Research on Increasing the Performance of Evolutionary Algorithm in Searching Robust Optimal Solutions Based on Quasi-Monte Carlo Method
ZHU Yun-Fei1,2, LUO Biao2, ZHENG Jin-Hua2, CAI Zi-Xing1
1.School of Information Science and Engineering, Central South University, Changsha 410083 2.Collage of Information Engineering, Xiangtan University, Xiangtan 411105
Abstract Robust optimal solution is of great significance in engineering application. It is one of the most important and difficult topics in evolutionary computation. Monte Carlo Integral (MCI) is generally used to approximate effective objective function (EOF) in searching robust optimal solution with evolutionary algorithm (EA). However, due to the low accuracy in existing crude Monte Carlo (C-MC) method, the performance of searching robust optimal solution with EA is unsatisfactory. Therefore, a Quasi-Monte Carlo (Q-MC) method is proposed to estimate EOF. The experimental results demonstrate that the proposed Q-MC methods-SQRT sequence, SOBOL sequence and Korobov Lattice approximate EOF more precisely compared with C-MC method, and consequently, the performance of searching robust optimal solution with EA is improved.
ZHU Yun-Fei,LUO Biao,ZHENG Jin-Hua等. Research on Increasing the Performance of Evolutionary Algorithm in Searching Robust Optimal Solutions Based on Quasi-Monte Carlo Method[J]. , 2011, 24(2): 201-209.
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