文献类型：期刊文献

英文题名：Chaos-genetic algorithm for multiobjective optimization

作者：Qi, Rongbin[1]; Qian, Feng[1]; Li, Shaojun[1]; Wang, Zhenlei[1]

机构：[1] Automation Institute, East China University of Science and Technology, Shanghai, 200237, China

年份：2006

卷号：1

起止页码：1563

外文期刊名：Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

收录：EI(收录号：20071510541469)

语种：英文

外文关键词：Chaos theory - Intelligent control - Multiobjective optimization - Optimization

摘要：Chaos-genetic algorithm (CGA) combining local chaotic search and nondominated sorting genetic algorithm for multiobjective optimization is proposed. The method is composed of two stages. The wide search with nondominated sorting genetic algorithm (NSGA-II) is performed at the first searching stage, then the local search with chaotic mutation is performed at the second stage. Moreover, we cancel the limitation of the number of the elitism at each generation and improve the original clustering method. We apply the coverage measure and spread measure to evaluate the performance of the two methods, and obtain more satisfactory results with CGA than that with NSGA-II. ? 2006 IEEE.