计算机工程与科学2011，Vol.33Issue(9)：88-94,7.DOI:10.3969/j.issn.1007-130X.2011.09.016
多目标优化差分进化算法
Differential Evolution Algorithm for Multi-Objective Optimization
敖友云 1迟洪钦2
作者信息
- 1. 安庆师范学院计算机与信息学院,安徽安庆246011
- 2. 上海师范大学计算机系,上海200234
- 折叠
摘要
Abstract
Fitness assignment of individuals and diversity maintenance of population are two key techniques of evolutionary algorithms. First, on the one hand, this paper introduces some related concepts of Pareto e~dom-inance which can determine the strength Pareto values of the individuals of population, according to the strength Pareto values of individuals, some better individuals are selected into the offspring population by the technique of Pareto ranking; on the other hand, in order to maintain the diversity of population, a crowded-density method is introduced to remove some individuals that are located in the crowed regions. Then, according to some characteristics of differential evolution (DE), through using the appropriate DE strategies and control parameters, this paper proposes a differential evolution algorithm for multi-objective optimization, which is called DEAMO. Finally, numerical experiments show that DEAMO can perform well when tested on several benchmark multi-objective optimization problems.关键词
多目标优化/差分进化/进化算法Key words
multi-objective optimization differential evolution/ evolutionary algorithm分类
信息技术与安全科学引用本文复制引用
敖友云,迟洪钦..多目标优化差分进化算法[J].计算机工程与科学,2011,33(9):88-94,7.
