Fusion of Learning Automata to OptimizeMulti-constraint Problem
سال انتشار: 1393
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 402
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شناسه ملی سند علمی:
JR_JIST-3-1_004
تاریخ نمایه سازی: 9 اسفند 1395
چکیده مقاله:
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
نویسندگان
Sara Motamed
Department of Computer Engineering, Fuman Branch, Islamic Azad University, Fuman, Iran
Ali Ahmadi
Department of Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran