文献类型：期刊文献

中文题名：分阶段二次变异的多目标混沌差分进化算法

英文题名：Multi-objective chaotic differential evolution algorithm with grading second mutation

作者：王筱珍[1];李鹏[2];俞国燕[2]

机构：[1]广东海洋大学信息学院,广东湛江524088;[2]广东海洋大学工程学院,广东湛江524088

年份：2011

卷号：26

期号：3

起止页码：457

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD、、EI(收录号：20111913966158)、Scopus(收录号：2-s2.0-79955663312)、CSCD2011_2012、北大核心2008、北大核心、CSCD

基金：国家自然科学基金项目(50675069);广东省海洋渔业局项目(A200899G02).

语种：中文

中文关键词：差分进化;混沌;分阶段二次变异;非支配解

外文关键词：differential evolution; chaotic; grading second mutation; non-dominance solution

中文摘要：提出一种结合分阶段二次变异和混沌理论的改进差分进化(DE)算法,以解决多目标约束优化问题.其核心思想是,在DE进化前期采用基于非支配解的随机二次变异来提高算法的全局寻优能力,进化后期采用基于非支配解的混沌二次变异来提高DE的局部寻优能力.通过对典型测试问题的仿真实验验证了所提出的算法能在全局搜索性能与局部搜索性能之间维持较好平衡,而且保持了DE算法的简洁性能,其收敛性、分布度和均衡性均优于标准DE.

外文摘要：To solve the multi-objective constraint optimization problem, this paper proposes an advanced differential evolution（DE）. In the proposed algorithm, grading second mutation and chaotic theory are combined into standard DE. At early evolution process of DE, random second mutation based on non-dominance Pareto solution is adopted in order to improve global exploring ability. And in the later evolution process, the chaotic second mutation based on non-dominance Pareto solution is added into DE evolution operation in order to enhance local searching ability of algorithm. By testing benchmarks functions, simulation results show that, this algorithm has better convergence and distribution property, and is superior to standard DE in keeping balance between diversity and convergence.