Fusion of Learning Automata to OptimizeMulti-constraint Problem

سال انتشار: 1393
محل انتشار: فصلنامه سیستم های اطلاعاتی و مخابرات، دوره: 3، شماره: 1
کد COI اختصاصی: JR_JIST-3-1_004
زبان مقاله: انگلیسی
تعداد مشاهده: 406

نویسندگان

Department of Computer Engineering, Fuman Branch, Islamic Azad University, Fuman, Iran

Department of Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran

چکیده

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.

کلیدواژه ها

Stochastic Automata with Fixed and Variable Structures; Discrete Generalized Pursuit Automata; Fusion Method; Parallel Processing

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