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Implementation of Parallel Optimized ABC Algorithm with SMA Technique for Garlic Expert Advisory System First Author N.Thirupathi Rao M.Tech (CST with AI & R) Andhra University Visakhapatnam Email: [email protected] Second Author Prof. M Surendra Prasad Babu Dept. of CS & SE Andhra University Visakhapatnam Email: [email protected] ‘Garlic Expert Advisory System’. Abstract: The present paper deals with This system is aimed at identifying the development of expert systems the using machine learning techniques management to advice the farmers in villages production to advise the farmers in through online. An expert system the villages on line to obtain is a computer program, with a set standardized yields. This advisory of rules encapsulating knowledge system is designed by using JSP as about a particular problem domain. front end and MYSQL as backend. Machine Learning is a mechanism, Key words: Expert Systems, diseases and in disease garlic crop used in the development of Expert Machine Learning, systems, concerned with writing a Algorithm, SMA computer that Garlic Crop, Optimization, JSP & with MYSQL. program automatically improves experience. ABC Algorithm was Introduction: considered as base and proposed a Expert Systems: ABC Technique, new technique known as ‘Shared Expert systems can be defined as a tool Memory for Technique) Algorithm Architecture’ to design named (SMA a information knowledge. new Expert implementations Parallel generation System from (ES) automatically Optimized ABC Algorithm with perform tasks for which specially SMA this trained or talented people required. ABC Expert systems are most common in a Algorithm we developed a new specific problem domain, traditional Parallel Technique. Using Optimized application and subfield of artificial 1 intelligence. A wide variety of Maharashtra and Bihar are the methods can be used to study the premium producers of Garlic in India. performance of an expert system. There are about six popular varieties in Expert systems might have learning garlic. Garlic has germanium in it. components but a common element is Germanium is an anti-cancer agent, that once the system is developed, it is and garlic has more of it than any other proven by placing it in the same real herb. Another benefit of garlic is it world problem solving situation as the helps human SME, typically which is an aid pressure. to human workers or a supplement to problems with low or high blood some The pressure, garlic can help equalize it. In sequence of steps followed to reach a addition to all these health benefits, conclusion is dynamically synthesized garlic is packed with vitamins and with each new case. It is not explicitly nutrients. Some of these include programmed when the system is built. protein, potassium, Vitamins A, B, B2 Problem solving is accomplished by and C, Calcium, Zinc and many others. applying specific knowledge rather Machine Learning: information system. regulate So the body's whether blood you have Machine Learning is a mechanism than specific technique. This is a key idea in expert systems technology. that concerned with writing a computer Garlic: program that automatically improves Garlic is one of the most commonly with experience. It is a very young used vegetables in India. Garlic is also scientific discipline whose birth can be known as Lassan and its botanical placed in the mid-seventies. The First name is Allium sativa Linn. It belongs Machine Learning Workshop was to the Lilliaceae family and is known taken place in 1980 at Carnie-Mellon by several many names in different University parts of India. Its Sanskrit name is machine learning is to program the Lashuna. Garlic is called as Velluri in computers such that to use example Telugu. is data or past experience to solve a given cultivated in all over India but problem. There were many successful Rajasthan, applications of machine learning exists Even though Karnataka, garlic TamilNadu, 2 (USA). The goal of today, including systems that analyze optimization problems. It is very past sales data to predict customer simple and flexible when compared to behavior, recognize faces or spoken the other Swarm Based algorithms speech, optimize robot behavior so that such as Particle Swarm Optimization a using (PSO). It does not require external extract parameters like mutation and crossover task can minimum be completed resources, and knowledge from bioinformatics data. rates, which are hard to determine in ABC Algorithm: prior. The algorithm combines local The Artificial Bee Colony (ABC) search methods with global search Algorithm [1, 3, and 4] is a meta-heuristic methods and tries to attain a balance algorithm for numerical optimization. between exploration and exploitation. Meta-heuristics high-level Researchers have come up with several strategies for exploring search spaces. real-world applications for the ABC Many algorithm. are meta-heuristic algorithms, inspired from nature, are efficient in Proposed System: solving optimization Here a web based application of problems. ABC algorithm is motivated Expert advisory system for garlic crop by the intelligent foraging behavior of is developed by using ABC Algorithm [6, 7] as base and modified this ABC was first proposed by Karaboga in Algorithm. The proposed Architecture 2005 for unconstrained optimization is as follows: numerical honey bees. The ABC algorithm problems. Subsequently, the algorithm has been developed by Karaboga and Basturk and extended to constrained optimization problems. Improvements to the performance of the algorithm and a hybrid version of the algorithm have been also been proposed. The Proposed Algorithm: ABC algorithm is a swarm-based In general, parallel architectures may algorithm good at solving unimodal use either a shared memory or a and message multimodal numerical 3 passing mechanism to Step.3. Steps 4 to 10 are carried out in parallel at each processor Pr. Step.4. For each solution in the local memory Mr of the range processor Pr, determine a neighbor. Step.5. Calculate the optimization for the solutions in Mr. Step.6. Place the onlookers on the food sources in Mr and improve the corresponding solutions (as in step 4). Step.7. Determine the abandoned solution (if any) in Mr and replace it with a new randomly produced solution. Step.8. Record the best local solution obtained till now at Pr. Step.9. Copy the solutions in Mr to the corresponding slots in S. Step.10. Repeat steps 4 to 9 until MCN cycles are completed. Step.11. Determine the global best solution among the best local solutions recorded at each processor. communicate in between the multiple processing elements. Parallel metaheuristic algorithms have been developed for both these kinds of architectures. A parallel implementation of the algorithm is designed for an optimized shared memory architecture, which overcomes these dependencies. The entire colony of bees is divided equally among the available processors. Each processor has a set of solutions in a local memory. A copy of each solution is also maintained in a global shared memory. During each cycle the set of bees at a processor improves the Database Generation: solutions in the local memory. The output optimization can be taken as the In this section, the setup for production rules in the knowledge base is presented. Generally the rules are of the form, total number of symptoms matching divided by the total number of Rule 1: S1=1,S2= 0,S3= 0,S4= 0, S5=0,S6= 1,S7= 0,S8=1, S9= 0,S10= 0,S11= 0,S12= 0 Resultant disease may be D1 symptoms in the system. At the end of the cycle, the solutions are copied into the corresponding slots in the shared memory by overwriting the previous Rule 2: S1= 1,S2=1 ,S3= 0 ,S4= 0, S5= 0,S6= 0 ,S7=1,S8= 0 ,S9= 0 ,S10= 0 ,S11=0,S12= 1Resultant disease may be D2 copies. The solutions are thus made available to all the processors. Step.1. Generate SN initial solutions randomly and evaluate them. Place them in the shared memory S. Step.2. Divide the solutions equally among p processors by copying SNp solutions to the local memory of each processor. Rule 3: S1= 0,S2= 1 ,S3= 0 ,S4= 0 , S5= 1,S6= 1 ,S7= 0,S8= 0 ,S9= 0 ,S10=1 ,S11=0 ,S12= 0 Resultant disease may be D3. 4 D i s e a s e S S S S S S S S S S S S S C 1 2 3 4 5 6 7 8 9 1 1 1 1 u 0 1 2 3 r e D 1 D 2 D 3 D 4 D 5 D 6 1 1 0 0 0 0 0 0 0 0 0 0 0 C 1 0 0 1 0 1 1 0 0 0 0 0 0 0 C 2 0 0 0 1 0 0 1 1 0 0 0 0 0 C 3 0 0 1 0 0 0 1 1 0 0 1 0 0 C 4 0 0 0 0 0 0 0 0 1 1 0 1 0 C 5 0 0 0 0 0 0 0 0 0 0 1 1 1 C 6 Fig: 3. Displaying advice to the end user Conclusions: In the proposed system, A Parallel Implementation of Artificial Colony Bee Optimized (ABC) Algorithm was developed which gives better results implementation Table:1 Database format compared of general to ABC Algorithm. In the present investigation Test Results: it was found that, the Parallel Optimized ABC Algorithm with SMA Technique gives a better optimization compared with general ABC Algorithm. The algorithm used in the present system can be treated as quite effective; in most of the cases it finds a Fig:1. Selection of Symptoms solution which represents a good approximation to the optimal one. Its main emphasis is to have a well designed interface for giving garlic plant related advices and suggestions in the area to farmers by providing facilities Fig:2. Selection of Symptoms like online interaction between expert system and the user without the need of expert all times. 5 By the thorough interaction with the Optimization users Advances and beneficiaries the Problems, in Soft LNCS: Computing: functionality of the System can be Foundations of Fuzzy Logic and Soft extended further to many more areas in Computing, Vol: 4529/2007, pp: 789- and around the world. 798, Springer- Verlag, 2007, IFSA References: 2007. doi: 10.1007/978-3-540-72950- 1. B.Basturk, Dervis Karaboga, An 1_77 Artificial Bee Algorithm for Optimization, Colony (ABC) 5. D. Karaboga, B. Basturk Akay, Numeric function Artificial Bee Colony Algorithm on IEEE Swarm Training Artificial Neural Networks, Intelligence Symposium 2006, May Signal 12-14, 2006, Indianapolis, Indiana, Communications Applications, 2007. USA. SIU 2007, IEEE 15th. 11-13 June 2. D. Karaboga, B. Basturk, A 2007, Powerful and Efficient Algorithm for 10.1109/SIU.2007.4298679 Numerical Function 6. D. Karaboga, B. Basturk Akay, C. Artificial Bee Algorithm, Optimization: Colony Journal of Processing Page(s):1 - and 4, doi: (ABC) Ozturk, Artificial Bee Colony (ABC) Global Optimization Algorithm for Training Optimization, Volume: 39, Issue: 3, Feed-Forward pp: 459-471, Springer Netherlands, LNCS: 2007. doi: 10.1007/s10898-007-9149. Artificial Intelligence, Vol: 4617/2007, 3. D. Karaboga, B. Basturk, On The pp:318-319, Springer-Verlag, 2007, Performance Of Artificial Bee Colony MDAI 2007. doi: 10.1007/978-3-540- (ABC) 73729-2_30 Algorithm, Computing, January Volume 2008, Applied 8, Pages Issue Soft Neural Modeling Networks, Decisions for 1, 7. J. Vesterstrøm, J. Riget, Particle 687-697. swarms extensions for improved local, doi:10.1016/j.asoc.2007.05.007 multimodal and dynamic search in 4. D. Karaboga, B. Basturk, Artificial numerical optimization, M.Sc. Thesis, Bee May 2002. Colony (ABC) Optimization Algorithm for Solving Constrained 6