Fusion of Learning Automata to Optimize Multi-constraint Problem

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.