MODIFIED UNIVARIATE MARGINAL DISTIBUTION ALGORITHM COMBINATION WITH EXTREMAL OPTIMIZATION AND LEARNING AUTOMATA
UMDA algorithm is a type of Estimation of Distribution Algorithms.This algorithm has better performance compared to others such as genetic algorithm in terms of speed, memory consumption and accuracy of solutions.It can explore unknown parts of search space well.It uses a probability vector and individuals of the population are created through the sampling.Furthermore, EO algorithm is suitable for local search of near global best solution in search space, and it does not stuck in local optimum. Hence, combining these two algorithms is able to create interaction between two fundamental concepts in evolutionary algorithms, exploration and exploitation, and achieve better results of this paper is used adaptive version of τ -EO algorithm called EO-LA.In this method the task of choosing a replacement component is assigned to Learning Automata.During the implementation of this algorithm, according to the suitability of produced solutions, feedback signals are sent to Learning Automata until adapt selected replacement component well.In this paper, results represent the better performance of the proposed algorithm (combination of three methods) on a Graph Bipartitioning, NP-hard problem.
Univariate Marginal Distribution Algorithm Extremal Optimization Learning Automata Estimation of Distribution Algorithm optimization problem
MITRA HASHEMI MOHAMMAD REZA MEYBODI
Department of Computer Engineering and Information Technology,Islamic Azad University Qazvin Branch, Department of Computer Engineering and Information Technology,Amirkabir University of Technology,Teh
国际会议
成都
英文
1590-1594
2011-11-25（万方平台首次上网日期，不代表论文的发表时间）