模式识别与人工智能
Wednesday, Apr. 2, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2011, Vol. 24 Issue (2): 201-214    DOI:
Articles Current Issue| Next Issue| Archive| Adv Search |
Research on Increasing the Performance of Evolutionary Algorithm in Searching Robust Optimal Solutions Based on Quasi Monte Carlo Method

Download: PDF (493 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
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.
Key wordsEvolutionary Algorithm, Robust Optimal Solutions      QuasiMonte Carlo Method      Effective Objective Function      Monte Carlo Integral      Performance     
ZTFLH: TP 18  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
ZHU Yun-Fei
LUO Biao
ZHENG Jin-Hua
CAI Zi-Xing
Cite this article:   
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): 201-214.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2011/V24/I2/201
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn