integrated Circuits and Signal Processing, Vol. 73, No. 3, pp. 861–871,
December 2012.
48. Juan Lanchares, Oscar Garnica, Francisco Fernandez-de-Vega and J. Ignacio Hidalgo, “A review of bioinspired computeraided design tools for hardware design”, Concurrency and Computation–Practice & Experience, Vol. 25, No. 8, pp.
1015–1036, June 10, 2013.
49. Xin-She Yang, “Multiobjective firefly algorithm for continuous optimization”, Engineering with Computers, Vol. 29, No.
2, pp. 175–184, April 2013.
188
50. Nantiwat Pholdee and Sujin Bureerat, “Hybridisation of real-code population-based incremental learning and differential
evolution for multiobjective design of trusses”, Information Sciences, Vol. 223, pp. 136–152, February 20, 2013.
51. Lixin Tang and Xianpen Wang, “A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization
Problems”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 20–45, February 2013.
52. Mohammad Reza Farmani, Jafar Roshanian, Meisam Babaie and Parviz M. Zadeh, “Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making”, Proceedings of the Institution of Mechanical Engineers Part C–Journal of Mechanical Engineering Science, Vol. 226, No. C9, pp. 2281–2295,
2012.
53. R.H. Ordonez-Hurtado and M.A. Duarte-Mermoud, “Finding common quadratic Lyapunov functions for switched linear
systems using particle swarm optimisation”, International Journal of Control, Vol. 85, No. 1, pp. 12–25, 2012.
54. Petr Kadlec and Zbynek Raida, “A Novel Multi-Objective Self-Organizing Migrating Algorithm”, Radioengineering, Vol.
20, No. 4, pp. 804–816, December 2011.
55. Fernando Alonso Zotes and Matilde Santos Penas, “Particle swarm optimisation of interplanetary trajectories from Earth
to Jupiter and Saturn”, Engineering Applications of Artificial Intelligence, Vol. 25, No. 1, pp. 189–199, February 2012.
56. S.N. Omkar, Akshay Venkatesh and Mrunmaya Mudigere, “MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures”, Engineering Applications of
Artificial Intelligence, Vol. 25, No. 8, pp. 1611–1627, December 2012.
57. Yong Zhang, Dun-wei Gong and Jian-hua Zhang, “Robot path planning in uncertain environment using multi-objective
particle swarm optimization”, Neurocomputing, Vol. 103, pp. 172–185, March 1, 2013.
58. Lie-Jane Kao and Cheng-Few Lee, “Alternative method for determining industrial bond ratings: theory and empirical
evidence”, International Journal of Information Technology & Decision Making, Vol. 11, No. 6, pp. 1215–1235,
November 2012.
59. Nantiwat Pholdee and Sujin Bureerat, “Performance enhancement of multiobjective evolutionary optimisers for truss
design using an approximate gradient”, Computers & Structures, Vol. 106, pp. 115–124, September 2012.
60. Nantiwat Pholdee and Sujin Bureerat, “Hybridisation of real-code population-based incremental learning and differential
evolution for multiobjective design of trusses”, Information Sciences, Vol. 223, pp. 136–152, February 20, 2013.
61. Robert Carrese, Hadi Winarto, Xiaodong Li, Andras Sobester and Samuel Ebenezer, “A comprehensive preference-based
optimization framework with application to high-lift aerodynamic design”, Engineering Optimization, Vol. 44, No. 10,
pp. 1209–1227, 2012.
62. Junwan Liu, Zhoujun Li, Xiaohua Hu, Yiming Chen and Feifei Liu, “Multi-objective dynamic population shuffled frogleaping biclustering of microarray data”, BMC Genomics, Vol. 13, Supplement: 3, Article Number: S6, June 11, 2012.
63. Jiuping Xu and Zongmin Li, “Multi-Objective Dynamic Construction Site Layout Planning in Fuzzy Random Environment”, Automation in Construction, Vol. 27, pp. 155–169, November 2012.
64. Xin-She Yang, “Bat algorithm for multi-objective optimisation”, International Journal of Bio-Inspired Computation,
Vol. 3, No. 5, pp. 267–274, 2011.
65. El-Ghazali Talbi, Matthieu Basseur, Antonio J. Nebro and Enrique Alba, “Multi-objective optimization using metaheuristics: non-standard algorithms”, International Transactions in Operational Research, Vol. 19, Nos. 1-2, pp. 283–
305, January-March 2012.
66. Francesco Castellini and Michele R. Lavagna, “Comparative Analysis of Global Techniques for Performance and Design
Optimization of Launchers”, Journal of Spacecraft and Rockets, Vol. 49, No. 2, pp. 274–285, March-April 2012.
67. A. Kaveh and K. Laknejadi, “A Hybrid Multi-Objective Optimization and Decision Making Procedure for Optimal
Design of Truss Structures”, Iranian Journal of Science and Technology–Transactions of Civil Engineering, Vol. 35, No.
C2, pp. 137–154, August 2011.
68. Juan J. Durillo and Antonio J. Nebro, “jMetal: A Java framework for multi-objective optimization”, Advances in
Engineering Software, Vol. 42, No. 10, pp. 760–771, October 2011.
69. Minh-Trien Pham, Diahai Zhang and Chang Seop Koh, “Multi-Guider and Cross-Searching Approach in Multi-Objective
Particle Swarm Optimization for Electromagnetic Problems”, IEEE Transactions on Magnetics, Vol. 48, No. 2, pp.
539–542, February 2012.
70. Leandro dos S. Coelho, Fabio A. Guerra and Jean V. Leite, “Multiobjective Exponential Particle Swarm Optimization
Approach Applied to Hysteresis Parameters Estimation”, IEEE Transactions on Magnetics, Vol. 48, No. 2, pp. 283–286,
February 2012.
71. Ahmad Nourbakhsh, Hamed Safikhani and Shahram Derakhshan, “The comparison of multi-objective particle swarm
optimization and NSGA II algorithm: applications in centrifugal pumps”, Engineering Optimization, Vol. 43, No. 10,
pp. 1095–1113, 2011.
189
72. Salman Khan and Andries P. Engelbrecht, “A fuzzy particle swarm optimization algorithm for computer communication
network topology design”, Applied Intelligence, Vol. 36, No. 1, pp. 161–177, January 2012.
73. Daqi Zhu, Qian Liu and Zhen Hu, “Fault-tolerant control algorithm of the manned submarine with multi-thruster based
on quantum-behaved particle swarm optimisation”, International Journal of Control, Vol. 84, No. 11, pp. 1817–1829,
2011.
74. Daqi Zhu, Jing Liu and Simon X. Yang, “Particle Swarm Optimization Approach to Thruster Fault-Tolerant Control of
Unmanned Underwater Vehicles”, International Journal of Robotics & Automation, Vol. 26, No. 3, pp. 282–287, 2011.
75. Hongbo Liu and Ajith Abraham, “An hybrid fuzzy variable neighborhood particle swarm optimization algorithm for
solving quadratic assignment problems”, Journal of Universal Computer Science, Vol. 13, No. 9, pp. 1309–1331, 2007.
76. Mengqi Hu, Jeffrey D. Weir and Teresa Wu, “Decentralized operation strategies for an integrated building energy system
using a memetic algorithm”, European Journal of Operational Research, Vol. 217, No. 1, pp. 185–197, February 16,
2012.
77. Ling Wang, Xiang Zhong and Min Liu, “A novel group search optimizer for multi-objective optimization”, Expert Systems
with Applications, Vol. 39, No. 3, pp. 2939–2946, February 15, 2012.
78. Siwadol Kanyakam and Sujin Bureerat, “Multiobjective evolutionary optimization of splayed pin-fin heat sink”, Engineering Applications of Computational Fluid Mechanics, Vol. 5, No. 4, pp. 553–565, December 2011.
79. Dilip Datta and Jose Rui Figueira, “Graph partitioning by multi-objective real-valued metaheuristics: A comparative
study”, Applied Soft Computing, Vol. 11, No. 5, pp. 3976–3987, July, 2011.
80. Yuhui Shi and Russ Eberhart, “Monitoring of particle swarm optimization”, Frontiers of Computer Science in China,
Vol. 3, No. 1, pp. 31–37, March 2009.
81. Leandro dos Santos Coelho and Diego Luis de Andrade Bernert, “PID control design for chaotic synchronization using
a tribes optimization approach”, Chaos Solitons & Fractals, Vol. 42, No. 1, pp. 634–640, October 15, 2009.
82. A. Rama Mohan Rao and K. Sivasubramanian, “Multi-objective optimal design of fuzzy logic controller using a self
configurable swarm intelligence algorithm”, Computers & Structures, Vol. 86, Nos. 23-24, pp. 2141–2154, December
2008.
83. Dervis Karaboga and Bahriye Akay, “A survey: algorithms simulating bee swarm intelligence”, Artificial Intelligence
Review, Vol. 31, Nos. 1-4, pp. 61–85, June 2009.
84. N.M. Pindoriya, S.N. Singh and S.K. Singh, “Multi-objective mean-variance-skewness model for generation portfolio
allocation in electricity markets”, Electric Power Systems Research, Vol. 80, No. 10, pp. 1314–1321, October 2010.
85. Shuang Wei and Henry Leung, “A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary
Multiobjective Optimization”, Mathematical Problems in Engineering, Article Number: 695087, 2011.
86. N.C. Sahoo, S. Ganguly and D. Das, “Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical
distribution systems using multi-objective particle swarm optimization”, Expert Systems with Applications, Vol. 39, No.
1, pp. 881–893, January 2012.
87. A. Rama Mohan Rao and K. Lakshmi, “Discrete hybrid PSO algorithm for design of laminate composites with multiple
objectives”, Journal of Reinforced Plastics and Composites, Vol. 30, No. 20, pp. 1703–1727, October 2011.
88. Joaquin Izquierdo, Idel Montalvo, Rafael Perez-Garcia and Agustin Matias, “On the Complexities of the Design of Water
Distribution Networks”, Mathematical Problems in Engineering, Vol. Article Number: 947961, 2012.
89. Rasmus K. Ursem and Peter Dueholm Justesen, “Multi-objective Distinct Candidates Optimization: Locating a few
highly different solutions in a circuit component sizing problem”, Applied Soft Computing, Vol. 12, No. 1, pp. 255–265,
January 2012.
90. De-bao Chen, Feng Zou and Jiang-tao Wang, “A multi-objective endocrine PSO algorithm and application”, Applied
Soft Computing, Vol. 11, No. 8, pp. 4508–4520, December 2011.
91. Andre B. de Carvalho and Aurora Pozo, “Measuring the convergence and diversity of CDAS Multi-Objective Particle
Swarm Optimization Algorithms: A study of many-objective problems”, Neurocomputing, Vol. 75, No. 1, pp. 43–51,
January 1, 2012.
92. Costin D. Untaroiu and Alexandrina Untaroiu, “Constrained Design Optimization of Rotor-Tilting Pad Bearing Systems”, Journal of Engineering for Gas Turbines and Power–Transactions of the ASME, Vol. 132, No. 12, Article
Number: 122502, December 2010.
93. R. de-Carvalho, R.A.F. Valente and A. Andrade-Campos, “Optimization strategies for non-linear material parameters
identification in metal forming problems”, Computers & Structures, Vol. 89, Nos. 1-2, pp. 246–255, January 2011.
94. Tawatchai Kunakote and Sujin Bureerat, “Multi-objective topology optimization using evolutionary algorithms”, Engineering Optimization, Vol. 43, No. 5, pp. 541–557, 2011.
95. Qian Tao, Hui-You Chang, Yang Yi, Chun-qin Gu and Wen-jie Li, “A rotary chaotic PSO algorithm for trustworthy
scheduling of a grid workflow”, Computers & Operations Research, Vol. 38, No. 5, pp. 824–836, May 2011.
190
96. Yaima Filiberto, Rafael Bello, Yaile Caballero and Rafael Larrua, “A measure in the rough set theory to decision systems
with continuo features”, Revista Facultad de Ingenierı́a–Universidad de Antioquia, No. 60, pp. 141–152, September 2011.
97. Pinaki Mitra and Ganesh Kumar Venayagamoorthy, “Implementation of an Intelligent Reconfiguration Algorithm for
an Electric Ship’s Power System”, IEEE Transactions on Industry Applications, Vol. 47, No. 5, pp. 2292–2300,
September-October 2011.
98. Leandro dos Santos Coelho, Helon Vicente Hultmann Ayala and Piergiorgio Alotto, “A Multiobjective Gaussian Particle
Swarm Approach Applied to Electromagnetic Optimization ”, IEEE Transactions on Magnetics, Vol. 46, No. 8, pp.
3289–3292, August 2010.
99. A. Kaveh and K. Laknejadi, “A novel hybrid charge system search and particle swarm optimization method for multiobjective optimization”, Expert Systems with Applications, Vol. 38, No. 12, pp. 15475–15488, November-December
2011.
100. Robert Carrese, Hadi Winarto, Jon Watmuff and Upali K. Wickramasinghe, “Benefits of Incorporating Designer Preferences Within a Multi-Objective Airfoil Design Framework”, Journal of Aircraft, Vol. 48, No. 3, pp. 832–844, May-June
2011.
101. Robert Carrese, Andras Sobester, Hadi Winarto and Xiaodong Li, “Swarm Heuristic for Identifying Preferred Solutions
in Surrogate-Based Multi-Objective Engineering Design”, AIAA Journal, Vol. 49, No. 7, pp. 1437–1449, July 2011.
102. Guang-ho Hu, Zhi-zhong Mao and Da-kuo He, “Multi-objective optimization for leaching process using improved twostage guide PSO algorithm”, Journal of Central South University of Technology, Vol. 18, No. 4, pp. 1200–1210, August
2011.
103. Yong Zhang, Dun-wei Gong and Zhong-hai Ding, “Handling multi-objective optimization problems with a multi-swarm
cooperative particle swarm optimizer”, Expert Systems with Applications, Vol. 38, No. 11, pp. 13933–13941, October
2011.
104. H. Moslemi and M. Zandieh, “Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory
system”, Expert Systems with Applications, Vol. 38, No. 10, pp. 12051–12057, September 15, 2011.
105. M.J. Mahmoodabadi, A. Bagheri, S. Arabani Mostaghim and M. Bisheban, “Simulation of stability using Java application
for Pareto design of controllers based on a new multi-objective particle swarm optimization”, Mathematical and Computer
Modelling, Vol. 54, Nos. 5-6, pp. 1584–1607, September 2011.
106. N.C. Sahoo, S. Ganguly and D. Das, “Simple heuristics-based selection of guides for multi-objective PSO with an
application to electrical distribution system planning”, Engineering Applications of Artificial Intelligence, Vol. 24, No.
4, pp. 567–585, June 2011.
107. Yann Cooren, Maurice Clerc and Patrick Siarry, “MO-TRIBES, an adaptive multiobjective particle swarm optimization
algorithm”, Computational Optimization and Applications, Vol. 49, No. 2, pp. 379–400, June 2011.
108. Jamal Saeedi and Karim Faez, “A new pan-sharpening method using multiobjective particle swarm optimization and the
shiftable contourlet transform”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 3, pp. 365–381,
May 2011.
109. Xiangwei Zheng and Hong Liu, “A scalable coevolutionary multi-objective particle swarm optimizer”, International
Journal of Computational Intelligence Systems, Vol. 3, No. 5, pp. 590–600, October 2010.
110. Magdalene Marinaki, Yannis Marinakis and Georgios E. Stavroulakis, “Fuzzy control optimized by a Multi-Objective
Particle Swarm Optimization algorithm for vibration suppression of smart structures”, Structural and Multidisciplinary
Optimization, Vol. 43, No. 1, pp. 29–42, January 2011.
111. Miltiadis Kotinis, “A particle swarm optimizer for constrained multi-objective engineering design problems”, Engineering
Optimization, Vol. 42, No. 10, pp. 907–926, October 2010.
112. S.-Z. Zhao and P.N. Suganthan, “Two-lbests based multi-objective particle swarm optimizer”, Engineering Optimization,
Vol. 43, No. 1, pp. 1–17, January 2011.
113. G. D’Errico, T. Cerri and G. Pertusi, “Multi-objective optimization of internal combustion engine by means of 1D
fluid-dynamic models”, Applied Energy, Vol. 88, No. 3, pp. 767–777, March 2011.
114. H. Yapicioglu, H. Liu, A.E. Smith and G. Dozier, “Hybrid approach for Pareto front expansion in heuristics”, Journal
of the Operational Research Society, Vol. 62, No. 2, pp. 348–359, February 2011.
115. Jingxuan Wei and Yuping Wang, “An Infeasible Elitist Based Particle Swarm Optimization for Constrained Multiobjective Optimization and Its Convergence”, International Journal of Pattern Recognition and Artificial Intelligence, Vol.
24, No. 3, pp. 381–400, May 2010.
116. Hao Cui and Osman Turan, “Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation
methodology in ship design”, Computer-Aided Design, Vol. 42, No. 11, pp. 1013–1027, November 2010.
117. Vincenzo Cavaliere, Marco Cioffi, Alessandro Formisano and Raffaele Martone, “Pareto swarm optimisation of high
temperature superconducting generators”, International Journal of Applied Electromagnetics and Mechanics, Vol. 25,
Nos. 1–4, pp. 273–279, 2007.
191
118. Junwan Liu, Zhoujun Li, Xiaohua Hu and Yiming Chen, “Biclustering of microarray data with MOSPO based on
crowding distance”, BMC Bioinformatics, Vol. 10, Article Number S9, Suppl. 4, April 29, 2009.
119. Yong Wang, Lin Li, Jun Ni and Shuhong Huang, “Form Tolerance Evaluation of Toroidal Surfaces Using Particle Swarm
Optimization”, Journal of Manufacturing Science and Engineering–Transactions of the ASME, Vol. 131, No. 5, Article
Number: 051015, October 2009.
120. Tuerkay Dereli, Serap Ulusam Seckiner, Guelesin Sena Das, Hadi Gokcen and Mehmet Emin Aydin, “An exploration
of the literature on the use of ’swarm intelligence-based techniques’ for public service problems”, European Journal of
Industrial Engineering, Vol. 3, No. 4, pp. 379–423, 2009.
121. A. Larrua, I. Olivera, Y. Caballero, Y. Filiberto, M. Guerra, R. Bello and J. Bonilla, “Application of the Artificial
Intelligence to the Prediction of the Ultimate Resistant Capacity of Connections in Steel-Concrete Composite Structures”,
Revista de la Construcción, Vol. 8, No. 2, pp. 109–119, December 2009.
122. Andre B. de Carvalho, Aurora Pozo and Silvia Regina Vergilio, “A symbolic fault-prediction model based on multiobjective particle swarm optimization”, Journal of Systems and Software, Vol. 83, No. 5, pp. 868–882, May 2010.
123. Sanjoy Deb, N. Basanta Singh, Samir Kumar Sarkar and Subir Kumar Sarkara, “Parameter Optimization for Better
Quantum Well Nanostructure Based on Comparative Performance Analysis of Particle Swarm Optimization and Genetic
Algorithm”, Journal of Computational and Theoretical Nanoscience, Vol. 7, No. 10, pp. 2024–2030, October 2010.
124. Hong Zhang and Masumi Ishikawa, “The performance verification of an evolutionary canonical particle swarm optimizer”,
Neural Networks, Vol. 23, No. 4, pp. 510–516, May 2010.
125. Vladimir Sedenka and Zbynek Raida, “Critical Comparison of Multi-objective Optimization Methods: Genetic Algorithms versus Swarm Intelligence”, Radioengineering, Vol. 19, No. 3, pp. 369–377, September 2010.
126. Antonio C. Briza and Prospero C. Naval, Jr., “Stock trading system based on the multi-objective particle swarm
optimization of technical indicators on end-of-day market data”, Applied Soft Computing, Vol. 11, No. 1, pp. 1191–
1201, January 2011.
127. M.A. Abido, “Multiobjective particle swarm optimization with nondominated local and global sets”, Natural Computing,
Vol. 9, No. 3, pp. 747–766, September 2010.
128. S. Bureerat and S. Srisomporn, “Optimum plate-fin heat sinks by using a multi-objective evolutionary algorithm”,
Engineering Optimization, Vol. 42, No. 4, pp. 305–323, April 2010.
129. Jan Hettenhausen, Andrew Lewis and Sanaz Mostaghim, “Interactive multi-objective particle swarm optimization with
heatmap-visualization-based user interface”, Engineering Optimization, Vol. 42, No. 2, pp. 119–139, February 2010.
130. Lingfeng Wang and Chanan Singh, “Reserve-constrained multiarea environmental/economic dispatch based on particle
swarm optimization with local search”, Engineering Applications of Artificial Intelligence, Vol. 22, No. 2, pp. 298–307,
March 2009.
131. Shubham Agrawal, B.K. Panigrahi and Manoj Kumar Tiwari, “Multiobjective Particle Swarm Algorithm with Fuzzy
Clustering for Electrical Power Dispatch”, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 5, pp.
529–541, October 2008.
132. A.B. de Carvalho and A.T.R. Pozo, “A Rule Learning Multiobjective Particle Swarm Optimization”, IEEE Latin America
Transactions, Vol. 7, No. 4, pp. 478–486, August 2009.
133. Babak Forouraghi, “Optimal tolerance allocation using a multiobjective particle swarm optimizer”, International Journal
of Advanced Manufacturing Technology, Vol. 44, Nos. 7–8, pp. 710–724, October 2009.
134. G. Venter and R.T. Haftka, “Constrained particle swarm optimization using a bi-objective formulation”, Structural and
Multidisciplinary Optimization, Vol. 40, Nos. 1-6, pp. 65–76, January 2010.
135. Stefan Janson, Daniel Merkle and Martin Middendorf, “Molecular docking with multi-objective particle swarm optimization”, Applied Soft Computing, Vol. 8, No. 1, pp. 666–675, January 2008.
136. Yujia Wang and Yupu Yang, “Particle swarm with equilibrium strategy of selection for multi-objective optimization”,
European Journal of Operational Research, Vol. 200, No. 1, pp. 187–197, January 1, 2010.
137. Xiangwei Zheng and Hong Liu, “A hybrid vertical mutation and self-adaptation based MOPSO”, Computers & Mathematics with Applications, Vol. 57, Nos. 11–12, pp. 2030–2038, June 2009.
• Carlos A. Coello Coello, Alan D. Christiansen and Francisco Alonso Farrera, “A Genetic Algorithm for
the Optimal Design of Axially Loaded Non-prismatic Columns”. Civil Engineering Systems, Vol. 14. pp.
111–146, 1996.
1. A. Cruz, W. Velez and P. Thomson, “Optimal sensor placement for modal identification of structures using genetic
algorithms-a case study: the olympic stadium in Cali, Colombia”, Annals of Operations Research, Vol. 181, No. 1, pp.
769–781, December 2010.
192
2. I. U. Cagdas and S. Adali, “Optimization of clamped columns under distributed axial load and subject to stress constraints”, Engineering Optimization, Vol. 39, No. 4, pp. 453–469, June 2007.
3. Sarp Adali and Izzet U. Cagdas, “Optimal design of simply supported columns subject to distributed axial load and
stress constraint”, Optimal Control Applications & Methods, Vol. 30, No. 5, pp. 505–520, September-October 2009.
• Leticia Cagnina, Susana Esquivel, and Carlos A. Coello Coello, “A particle swarm optimizer for multiobjective optimization”, Journal of Computer Science & Technology, Vol. 5, No. 4, pp. 204–210, 2005.
1. Arup Ratan Bhowmik and A.K. Chakraborty, “Solution of optimal power flow using nondominated sorting multi objective
gravitational search algorithm”, International Journal of Electrical Power & Energy Systems, Vol. 62, pp. 323–334,
November 2014.
2. Yen-Liang Chen and Xiang-Han Chen, “An evolutionary PageRank approach for journal ranking with expert judgements”, Journal of Information Science, Vol. 37, No. 3, pp. 254–272, June 2011.
3. Ngai M. Kwok, Q.P. Ha, Dikai Liu and Gu Fang, “Contrast Enhancement and Intensity Preservation for Gray-Level
Images Using Multiobjective Particle Swarm Optimization”, IEEE Transactions on Automation Science and Engineering,
Vol. 6, No. 1, pp. 145–155, January 2009.
• Y. Pablo Oñate, Juan M. Ramirez and Carlos A. Coello Coello, “An optimal power flow plus transmission
costs solution”, Electric Power Systems Research, Volume 79, No. 8, pp. 1240–1246, August 2009.
1. Nampetch Sinsuphan, Uthen Leeton and Thanatchai Kulworawanichpong, “ Optimal power flow solution using improved
harmony search method”, Applied Soft Computing, Vol. 13, No. 5, pp. 2364–2374, May 2013.
2. Bastin Solai J. Nazaran and K. Selvi, “Security Enhanced Optimal Power Flow with Transmission Cost Solution”,
International Review of Electrical Engineering-IREE, Part B, Vol. 7, No. 4, pp. 4963–4970, July-August 2012.
3. Taher Niknam, Mohammad Rasoul Narimani, Masoud Jabbari and Admad Reza Malekpour, “A modified shuffle frog
leaping algorithm for multi-objective optimal power flow”, Energy, Vol. 36, No. 11, pp. 6420–6432, November 2011.
4. T. Niknam, M.R. Narimani, J. Aghaei, S. Tabatabaei and M. Nayeripour, “Modified Honey Bee Mating Optimisation
to solve dynamic optimal power flow considering generator constraints”, IET Generation Transmission & Distribution,
Vol. 5, No. 10, pp. 989–1002, October 2011.
5. A.Y. Abdelaziz, F.M. Mohammed, S.F. Mekhamer and M.A.L. Badr, “Distribution Systems Reconfiguration using a
modified particle swarm optimization algorithm”, Electric Power Systems Research, Vol. 79, No. 11, pp. 1521–1530,
November 2009.
• Carlos A. Coello Coello and Ricardo Landa Becerra, “Evolutionary Multi-Objective Optimization in Materials
Science and Engineering”, Materials and Manufacturing Processes, Vol. 24, No. 2, pp. 119–129, February
2009.
1. Nirupam Chakraborti, “Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic
algorithms in materials research”, Materials Science and Technology, Vol. 30, No. 11, pp. 1259–1262, September 2014.
2. Daniel Teixidor, Joaquim Ciurana and Ciro Rodriguez, “Multiobjective Optimization of Laser Milling Parameters of
Microcavities for the Manufacturing of DES”, Materials and Manufacturing Processes, Vol. 28, No. 12, pp. 1370–1378,
December 2, 2013.
3. Wojciech Paszkowicz, “Genetic Algorithms, a Nature-Inspired Tool: A Survey of Applications in Materials Science and
Related Fields: Part II”, Materials and Manufacturing Processes, Vol. 28, No. 7, pp. 708–725, July 3, 2013.
4. T. Cheung, N. Cheung, C.M.T. Tobar, P.R. Mei and A. Garcia, “Zone Refining of Tin: Optimization of Zone Length
by a Genetic Algorithm”, Materials and Manufacturing Processes, Vol. 28, No. 7, pp. 746–752, July 3, 2013.
5. Tarun Kumar Sharma, Millie Pant and Mohar Singh, “Nature-Inspired Metaheuristic Techniques as Powerful Optimizers
in the Paper Industry”, Materials and Manufacturing Processes, Vol. 28, No. 7, pp. 788–802, July 3, 2013.
6. Xuemei Sun, Guoqun Zhao, Cunsheng Zhang, Yanjin Guan and Anjiang Gao, “Optimal Design of Second-Step Welding
Chamber for a Condenser Tube Extrusion Die Based on the Response Surface Method and the Genetic Algorithm”.
Materials and Manufacturing Processes, Vol. 28, No. 7, pp. 823–834, July 3, 2013.
7. Durul Ulutan and Tugrul Özel, “Multiobjective Optimization of Experimental and Simulated Residual Stresses in Turning
of Nickel-Alloy IN100”, Materials and Manufacturing Processes, Vol. 28, No. 7, pp. 835–841, July 3, 2013.
8. Itziar Marquez, Maribel Arribas, Ana Carrillo and Jose Luis Arana, “Optimisation of total roll power using genetic
algorithms in a compact strip production plant”, International Journal of Materials Research, Vol. 104, No. 7, pp.
686–696, July 2013.
9. F. Tancret, “Computational thermodynamics, Gaussian processes and genetic algorithms: combined tools to design new
alloys”, Modelling and Simulation in Materials Science and Engineering, Vol. 21, No. 4, Article Number: 045013, June
2013.
193
10. B.N. Pathak, K.L. Sahoo and Madhawanand Mishra, “Effect of Machining Parameters on Cutting Forces and Surface
Roughness in Al-(1-2) Fe-1V-1Si Alloys”, Materials and Manufacturing Processes, Vol. 28, No. 4, pp. 463–469, April 1,
2013.
11. Elisabet Capon-Garcia, Aaron D. Bojarski, Antonio Espuna and Luis Puigjaner, “Multiobjective Evolutionary Optimization of Batch Process Scheduling Under Environmental and Economic Concerns”, AICHE Journal, Vol. 59, No. 2,
pp. 429–444, february 2013.
12. Vasdev Malhotra, Tilak Raj and Ashok Arora, “Evaluation of Barriers Affecting Reconfigurable Manufacturing Systems
with Graph Theory and Matrix Approach”, Materials and Manufacturing Processes, Vol. 27, No. 1, pp. 88–94, 2012.
13. Hiromitsu Tomizawa, “Advanced metaheuristic algorithms for laser optimization in optical accelerator technologies”,
Radiation Physics and Chemistry, Vol. 80, No. 10, pp. 1145–1149, October 2011.
14. R. Venkata Rao and V.D. Kalyankar, “Parameter Optimization of Machining Processes Using a New Optimization
Algorithm”, Materials and Manufacturing Processes, Vol. 27, No. 9, pp. 978–985, 2012.
15. Liqiang Zhang and Rongji Wang, “An intelligent system for low-pressure die-cast process parameters optimization”,
International Journal of Advanced Manufacturing Technology, Vol. 65, Nos. 1-4, pp. 517–524, March 2013.
16. Sudipta Sikdar and Indrajit Mukherjee, “A Holistic Framework for Multiple Response Optimization of Hot Strip Rolling
Process”, Materials and Manufacturing Processes, Vol. 26, No. 11, pp. 1393–1403, 2011.
17. Q. Zhang, M. Mahfouf, G. Panoutsos, K. Beamish and I. Norris, “Knowledge discovery for friction stir welding via data
driven approaches Part 2-multiobjective modelling using fuzzy rule based systems”, Science and Technology of Welding
and Joining, Vol. 17, No. 8, pp. 681–693, November 2012.
18. Daniel Teixidor, Ines Ferrer, Joaquim Ciurana and Tugrul Özel, “Optimization of process parameters for pulsed laser
milling of micro-channels on AISI H13 tool steel”, Robotics and Computer-Integrated Manufacturing, Vol. 29, No. 1, pp.
209–218, February 2013.
19. M.R. Dashtbayazi, “Artificial Neural Network-Based Multiobjective Optimization of Mechanical Alloying Process for
Synthesizing of Metal Matrix Nanocomposite Powder”, Materials and Manufacturing Processes, Vol. 27, No. 1, pp.
33–42, 2012.
20. Liqiang Zhang, Luoxing Li, Shiuping Wang and Biwu Zhu, “Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method”, Journal of Materials Engineering and Performance,
Vol. 21, No. 4, pp. 492–499, April 2012.
21. F. Tancret, “Computational thermodynamics and genetic algorithms to design affordable gamma ’-strengthened nickeliron based superalloys”, Modelling and Simulation in Materials Science and Engineering, Vol. 20, No. 4, Article Number:
045012, June 2012.
22. Chung-Feng Jeffery Kuo, Shin-Wei Liang and Hung-Min Tu, “Optimization Parameters of Femtosecond Laser Isolation
Processing for a Microcrystalline Silicon Thin Film Solar Cell”, Materials and Manufacturing Processes, Vol. 26, No.
10, pp. 1310–1318, 2011.
23. Aman Kumar, Debalay Chakrabarti and Nirupam Chakraborti, “Data-Driven Pareto Optimization for Microalloyed
Steels Using Genetic Algorithms”, Steel Research International, Vol. 83, No. 2, pp. 169–174, February 2012.
24. T. Cheung, N. Cheung, C.M.T. Tobar, R. Caram and A. Garcia, “Application of a Genetic Algorithm to Optimize
Purification in the Zone Refining Process”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 493–500, 2011.
25. Qian Zhang, Mahdi Mahfouf, John R. Yates, Christophe Pinna, George Panoutsos, Soufiene Boumaiza, Richard J.
Greene and Luis de Leon, “Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys
Using a Fast Hierarchical Multiobjective Optimization Algorithm”, Materials and Manufacturing Processes, Vol. 26,
No. 3, pp. 508–520, 2011.
26. A. Schmidt, “Numerical Prediction and Sequential Process Optimization in Sheet Forming Based on Genetic Algorithm”,
Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 521–526, 2011.
27. Chih-Cherng Chen, Pao-Lin Su, Chung-Biau Chiou and Ko-Ta Chiang, “Experimental Investigation of Designed Parameters on Dimension Shrinkage of Injection Molded Thin-Wall Part by Integrated Response Surface Methodology and
Genetic Algorithm: A Case Study”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 534–540, 2011.
28. Andre Felipe Henriques Librantz, Nivaldo Lemos Coppini, Elesandro Antonio Baptista, Sidnei Alves de Araujo and
Aparecida de Fatima Castello Rosa, “Genetic Algorithm Applied to Investigate Cutting Process Parameters Influence
on Workpiece Price Formation”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 550–557, 2011.
29. Arup Kumar Nandi, Kalyanmoy Deb, Subhas Ganguly and Shubhabrata Datta, “Investigating the role of metallic fillers
in particulate reinforced flexible mould material composites using evolutionary algorithms”, Applied Soft Computing,
Vol. 12, No. 1, pp. 28–39, January 2012.
30. Ashish M. Gujarathi and B.V. Babu, “Multiobjective Optimization of Industrial Processes Using Elitist Multiobjective
Differential Evolution (Elitist-MODE)”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 455–463, 2011.
194
31. Byungwhan Kim, Daehyun Kim, Dongil Han and Nae-Il Lee, “Optimization of Wavelet-Filtered In-Situ Plasma Etch
Data Using Neural Network and Genetic Algorithm”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp.
398–402, 2011.
32. Pedro E.J. Rivera-Diaz-del-Castillo and W. Xu, “Heat Treatment and Composition Optimization of Nanoprecipitation
Hardened Alloys”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 375–381, 2011.
33. Debanga Nandan Mondal, Kadambini Sarangi, Frank Pettersson, Prodip Kumar Sen, Henrik Saxen and Nirupam
Chakraborti, “Cu-Zn separation by supported liquid membrane analyzed through Multi-objective Genetic Algorithms”,
Hydrometallurgy, Vol. 107, Nos. 3-4, pp. 112–123, May 2011.
34. Pankaj Rajak, Ujjal Tewary, Sumitesh Das, Baidurya Bhattacharya and Nirupam Chakraborti, “Phases in Zn-coated
Fe analyzed through an evolutionary meta-model and multi-objective Genetic Algorithms”, Computational Materials
Science, Vol. 50, No. 8, pp. 2502–2516, June 2011.
35. Kishalay Mitra, “Handling Uncertainty in Kinetic Parameters in Optimal Operation of a Polymerization Reactor”,
Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 446–454, 2011.
36. Arup Kumar Nandi, Shubhabrata Datta and Kalyanmoy Deb, “Investigating the Role of Nonmetallic Fillers in ParticulateReinforced Mold Composites using EAs”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 541–549, 2011.
37. Karthik Sindhya and Kaisa Miettinen, “New Perspective to Continuous Casting of Steel with a Hybrid Evolutionary
Multiobjective Algorithm”, Materials and Manufacturing Processes, Vol. 26, No. 3, pp. 481–492, 2011.
38. Elisa Vazquez, Joaquim Ciurana, Ciro A. Rodriguez, Thanongsak Thepsonthi and Tugrul Özel, “Swarm Intelligent
Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices”, Materials
and Manufacturing Processes, Vol. 26, No. 3, pp. 403–414, 2011.
39. T.M. El-Hossainy, A.A. El-Zoghby, M.A. Badr, K.Y. Maalawi and M.F. Nasr, “Cutting Parameter Optimization when
Machining Different Materials”, Materials and Manufacturing Processes, Vol. 25, No. 10, pp. 1101–1114, 2010.
40. N. Chandrasekhar and M. Vasudevan, “Intelligent Modeling for Optimization of A-TIG Welding Process”, Materials
and Manufacturing Processes, Vol. 25, No. 11, pp. 1341–1350, 2010.
41. Avneet Kaur and A.K. Bakhshi, “Electro-active ternary copolymer design using genetic algorithm”, Indian Journal of
Chemistry Section A–Inorganic Bio-Inorganic Physical Theoretical & Analytical Chemistry, Vol. 50, No. 1, pp. 9–14,
January 2011.
42. A. Agarwal, U. Tewary, F. Pettersson, S. Das, H. Saxen H and N. Chakraborti, “Analysing blast furnace data using
evolutionary neural network and multiobjective genetic algorithms”, Ironmaking & Steelmaking, Vol. 37, No. 5, pp.
353–359, July 2010.
43. Deepak Govindan, Suman Chakraborty and Nirupam Chakraborti, “Analyzing the Fluid Flow in Continuous Casting
through Evolutionary Neural Nets and Multi-Objective Genetic Algorithms”, Steel Research International, Vol. 81, No.
3, pp. 197–203, March 2010.
44. Kishalay Mitra, “Multiobjective optimization of an industrial grinding operation under uncertainty”, Chemical Engineering Science, Vol. 64, No. 23, pp. 5043–5056, December 1, 2009.
45. Baidurya Bhattacharya, G.R. Dinesh Kumar, Akash Agarwal, Sakir Erkoc, Arunima Singh and Nirupam Chakraborti,
“Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms”,
Computational Materials Science, Vol. 46, No. 4, pp. 821–827, October 2009.
• Efrén Mezura-Montes and Carlos A. Coello Coello, “An Empirical Study About The Usefulness of Evolution
Strategies to Solve Constrained Optimization Problems”, International Journal of General Systems, Vol. 37,
No. 4, pp. 443–473, August 2008.
1. Seyedali Mirjalili and Andrew Lewis, “Adaptive gbest-guided gravitational search algorithm”, Neural Computing &
Applications, Vol. 25, Nos. 7-8, December 2014.
2. Hamid Salimi, “Stochastic Fractal Search: A powerful metaheuristic algorithm”, Knowledge-based Systems, Vol. 75, pp.
1–18, February 2015.
3. Haipeng Kong, Li Ni and Yuzhong Shen, “Adaptive double chain quantum genetic algorithm for constrained optimization
problems”, Chinese Journal of Aeronautics, Vol. 28, No. 1, pp. 214–228, February 2015.
4. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics inspired optimization
(OIO)”, Computer-Aided Design, Vol. 63, pp. 52–71, June 2015.
5. Selim Yilmaz and Ecir U. Kucuksille, “A new modification approach on bat algorithm for solving optimization problems”,
Applied Soft Computing, Vol. 28, pp. 259–275, March 2015.
6. Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis, “Grey Wolf Optimizer”, Advances in Engineering
Software, Vol. 69, pp. 46–61, March 2014.
195
7. B. Nouhi, S. Talatahari, H. Kheiri and C. Cattani, “Chaotic Charged System Search with a Feasible-Based Method for
Constraint Optimization Problems”, Mathematical Problems in Engineering, Article Number: 391765, 2013.
8. Harish Garg, “Solving Structural Engineering Design Optimization Problems using an Artificial Bee Colony Algorithm”,
Journal of Industrial and Management Optimization, Vol. 10, No. 3, pp. 777–794, July 2014.
9. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “ Adaptive Configuration of evolutionary algorithms for
constrained optimization”, Applied Mathematics and Computation, Vol. 222, pp. 680–711, October 1, 2013.
10. A. Kaveh and S. Talatahari, “Hybrid charged system search and particle swarm optimization for engineering design
problems”, Engineering Computations, Vol. 28, Nos. 3-4, pp. 423–440, 2011.
11. A. Kaveh, Mohammad A. Motie Share and M. Moslehi, “Magnetic charged system search: a new meta-heuristic algorithm
for optimization”, Acta Mechanica, Vol. 224, No. 1, pp. 85–107, January 2013.
12. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi and Siamak Talatahari, “Bat algorithm for constrained
optimization tasks”, Neural Computing & Applications, Vol. 22, No. 6, pp. 1239–1255, May 2013.
13. Ali Sadollah, Ardeshir Bahreininejad, Hadi Eskandar and Mohd Hamdi, “Mine blast algorithm: A new population based
algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, Vol. 13, No. 5, pp.
2592–2612, May 2013.
14. A. Kaveh and M. Ahangaran, “Social Harmony Search Algorithm for Continuous Optimization”, Iranian Journal of
Science and Technology-Transactions of Civil Engineering, Vol. 36, No. C2, pp. 121–137, August 2012.
15. Hadi Eskandar, Ali Sadollah, Ardeshir Bahreininejad and Mohd Hamdi, “Water cycle algorithm - A novel metaheuristic
optimization method for solving constrained engineering optimization problems”, Computers & Structures, Vol. 110, pp.
151–166, November 2012.
16. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “On an evolutionary approach for constrained optimization
problem solving”, Applied Soft Computing, Vol. 12, No. 10, pp. 3208–3227, October 2012.
17. S.O. Degertekin, “Improved harmony search algorithms for sizing optimization of truss structures”, Computers & Structures, Vol. 92-93, pp. 229–241, February 2012.
18. Ali Husseinzadeh Kashan, “An efficient algorithm for constrained global optimization and application to mechanical
engineering design: League championship algorithm (LCA)”, Computer-Aided Design, Vol. 43, No. 12, pp. 1769–1792,
December 2011.
19. Shih-Cheng Horng, Shieh-Shing Lin and Feng-Yi Yang, “Evolutionary algorithm for stochastic job shop scheduling with
random processing time”, Expert Systems with Applications, Vol. 39, No. 3, pp. 3603–3610, February 15, 2012.
20. A. Kaveh and S. Talatahari, “An improved ant colony optimization for constrained engineering design problems”,
Engineering Computations, Vol. 27, Nos. 1-2, pp. 155–182, 2010.
21. A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search”, Acta Mechanica, Vol.
213, Nos. 3-4, pp. 267–289, September 2010.
22. A. Kaveh and S. Talatahari, “A particle swarm ant colony optimization for truss structures with discrete variables”,
Journal of Constructional Steel Research, Vol. 65, Nos. 8–9, pp. 1558–1568, August-September 2009.
Congresos Internacionales
• Nareli Cruz-Cortés, Francisco Rodrı́guez Henrı́quez, Raúl Juárez-Morales and Carlos A. Coello Coello, “Finding Optimal Addition Chains Using a Genetic Algorithm Approach”, in Yue Hao et al. (editors), Computational Intelligence and Security. International Conference, CIS 2005, pp. 208–215, Part I, Springer-Verlag,
Lecture Notes in Artificial Intelligence Vol. 3801, Xi’an, China, December 2005.
1. Saul Dominguez-Isidro, Efren Mezura-Montes and Luis-Guillermo Osorio-Hernandez, “Evolutionary programming for
the length minimization of addition chains”, Engineering Applications of Artificial Intelligence, Vol. 37, pp. 125–134,
January 2015.
• Guillermo Leguizamón and Carlos A. Coello Coello, “An alternative ACOR algorithm for continuous optimization problems”, in Marco Dorigo et al. (editors), Swarm Intelligence, 7th International Conference,
ANTS 2010, pp. 48–59, Springer, Lecture Notes in Computer Science Vol. 6234, Brussels, Belgium, September 2010.
1. Reza Shamsaee, Mahmood Fathy and Ali Masoudi-Nejad, “Extracting a cancer model by enhanced ant colony optimisation algorithm”. International Journal of Data Mining and Bioinformatics, Vol. 10, No. 1, pp. 83–97, 2014.
2. Tianjun Liao, Thomas Stützle, Marco A. Montes de Oca and Marco Dorigo, “A unified ant colony optimization algorithm
for continuous optimization”, European Journal of Operational Research, Vol. 234, No. 3, pp. 597–609, May 1, 2014.
196
• Saúl Zapotecas Martı́nez and Carlos A. Coello Coello, “A Memetic Algorithm with Non Gradient-Based
Local Search Assisted by a Meta-Model”, in Robert Schaefer, Carlos Cotta, Joanna Kolodziej and Günter
Rudolph (editors), Parallel Problem Solving from Nature–PPSN XI, 11th International Conference, Part I,
pp. 576–585, Springer, Lecture Notes in Computer Science Vol. 6238, Krakow, Poland, September 2010.
1. Yaochu Jin, “Surrogate-assisted evolutionary computation: Recent advances and future challenges”, Swarm and Evolutionary Computation, Vol. 1, No. 2, pp. 61–70, June 2011.
• Emilia Tantar, Oliver Schütze, José Rui Figueira, Carlos A. Coello Coello and El-Ghazali Talbi, “Computing and Selecting ε-Efficient Solutions of {0,1}-Knapsack Problems”, in Matthias Ehrgott, Boris Naujoks,
Theodor J. Stewart and Jyrki Wallenius (editors), Multiple Criteria Decision Making for Sustainable Energy
and Transportation Systems, pp. 379–389, Springer, Lecture Notes in Economics and Mathematical Systems
Vol. 634, Heidelberg, Germany, 2010.
1. Hu Xia, Jian Zhuang and Dehong Yu, “Multi-objective unsupervised feature selection algorithm utilizing redundancy
measure and negative epsilon-dominance for fault diagnosis”, Neurocomputing, Bol. 146, pp. 113–124, December 25,
2014.
• Oliver Schütze, Carlos Coello Coello and El-Ghazali Talbi, “Approximating the -Efficient Set of an MOP
with Stochastic Search Algorithms”, in Alexander Gelbukh and Ángel Fernando Kuri Morales (editors),
MICAI 2007: Advances in Artificial Intelligence, 6th International Conference on Artificial Intelligence,
pp. 128–138, Springer, Lecture Notes in Artificial Intelligence Vol. 4827, Aguascalientes, México, November
2007.
1. Hu Xia, Jian Zhuang and Dehong Yu, “Multi-objective unsupervised feature selection algorithm utilizing redundancy
measure and negative epsilon-dominance for fault diagnosis”, Neurocomputing, Vol. 146, pp. 113–124, December 25,
2014.
• Antonio López-Jaimes, Alfredo Arias-Montaño and Carlos A. Coello Coello, “Preference Incorporation to
Solve Many-Objective Airfoil Design Problems”, in 2011 IEEE Congress on Evolutionary Computation
(CEC’2011), pp. 1605–1612, IEEE Service Center, New Orleans, Louisiana, USA, 5-8 June, 2011.
1. Jürgen Branke, Salvatore Greco, Roman Slowinski and Piotr Zielniewicz, “Learning Value Functions in Interactive
Evolutionary Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp.
88–102, February 2015.
• Mario Garza-Fabre, Gregorio Toscano-Pulido, Carlos A. Coello Coello and Eduardo Rodrı́guez-Tello, “Effective Ranking + Speciation = Many-Objective Optimization”, in 2011 IEEE Congress on Evolutionary
Computation (CEC’2011), pp. 2115–2122, IEEE Service Center, New Orleans, Louisiana, USA, 5-8 June,
2011.
1. Miqing Li, Shengxiang Yang and Xiaohui Liu, “ Shift-Based Density Estimation for Pareto-Based Algorithms in ManyObjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 348–365, June 2014.
• Leticia C. Cagnina, Susana C. Esquivel and Carlos A. Coello Coello, “A Particle Swarm Optimizer for
Constrained Numerical Optimization”, in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke,
Juan J. Merelo-Guervós, L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving from Nature
(PPSN IX). 9th International Conference, Springer, pp. 910–919, Lecture Notes in Computer Science Vol.
4193, Reykjavik, Iceland, September 2006.
1. Saber M. Elsayed, Ruhul A. Sarker and Efren Mezura-Montes, “Self-adaptive mix of particle swarm methodologies for
constrained optimization”, Information Sciences, Vol. 277, pp. 216–233, September 1, 2014.
2. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionary
searches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,
March 2015.
• Antonio J. Nebro, Juan J. Durillo, Mirialys Machı́n, Carlos A. Coello Coello and Bernabé Dorronsoro, “A
Study of the Combination of Variation Operators in the NSGA-II Algorithm”, in Concha Bielza, Antonio
Salmerón, Amparo Alonso-Betanzos, J. Ignacio Hidalgo, Luis Martı́nez, Alicia Troncoso, Emilio Corchado
and Juan M. Corchado (Editors), 15th Conference of the Spanish Association for Artificial Intelligence,
CAEPIA 2013, pp. 269–278, Springer, Lecture Notes in Computer Science Vol. 8109, Madrid, Spain,
September 17-20, 2013.
197
1. Mengqi Hu, Jeffery D. Weir and Teresa Wu, “An augmented multi-objective particle swarm optimizer for building cluster
operation decisions”, Applied Soft Computing, Vol. 25, pp. 347–359, December 2014.
• Luis Miguel Antonio and Carlos A. Coello Coello, “Use of Cooperative Coevolution for Solving Large Scale
Multiobjective Optimization Problems”, in 2013 IEEE Congress on Evolutionary Computation (CEC’2013),
pp. 2758–2765, IEEE Press, Cancún, México, 20-23 June, 2013.
1. Sedigheh Mandavi, Mohammad Ebrahim Shiri and Shahryar Rahnamayan, “Metaheuristics in large-scale global continues
optimization: A survey”, Information Sciences, Vol. 295, pp. 407–428, February 20, 2015.
• Kunal Pal, Chiranjib Saha, Swagatam Das and Carlos A. Coello Coello, “Dynamic Constrained Optimization
with Offspring Repair based Gravitational Search Algorithm”, in 2013 IEEE Congress on Evolutionary
Computation (CEC’2013), pp. 2414–2421, IEEE Press, Cancún, México, 20-23 June, 2013.
1. Mohsen Davarynejad, Jan van den Berg and Jafar Rezaei, “Evaluating center-seeking and initialization bias: The case of
particle swarm and gravitational search algorithms”, Information Sciences, Vol. 278, pp. 802–821, September 10, 2014.
• Elizabeth Montero, Marı́a-Cristina Riff, Leslie Pérez-Caceres and Carlos A. Coello Coello, “Are State-of-theArt Fine-Tuning Algorithms Able to Detect a Dummy Parameter?”, in Carlos A. Coello Coello, Vincenzo
Cutello, Kalyanmoy Deb, Stephanie Forrest, Giuseppe Nicosia and Mario Pavone (Eds.), Parallel Problem
Solving from Nature - PPSN XII, 12th International Conference, pp. 306–315, Springer, Lecture Notes in
Computer Science Vol. 7491, Taormina, Italy, September 1-5, 2012, ISBN 978-3-642-32936-4.
1. Nasser R. Sabar, Masri Ayob, Graham Kendall and Rong Qu, “A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems”, IEEE Transactions on Cybernetics, Vol. 45, No.
2, pp. 217–228, February 2015.
• Alfredo Arias-Montaño, Carlos A. Coello Coello and Efrén Mezura-Montes, “Multi-Objective Airfoil Shape
Optimization Using a Multiple-Surrogate Approach”, in 2012 IEEE Congress on Evolutionary Computation
(CEC’2012), pp. 1188–1195, IEEE Press, Brisbane, Australia, June 10-15, 2012.
1. Rommel G. Regis, “Evolutionary Programming for High-Dimensional Constrained Expensive Black-Box Optimization
Using Radial Basis Functions”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 326–347, June
2014.
• Carlos A. Coello Coello, “Evolutionary Multi-Objective Optimization: Basic Concepts and Some Applications in Pattern Recognition”, in José Francisco Martı́nez-Trinidad, Jesús Ariel Carrasco-Ochoa, Cherif
Ben-Youssef Brants and Edwin Robert Hancock (Editors), Pattern Recognition, Third Mexican Conference,
MCPR 2011, pp. 22–33, Springer, Lecture Notes in Computer Science Vol. 6718, Cancun, México, June/July
2011.
“Physical programming for preference driven evolutionary multi-objective optimization”, Applied Soft Computing, Vol.
24, pp. 341–362, November 2014.
• Cynthia A. Rodrı́guez Villalobos and Carlos A. Coello Coello, “A New Multi-Objective Evolutionary Algorithm Based on a Performance Assessment Indicator”, in 2012 Genetic and Evolutionary Computation
Conference (GECCO’2012), pp. 505–515, ACM Press, Philadelphia, USA, July 7-11, 2012, ISBN 978-14503-1177-9.
1. Jie Zeng and Wei Nie, “Novel multi-objective optimization algorithm”, Journal of Systems Engineering and Electronics,
Vol. 25, No. 4, pp. 697–710, August 2014.
• Mario Garza Fabre, Gregorio Toscano Pulido and Carlos A. Coello Coello, “Alternative Fitness Assignment Methods for Many-Objective Optimization Problems”, in Pierre Collet, Nicolas Monmarché, Pierrick
Legrand, Marc Schoenauer and Evelyne Lutton (editors), Artificial Evolution, 9th International Conference,
Evolution Artificielle, EA 2009, pp. 146–157, Springer, Lecture Notes in Computer Science Vol. 5975,
Strasbourg, France, 2010.
1. Ruby L.V. Moritz, Enrico Reich, Maik Schwarz, Matthias Bernt and Martin Middendorf, “Refined ranking relations for
selection of solutions in multi objective metaheuristics”, European Journal of Operational Research, Vol. 243, No. 2, pp.
454–464, June 1, 2015.
2. Ramprasad Joshi and Bharat Deshpande, “Empirical and analytical study of many-objective optimization problems:
analysing distribution of nondominated solutions and population size for scalability of randomized heuristics”, Memetic
Computing, Vol. 6, No. 2, pp. 133–145, June 2014.
198
3. Hossein Karshenas, Roberto Santana, Concha Bielza and Pedro Larrañaga, “Multiobjective Estimation of Distribution
Algorithm Based on Joint Modeling of Objectives and Variables”, IEEE Transactions on Evolutionary Computation,
Vol. 18, No. 4, pp. 519–542, August 2014.
4. Zhenan He, Gary G. Yen and Jun Zhang, “Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms”,
IEEE Transactions on Evolutionary Computation, Vol. 18, No. 2, pp. 269–285, April 2014.
• Adriana Lara López, Carlos A. Coello Coello and Oliver Schuetze, “A Painless Gradient-Assisted MultiObjective Memetic Mechanism for Solving Continuous Bi-objective Optimization Problems”, in 2010 IEEE
Congress on Evolutionary Computation (CEC’2010), pp. 577–584, IEEE Press, Barcelona, Spain, July 18–23,
2010.
1. Hyoungjin Kim and Meng-Sing Liou, “Adaptive directional local search strategy for hybrid evolutionary multiobjective
optimization”, Applied Soft Computing, Vol. 19, pp. 290–311, June 2014.
2. Honggang Wang, “Zigzag Search for Continuous Multiobjective Optimization”, INFORMS Journal on Computing, Vol.
25, No. 4, pp. 654–665, Fall 2013.
• Saúl Zapotecas Martı́nez and Carlos A. Coello Coello, “A Direct Local Search Mechanism for Decompositionbased Multi-Objective Evolutionary Algorithms”, in 2012 IEEE Congress on Evolutionary Computation
(CEC’2012), pp. 3431–3438, IEEE Press, Brisbane, Australia, June 10-15, 2012.
1. Ke Li, Alvaro Fialho, Sam Kwong and Qingfu Zhang, “Adaptive Operator Selection With Bandits for a Multiobjective
Evolutionary Algorithm Based on Decomposition”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 1,
pp. 114–130, February 2014.
• Efrén Mezura-Montes and Carlos A. Coello Coello, “A Simple Evolution Strategy to Solve Constrained Optimization Problems”, in Erick Cantú-Paz et al. (editors), Genetic and Evolutionary Computation Conference—
GECCO’2003. Proceedings, Part I, Lecture Notes in Computer Science Vol. 2723, pp. 640–641, Springer,
Chicago, USA, July 2003.
1. Saber M. Elsayed, Ruhul A. Sarker and Daryl L. Essam, “ Adaptive Configuration of evolutionary algorithms for
constrained optimization”, Applied Mathematics and Computation, Vol. 222, pp. 680–711, October 1, 2013.
• Alfredo Arias Montaño, Carlos A. Coello Coello and Efrén Mezura-Montes, “pMODE-LD+SS: A Parallel
Multi-Objective DE-based Algorithm”, in Robert Schaefer, Carlos Cotta, Joanna Kolodziej and Günter
Rudolph (editors), Parallel Problem Solving from Nature–PPSN XI, 11th International Conference, Part II,
pp. 21–30, Springer, Lecture Notes in Computer Science Vol. 6239, Krakow, Poland, September 2010.
1. Hossein Rajabalipour Cheshmehgaz, Mohammad Ishak Desa and Antoni Wibowo, “Effective local evolutionary searches
distributed on an island model solving bi-objective optimization problems”, Applied Intelligence, Vol. 38, No. 3, pp.
331–356, April 2013.
2. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionary
searches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,
March 2015.
3. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “An effective model of multiple multiobjective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs”,
Applied Soft Computing, Vol. 13, No. 5, pp. 2863–2895, May 2013.
• Saúl Zapotecas Martı́nez and Carlos A. Coello Coello, “An Archive Strategy Based on the Convex Hull
of Individual Minima for MOEAs”, 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pp.
912–919, IEEE Press, Barcelona, Spain, July 18–23, 2010.
1. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
• Antonio López Jaimes, Hernán Aguirre, Kiyoshi Tanaka and Carlos A. Coello Coello, “Objective Space
Partitioning Using Conflict Information for Many-objective Optimization”, in Robert Schaefer, Carlos Cotta,
Joanna Kolodziej and Günter Rudolph (editors), Parallel Problem Solving from Nature–PPSN XI, 11th
International Conference, Part I, pp. 657–666, Springer, Lecture Notes in Computer Science Vol. 6238,
Krakow, Poland, September 2010.
1. Susmita Bandyopadhyay and Ranja Bhattacharya, “Solving a tri-objective supply chain problem with modified NSGA-II
algorithm”, Journal of Manufacturing Systems, Vol. 33, No. 1, pp. 41–50, January 2014.
199
2. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
• Saúl Zapotecas Martı́nez and Carlos A. Coello Coello, “A Multi-objective Particle Swarm Optimizer Based
on Decomposition”, in 2011 Genetic and Evolutionary Computation Conference (GECCO’2011), pp. 69–76,
ACM Press, Dublin, Ireland, July 12-16, 2011.
1. Wang Hu and Gary G. Yen, “Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate
System”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 1–18, February 2015.
2. Mengqi Hu, Jeffery D. Weir and Teresa Wu, “An augmented multi-objective particle swarm optimizer for building cluster
operation decisions”, Applied Soft Computing, Vol. 25, pp. 347–359, December 2014.
3. Yutao Qi, Xiaoliang Ma, Fang Liu, Licheng Jiao, Jianyong Sun and Jianshe Wu, “MOEA/D with Adaptive Weight
Adjustment”, Evolutionary Computation, Vol. 22, No. 2, pp. 231–264, Summer 2014.
4. N. Al Moubayed, A. Petrovski and J. McCall, “D2 MOPSO: MOPSO Based on Decomposition and Dominance with
Archiving Using Crowding Distance in Objective and Solution Spaces”, Evolutionary Computation, Vol. 22, No. 1, pp.
47–77, Spring 2014.
5. Guanghui Wang, Jie Chen, Tao Cai and Bin Xin, “Decomposition-based multi-objective differential evolution particle
swarm optimization for the design of a tubular permanent magnet linear synchronous motor”, Engineering Optimization,
Vol. 45, No. 9, pp. 1107–1127, September 1, 2013.
• Antonio López Jaimes, Carlos A. Coello Coello, Hernán Aguirre and Kiyoshi Tanaka, “Adaptive Objective
Space Partitioning Using Conflict Information for Many-Objective Optimization”, in Ricardo H.C. Takahashi, Kalyanmoy Deb, Elizabeth F. Wanner and Salvatore Grecco (editors), Evolutionary Multi-Criterion
Optimization, 6th International Conference, EMO 2011, pp. 151–165, Springer. Lecture Notes in Computer
Science Vol. 6576, Ouro Preto, Brazil, April 2011.
1. Susmita Bandyopadhyay and Ranja Bhattacharya, “Solving a tri-objective supply chain problem with modified NSGA-II
algorithm”, Journal of Manufacturing Systems, Vol. 33, No. 1, pp. 41–50, January 2014.
2. Miqing Li, Shengxiang Yang and Xiaohui Liu, “ Shift-Based Density Estimation for Pareto-Based Algorithms in ManyObjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 348–365, June 2014.
3. Christian von Lücken, Benjamı́n Barán and Carlos Brizuela, “A survey on multi-objective evolutionary algorithms for
many-objective problems”, Computational Optimization and Applications, Vol. 58, No. 3, pp. 707–756, July 2004.
4. Shengxiang Yang, Miqing Li, Xiaohui Liu and Jinhua Zheng, “ A Grid-Based Evolutionary Algorithm for Many-Objective
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 5, pp. 721–736, October 2013.
• Mario Garza Fabre, Gregorio Toscano Pulido and Carlos A. Coello Coello, “Two Novel Approaches for ManyObjective Optimization”, 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pp. 4480–4487,
IEEE Press, Barcelona, Spain, July 18–23, 2010.
1. Andre Britto and Aurora Pozo, “Using reference points to update the archive of MOPSO algorithms in Many-Objective
Optimization”, Neurocomputing, Vol. 127, pp. 78–87, March 15, 2014.
2. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
3. Shengxiang Yang, Miqing Li, Xiaohui Liu and Jinhua Zheng, “ A Grid-Based Evolutionary Algorithm for Many-Objective
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 5, pp. 721–736, October 2013.
• Mario Garza Fabre, Gregorio Toscano Pulido and Carlos A. Coello Coello, “Ranking Methods for ManyObjective Problems”, in Arturo Hernández Aguirre, Raúl Monroy Borja and Carlos Alberto Reyes Garcı́a
(editors), MICAI 2009: Advances in Artificial Intelligence. 8th Mexican International Conference on Artificial Intelligence, pp. 633–645, Springer, Lecture Notes in Artificial Intelligence Vol. 5845, Guanajuato,
México, November 2009.
1. Ruby L.V. Moritz, Enrico Reich, Maik Schwarz, Matthias Bernt and Martin Middendorf, “Refined ranking relations for
selection of solutions in multi objective metaheuristics”, European Journal of Operational Research, Vol. 243, No. 2, pp.
454–464, June 1, 2015.
2. Wang Hu and Gary G. Yen, “Adaptive Multiobjective Particle Swarm Optimization Based on Parallel Cell Coordinate
System”, IEEE Transactions on Evolutionary Computation, Vol. 19, No. 1, pp. 1–18, February 2015.
3. Kalyanmoy Deb and Himanshu Jain, “An Evolutionary Many-Objective Optimization Algorithm Using Reference-PointBased Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 4, pp. 577–601, August 2014.
200
4. Hossein Karshenas, Roberto Santana, Concha Bielza and Pedro Larrañaga, “Multiobjective Estimation of Distribution
Algorithm Based on Joint Modeling of Objectives and Variables”, IEEE Transactions on Evolutionary Computation,
Vol. 18, No. 4, pp. 519–542, August 2014.
5. Zhenan He, Gary G. Yen and Jun Zhang, “Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms”,
IEEE Transactions on Evolutionary Computation, Vol. 18, No. 2, pp. 269–285, April 2014.
6. Ruochen Liu, Chenlin Ma, Fei He, Wenping Ma and Licheng Jiao, “Reference direction based immune clone algorithm
for many-objective optimization”, Frontiers of Computer Science, Vol. 8, No. 4, pp. 642–655, August 2014.
7. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
8. Vui Ann Shim, Kay Chen Tan, Chun Yew Cheong and Jun Yong Chia, “Enhancing the scalability of multi-objective
optimization via restricted Boltzmann machine-based estimation of distribution algorithm”, Information Sciences, Vol.
248, pp. 191–213, November 1, 2013.
9. Eunice Oliveira, Carlos Henggeler Antunes and Alvaro Gomes, “A comparative study of different approaches using an
outranking relation in a multi-objective evolutionary algorithm”, Computers & Operations Research, Vol. 40, No. 6, pp.
1602–1615, June 2013.
• Adriana Lara, Carlos A. Coello Coello and Oliver Schütze, “Using Gradient-Based Information to Deal with
Scalability in Multi-objective Evolutionary Algorithms”, in 2009 IEEE Congress on Evolutionary Computation (CEC’2009), pp. 16–23, IEEE Press, Trodheim, Norway, May 2009.
1. Ramprasad Joshi and Bharat Deshpande, “Empirical and analytical study of many-objective optimization problems:
analysing distribution of nondominated solutions and population size for scalability of randomized heuristics”, Memetic
Computing, Vol. 6, No. 2, pp. 133–145, June 2014.
2. Vui Ann Shim, Kay Chen Tan, Chun Yew Cheong and Jun Yong Chia, “Enhancing the scalability of multi-objective
optimization via restricted Boltzmann machine-based estimation of distribution algorithm”, Information Sciences, Vol.
248, pp. 191–213, November 1, 2013.
3. L.C. Jiao, Handing Wang, R.H. Shang and F. Liu, “A co-evolutionary multi-objective optimization algorithm based on
direction vectors”, Information Sciences, Vol. 228, pp. 90–112, April 10, 2013.
• Julio Barrera and Carlos A. Coello Coello, “A Particle Swarm Optimization Method for Multimodal Optimization Based on Electrostatic Interaction”, in Arturo Hernández Aguirre, Raúl Monroy Borja and Carlos
Alberto Reyes Garcı́a (editors), MICAI 2009: Advances in Artificial Intelligence. 8th Mexican International Conference on Artificial Intelligence, pp. 622–632, Springer, Lecture Notes in Artificial Intelligence
Vol. 5845, Guanajuato, México, November 2009.
1. Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Songdong Xue and Yaochu Jin, “A new fitness estimation strategy for
particle swarm optimization”, Information Sciences, Vol. 221, pp. 355–370, February 1, 2013.
• Luis Vicente Santana-Quintero and Carlos A. Coello Coello, “An Algorithm Based on Differential Evolution
for Multiobjective Problems”, in Cihan H. Dagli, Anna L. Buczak, David L. Enke, Mark J. Embrechts and
Okan Ersoy (editors), Smart Engineering System Design: Neural Networks, Evolutionary Programming and
Artificial Life, Vol. 15, pp. 211–220, ASME Press, St. Louis, Missouri, USA, November 2005.
1. Gilberto Reynoso-Meza, Javier Sanchis, Xavier Blasco and Juan M. Herrero, “Multiobjective evolutionary algorithms
for multivariable PI controller design”, Expert Systems with Applications, Vol. 39, No. 9, pp. 7895–7907, July 2012.
• Adriana Menchaca-Mendez and Carlos A. Coello Coello, “A New Proposal to Hybridize the Nelder-Mead
Method to a Differential Evolution Algorithm for Constrained Optimization”, in 2009 IEEE Congress on
Evolutionary Computation (CEC’2009), pp. 2598–2605, IEEE Press, Trodheim, Norway, May 2009.
1. Mario Garza-Fabre, Eduardo Rodriguez-Tello and Gregorio Toscano-Pulido, “Constraint-handling through multi-objective
optimization: The hydrophobic-polar model for protein structure prediction”, Computers & Operations Research, Vol.
53, pp. 128–153, January 2015.
2. Musrrat Ali, Millie Pant, Atulya K. Nagar and Chang Wook Ahn, “Two Local Search Strategies for Differential Evolution”, Journal of Universal Computer Science, Vol. 18, No. 13, pp. 1853–1870, 2012.
3. Ali Wagdy Mohamed and Hegazy Zaher Sabry, “Constrained optimization based on modified differential evolution
algorithm”, Information Sciences, Vol. 194, pp. 171–208, July 1, 2012.
4. Abu S.S.M. Barkat Ullah, Ruhul Sarker and Chris Lokan, “Handling equality constraints in evolutionary optimization”,
European Journal of Operational Research, Vol. 221, No. 3, pp. 480–490, September 16, 2012.
201
• Luis V. Santana-Quintero, Noel Ramı́rez and Carlos Coello Coello, “A Multi-Objective Particle Swarm Optimizer Hybridized with Scatter Search”, in Alexander Gelbukh and Carlos Alberto Reyes-Garcı́a (Editors),
MICAI 2006: Advances in Artificial Intelligence, 5th International Conference in Artificial Intelligence,
Springer, pp. 294–304, Lecture Notes in Artificial Intelligence Vol. 4293, Apizaco, México, November 2006.
1. Zhigang Ren, Aimin Zhang, Changyun Wen and Zuren Feng, “A Scatter Learning Particle Swarm Optimization Algorithm for Multimodal Problems”, IEEE Transactions on Cybernetics, Vol. 44, No. 7, pp. 1127–1140, July 2014.
2. Lixin Tang and Xianpen Wang, “A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization
Problems”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 20–45, February 2013.
3. Tao Zhang, W.A. Chaovalitwongse and Yuejie Zhang, “Scatter search for the stochastic travel-time vehicle routing
problem with simultaneous pick-ups and deliveries”, Computers & Operations Research, Vol. 39, No. 10, pp. 2277–2290,
October 2012.
• Alfredo Arias Montaño, Carlos A. Coello Coello and Efrén Mezura-Montes, “MODE-LD+SS: A Novel Differential Evolution Algorithm Incorporating Local Dominance and Scalar Selection Mechanisms for MultiObjective Optimization”, 2010 IEEE Congress on Evolutionary Computation (CEC’2010), pp. 3284–3291,
IEEE Press, Barcelona, Spain, July 18–23, 2010.
1. Hossein Rajabalipour Cheshmehgaz, Habibollah Haron and Abdollah Sharifi, “The review of multiple evolutionary
searches and multi-objective evolutionary algorithms”, Artificial Intelligence Review, Vol. 43, No. 3, pp. 311–343,
March 2015.
2. Nguyen Long, Lam T. Bui and Hussein A. Abbass, “DMEA-II: the direction-based multi-objective evolutionary algorithmII”, Soft Computing, Vol. 18, No. 11, pp. 2119–2134, November 2014.
3. Musrrat. Ali, Patrick Siarry and Millie. Pant, “An efficient Differential Evolution based algorithm for solving multiobjective optimization problems”, European Journal of Operational Research, Vol. 217, No. 2, pp. 404–416, March 1,
2012.
• Vı́ctor Serrano, Matı́as Alvarado and Carlos A. Coello Coello, “Optimization to Manage Supply Chain
Disruptions Using the NSGA-II”, in Oscar Castillo, Patricia Melin, Oscar Montiel Ross, Roberto Sepúlveda
Cruz, Witold Pedrycz and Janusz Kacprzyk (editors), Theoretical Advances and Applications of Fuzzy Logic
and Soft Computing, pp. 476–485, Springer, 2007.
1. Jie Zhu, Xin Cai, Pan Pan and Rongrong Gu, “Multi-Objective Structural Optimization Design of Horizontal-Axis Wind
Turbine Blades Using the Non-Dominated Sorting Genetic Algorithm II and Finite Element Method”, Energies, Vol. 7,
No. 2, pp. 988–1002, February 2014.
2. N.C. Hiremath, Sadananda Sahu and Manoj Kuma Tiwari, “Multi objective outbound logistics network design for a
manufacturing supply chain”, Journal of Intelligent Manufacturing, Vol. 24, No. 6, pp. 1071–1084, December 2013.
3. Huanlai Xing and Rong Qu, “A nondominated sorting genetic algorithm for bi-objective network coding based multicast
routing problems”, Information Sciences, Vol. 233, pp. 36–53, June 1, 2013.
4. Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa and Antoni Wibowo, “A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm”, Journal of Intelligent
Manufacturing, Vol. 24, No. 2, pp. 277–293, April 2013.
5. S. Afshin Mansouri, David Gallear and Mohammad H. Askariazad, “Decision support for build-to-order supply chain
management through multiobjective optimization”, International Journal of Production Economics, Vol. 135, No. 1,
pp. 24–36, January 2012.
• Antonio J. Nebro, Juan J. Durillo, Jose Garcia-Nieto, Carlos A. Coello Coello, Francisco Luna and Enrique
Alba, “SMPSO: A New PSO-based Metaheuristic for Multi-objective Optimization”, in 2009 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, pp. 66–73, IEEE Press, Nashville,
Tennessee, USA, March 30 - April 2, 2009.
1. Sofiene Kachroudi, Mathieu Grossard and Neil Abroug, “Predictive Driving Guidance of Full Electric Vehicles Using
Particle Swarm Optimization”, IEEE Transactions on Vehicular Technology, Vol. 61, No. 9, pp. 3909–3919, November
2012.
2. Sandra Garcia, David Quintana, InS M. Galvan and Pedro Isasi, “Extended mean-variance model for reliable evolutionary
portfolio optimization”, AI Communications, Vol. 27, No. 3, pp. 315–324, 2014.
3. Xiaoguang He, Cai Dai and Zehua Chen, “Many-Objective Optimization Using Adaptive Differential Evolution with a
New Ranking Method”, Mathematical Problems in Engineering, Article Number: 259473, 2014.
4. Cai Dai, Yuping Wang and Miao Ye, “A new evolutionary algorithm based on contraction method for many-objective
optimization problems”, Applied Mathematics and Computation, Vol. 245, pp. 191–205, October 15, 2014.
202
5. Shuai Wang, Shaukat Ali and Arnaud Gotlieb, “Cost-effective test suite minimization in product lines using search
techniques”, Journal of Systems and Software, Vol. 103, pp. 370–391, May 2015.
6. Yu-Bin Zhong, Yi Xiang and Hai-Lin Liu, “A multi-objective artificial bee colony algorithm based on division of the
searching space”, Applied Intelligence, Vol. 41, No. 4, pp. 987–1011, December 2014.
7. Siwei Jiang, Yew-Soon Ong, Jie Zhang and Liang Feng, “Consistencies and Contradictions of Performance Metrics in
Multiobjective Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2391–2404, December 2014.
8. Amir Nejat, Pooya Mirzabeygi and Masoud Shariat Panahi, “Airfoil shape optimization using improved Multiobjective
Territorial Particle Swarm algorithm with the objective of improving stall characteristics”, Structural and Multidisciplinary Optimization, Vol. 49, No. 6, pp. 953–967, June 2014.
9. Amin Ibrahim, Shahryar Rahnamayan, Miguel Vargas Martin and Bekir Yilbas, “Multi-objective thermal analysis of a
thermoelectric device: Influence of geometric features on device characteristics”, Energy, Vol. 77, pp. 305–317, December
1, 2014.
10. Kian Sheng Lim, Zuwairie Ibrahim, Salinda Buyamin, Anita Ahmad, Faradila Naim, Kamarul Hawari Ghazali, Norrima Mokhtar, “Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions”,
Scientific World Journal, Article Number: 510763, 2013.
11. Kian Sheng Lim, Salinda Buyamin, Anita Ahmad, Mohd Ibrahim Shapiai, Faradila Naim, Marizan Mubin and Dong
Hwa Kim, “Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders”, Scientific
World Journal, Article Number: 364179, 2014.
12. Immanuel Trummer, Boi Faltings and Walter Binder, “Multi-Objective Quality-Driven Service Selection-A Fully Polynomial Time Approximation Scheme”, IEEE Transactions on Software Engineering, Vol. 40, No. 2, pp. 167–191, February
2014.
13. Roman Stryczek and Boguslaw Pytlak, “Multi-Objective Optimization with Adjusted PSO Method on Example of
Cutting Process of Hardened 18CrMo4 Steel”, Eksploatacja Niezawodnosc–Maintenance and Reliability, No. 1, pp.
236–245, 2014.
14. Andre Britto and Aurora Pozo, “Using reference points to update the archive of MOPSO algorithms in Many-Objective
Optimization”, Neurocomputing, Vol. 127, pp. 78–87, March 15, 2014.
15. Eduardo J. Solteiro Pires, Jose A. Tenreiro Machado and Paulo B. de Moura Oliveira, “Entropy Diversity in MultiObjective Particle Swarm Optimization”, Entropy, Vol. 15, No. 12, pp. 5475–5491, December 2013.
16. Jun Guo, Jianzhong Zhou, Qiang Zou, Yi Liu and Lixiang Song, “A Novel Multi-Objective Shuffled Complex Differential
Evolution Algorithm with Application to Hydrological Model Parameter Optimization”, Water Resources Management,
Vol. 27, No. 8, pp. 2923–2946, June 2013.
17. Sandra Garcia, David Quintana, Ines M. Galvan and Pedro Isasi, “Multiobjective Algorithms with Resampling for
Portfolio Optimization”, Computing and Informatics, Vol. 32, No. 4, pp. 777–796, 2013.
18. Yan Wang and Jian-chao Zeng, “A multi-objective artificial physics optimization algorithm based on ranks of individuals”, Soft Computing, Vol. 17, No. 6, pp. 939–952, June 2013.
19. Xianpeng Wang and Lixin Tang, “ Multiobjective Operation Optimization of Naphtha Pyrolysis Process Using Parallel
Differential Evolution”, Industrial & Engineering Chemistry Research, Vol. 52, No. 40, pp. 14415–14428, October 9,
2013.
20. Lixin Tang and Xianpen Wang, “A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization
Problems”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 1, pp. 20–45, February 2013.
21. Youcef Bouchebaba, Ali-Erdem Ozcan, Pierre Paulin and Gabriela Nicolescu, “MpAssign: a framework for solving the
many-core platform mapping problem”, Software–Practice & Experience, Vol. 42, No. 7, pp. 891–915, July 2012.
22. Andre B. de Carvalho and Aurora Pozo, “Measuring the convergence and diversity of CDAS Multi-Objective Particle
Swarm Optimization Algorithms: A study of many-objective problems”, Neurocomputing, Vol. 75, No. 1, pp. 43–51,
January 1, 2012.
• Oliver Schütze, El-Ghazali Talbi, Carlos Coello Coello and Luis Vicente Santana-Quintero, “A Memetic
PSO Algorithm for Scalar Optimization Problems”, in Proceedings of the 2007 IEEE Swarm Intelligence
Symposium (SIS 2007), pp. 128–134, IEEE Press, Honolulu, Hawaii, USA, April 2007.
1. Hongfeng Wang, Ilkyeong Moon, Shenxiang Yang and Dingwe Wang, “A memetic particle swarm optimization algorithm
for multimodal optimization problems”, Information Sciences, Vol. 197, pp. 38–52, August 15, 2012.
2. Karthik Sindhya, Sauli Ruuska, Tomi Haanpää and Kaisa Miettinen, “A new hybrid mutation operator for multiobjective
optimization with differential evolution”, Soft Computing, Vol. 15, No. 10, pp. 2041–2055, October 2011.
203
• Alfredo G. Hernandez-Diaz, Carlos A. Coello Coello, Luis V. Santana-Quintero, Fatima Perez, Julian Molina
and Rafael Caballero, “On the use of Projected Gradients for Constrained Multiobjective Optimization
Problems”, in Günter Rudolph, Thomas Jansen, Simon Lucas, Carlo Poloni and Nicola Beume (editors),
Parallel Problem Solving from Nature–PPSN X, pp. 712–721, Springer, Lecture Notes in Computer Science
Vol. 5199, Dortmund, Germany, September 2008.
1. Gang Yu, Tianyou Chai and Xiaochuan Luo, “Multiobjective Production Planning Optimization Using Hybrid Evolutionary Algorithms for Mineral Processing”, IEEE Transactions on Evolutionary Computation, Vol. 15, No. 4, pp.
487–514, August 2011.
• Antonio López Jaimes and Carlos A. Coello Coello, “Study of Preference Relations in Many-Objective
Optimization”, in 2009 Genetic and Evolutionary Computation Conference (GECCO’2009), pp. 611–618,
ACM Press, Montreal, Canada, July 8–12, 2009, ISBN 978-1-60558-325-9.
1. Miqing Li, Shengxiang Yang and Xiaohui Liu, “ Shift-Based Density Estimation for Pareto-Based Algorithms in ManyObjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 348–365, June 2014.
2. Miqing Li, Shengxiang Yang and Xiaohui Liu, “Diversity Comparison of Pareto Front Approximations in Many-Objective
Optimization”, IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2568–2584, December 2014.
3. Christian von Lücken, Benjamı́n Barán and Carlos Brizuela, “A survey on multi-objective evolutionary algorithms for
many-objective problems”, Computational Optimization and Applications, Vol. 58, No. 3, pp. 707–756, July 2004.
4. Andre Britto and Aurora Pozo, “Using reference points to update the archive of MOPSO algorithms in Many-Objective
Optimization”, Neurocomputing, Vol. 127, pp. 78–87, March 15, 2014.
5. Shengxiang Yang, Miqing Li, Xiaohui Liu and Jinhua Zheng, “ A Grid-Based Evolutionary Algorithm for Many-Objective
Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 17, No. 5, pp. 721–736, October 2013.
6. Andre B. de Carvalho and Aurora Pozo, “Measuring the convergence and diversity of CDAS Multi-Objective Particle
Swarm Optimization Algorithms: A study of many-objective problems”, Neurocomputing, Vol. 75, No. 1, pp. 43–51,
January 1, 2012.
7. Slim Bechikh, Lamjed Ben Said and Khaled Ghédira, “Searching for knee regions of the Pareto front using mobile
reference points”, Soft Computing, Vol. 15, No. 9, pp. 1807–1823, 2011.
• Oliver Schuetze, Carlos A. Coello Coello, Emilia Tantar and El-Ghazali Talbi, “Computing Finite Size Representations of the Set of Approximate Solutions of an MOP with Stochastic Search Algorithms”, in 2008
Genetic and Evolutionary Computation Conference (GECCO’2008), pp. 713–720, ACM Press, Atlanta,
USA, July 2008, ISBN 978-1-60558-131-6.
1. Yu Chen, Xiufen Zou and Weicheng Xie, “Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front”, Information Sciences, Vol. 181, No. 16, pp. 3336–3355, August 15,
2011.
• Victoria S. Aragón, Susana C. Esquivel and Carlos A. Coello Coello, “A Novel Model of Artificial Immune
System for Solving Constrained Optimization Problems with Dynamic Tolerance Factor”, in Alexander Gelbukh and Ángel Fernando Kuri Morales (editors), MICAI 2007: Advances in Artificial Intelligence, 6th
International Conference on Artificial Intelligence, pp. 19–29, Springer, Lecture Notes in Artificial Intelligence Vol. 4827, Aguascalientes, México, November 2007.
1. Weiwei Zhang, Gary G. Yen and Zhongshi He, “Constrained Optimization via Artificial Immune System”, IEEE Transactions on Cybernetics, Vol. 44, No. 2, pp. 185–198, February 2014.
2. Jianyong Chen, Qiuzhen Lin and LinLin Shen, “An Immune-Inspired Evolution Strategy for Constrained Optimization
Problems”, International Journal on Artificial Intelligence Tools, Vol. 20, No. 3, pp. 549–561, June 2011.
• Efrén Mezura Montes and Carlos A. Coello Coello, “Useful Infeasible Solutions in Engineering Optimization
with Evolutionary Algorithms”, in Alexander Gelbukh, Álvaro de Albornoz and Hugo Terashima-Marı́n
(editors), MICAI 2005: Advances in Artificial Intelligence, Springer, pp. 652–662, Lecture Notes in Artificial
Intelligence Vol. 3789, Monterrey, México, November 2005.
1. Ali Husseinzadeh Kashan, “An effective algorithm for constrained optimization based on optics i
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