Download cpsc_20371_20biblio

Bibliography
Bonabeau E., M. Dorigo & T. Theraulaz (1999). From Natural to Artificial Swarm
Intelligence. New York: Oxford University Press.
Colorni A., M. Dorigo & V. Maniezzo (1992). “Distributed Optimization by Ant
Colonies.” Proceedings of the First European Conference on Artificial Life, Paris,
France, F.Varela and P.Bourgine (Eds.), Elsevier Publishing, 134-142.
Colorni A., M. Dorigo & V. Maniezzo (1992). “An Investigation of Some Properties of
an Ant Algorithm.” Proceedings of the Parallel Problem Solving from Nature
Conference (PPSN 92), Brussels, Belgium, R.Männer and B.Manderick (Eds.),
Elsevier Publishing, 509-520.
Colorni A., M. Dorigo, V. Maniezzo and M. Trubian (1994). “Ant system for Job-shop
Scheduling.” JORBEL - Belgian Journal of Operations Research, Statistics and
Computer Science, 34(1):39-53.
Dorigo M., V. Maniezzo & A. Colorni (1991). “Positive Feedback as a Search Strategy.”
Technical Report No. 91-016, Politecnico di Milano, Italy.
Dorigo M., V. Maniezzo & A. Colorni (1991). “The Ant System: An Autocatalytic
Optimizing Process.” Technical Report No. 91-016 Revised, Politecnico di Milano,
Italy.
Dorigo M., V. Maniezzo & A. Colorni (1996). “The Ant System: Optimization by a
Colony of Cooperating Agents.” IEEE Transactions on Systems, Man, and
Cybernetics-Part B, 26(1):29-41.
Dorigo M. & L.M. Gambardella (1996). “A Study of Some Properties of Ant-Q.”
Proceedings of PPSN IV-Fourth International Conference on Parallel Problem
Solving From Nature, September 22-27, 1996, Berlin, Germany, Berlin: SpringerVerlag, 656Ð665.
Dorigo M. & L.M. Gambardella (1997). “Ant Colonies for the Traveling Salesman
Problem.” BioSystems, 43:73-81.
The paper “Ant Colonies for the Traveling Salesman”, describes an artificial ant
colony capable of solving the traveling salesperson problem. Ants are the agents which
move from city to city, on a TSP graph. The ant chooses the city to move to using a
probabilistic function both of trails accumulated on edges and of a heuristic value, which
is a function of the edges length. The paper also describes how the artificial ant’s discrete
pheromones on the edges used. The paper also shows some results run in a simulation
program. The results are presented in Tables, graphs and the documentation contains the
different parameters the simulations were run on (e.g. number of ants, edge length, etc.).
Dorigo M., G. Di Caro & L. M. Gambardella (1999). “Ant Algorithms for Discrete
Optimization.” Artificial Life, 5(2):137-172.
The paper “Ant Algorithms for Discrete Optimization” looks at some recent work
on ant algorithms for discrete algorithms. The first part of the paper describes the basic
biological findings on real ants. Then it describes the concept of ant colony
optimization (ACO) meta-heuristics in how a colony of artificial ants cooperates in
finding good solutions to discrete optimization problems. Some ideas of ACO that this
paper looks at are (1) Colony of cooperating individuals. (2) Pheromone trail and
stigmergy, (3) Shortest path searching and local moves and (4) Stochastic and myopic
state transition policy. The second part of the paper a number of applications to
combinatorial optimization and routing in communications networks are described.
The following problems are briefly discussed:
1.
2.
3.
4.
5.
6.
7.
8.
Quadratic assignment problem
Job-shop scheduling problem
Vehicle routing problem
Shortest common super sequence problem
Graph coloring problem
Sequential ordering problem
Connection-oriented networks routing
connectionless networks routing
The conclusion is a discussion of related work and some different aspects of the ACO
meta-heuristics.
Dorigo, M., G. Di Caro & T Stützle (2000). “Ant Colony Organization.” Future
Generation Computer Systems Journal.
Forsyth P. and A. Wren (1997). “An Ant System for Bus Driver Scheduling.” Presented
at the 7th International Workshop on Computer-Aided Scheduling of Public
Transport, Boston, August 1997.
Gambardella L.M. & M. Dorigo (1995). “Ant-Q: A Reinforcement Learning Approach
to the Traveling Salesman Problem.” Proceedings of ML-95, Twelfth International
Conference on Machine Learning, Tahoe City, CA, A. Prieditis and S. Russell
(Eds.), Morgan Kaufmann, 252-260.
Maniezzo V., A. Colorni and M. Dorigo (1994). “The Ant System Applied to the
Quadratic Assignment Problem.” Tech. Rep. IRIDIA/94-28, Université Libre de
Bruxelles, Belgium.
Stützle T. and H. Hoos (1997). “The MAX-MIN Ant System and Local Search for the
Traveling Salesman Problem.” Proceedings of ICEC'97 - 1997 IEEE 4th
International Conference on Evolutionary Computation, IEEE Press, 308-313.
Stützle T. and H. Hoos (1997). “Improvements on the Ant System: Introducing the
MAX-MIN Ant System.” ICANNGA97 - Third International Conference on
Artificial Neural Networks and Genetic Algorithms, University of East Anglia,
Norwich, UK, Wien: Springer Verlag.
Stützle T. and M. Dorigo (1999). “ACO Algorithms for the Traveling Salesman
Problem.” In K. Miettinen, M. Makela, P. Neittaanmaki, J. Periaux, editors,
Evolutionary Algorithms in Engineering and Computer Science, Wiley, 1999.
Stützle T. and M. Dorigo (1999). “ACO Algorithms for the Quadratic Assignment
Problem.” In D. Corne, M. Dorigo and F. Glover, editors, New Ideas in
Optimization, McGraw-Hill.