Article


Article Code : 139308051146232811(DOI : 10.7508/jist.2015.01.003)

Article Title : Fusion of Learning Automata to Optimize Multi-constraint Problem

Journal Number : 9 Winter 2015

Visited : 864

Files : 359 KB


List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Sara Motamed Samotamed@yahoo.com Teacher Assistant PhD
2 Ali Ahmadi ahmadi@eetd.kntu.ac.ir Assistant Professor PhD

Abstract

This paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable structures are a reinforcement learning method. Having no information about optimized operation, such models try to find an answer to a problem. Converging speed in such algorithms in solving different problems and their route to the answer is so that they produce a proper condition if the answer is obtained. However, despite all tricks to prevent the algorithm involvement with local optimal, the algorithms do not perform well for problems with a lot of spread local optimal points and give no good answer. In this paper, the fusion of stochastic learning automata algorithms has been used to solve given problems and provide a centralized control mechanism. Looking at the results, is found that the recommended algorithm for partitioning constraints and finding optimization problems are suitable in terms of time and speed, and given a large number of samples, yield a learning rate of 97.92%. In addition, the test results clearly indicate increased accuracy and significant efficiency of recommended systems compared with single model systems based on different methods of learning automata.