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.
朱云飞，罗彪，郑金华，蔡自兴. 基于拟蒙特卡罗方法的进化算法搜索鲁棒最优解的性能提高研究[J]. 模式识别与人工智能, 2011, 24(2): 201-209.
ZHU Yun-Fei, LUO Biao, ZHENG Jin-Hua, CAI Zi-Xing. Research on Increasing the Performance of Evolutionary Algorithm in Searching Robust Optimal Solutions Based on Quasi-Monte Carlo Method. , 2011, 24(2): 201-209.
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