Differential Evolution with Cloud Model Based Self-adaptive Crossover Strategy
Efficiency of the evolutionary algorithms (EAs) is strongly dependent on the parameter setting. Differential evolution (DE) is well known as a simple and efficient evolutionary algorithm over continuous spaces. Of the three main parameters which control DEs behavior-population size NP, scaling factor F and crossover rate CR-CR is the most important. Despite the important role CR plays in effecting the performance of DE there are few studies that have examined the adaptive or self-adaptive strategies of CR. This paper is aim to investigate the influence of CR on the behavior of DE both from a theoretical and numerical point of view. In addition, a novel selfadaptive scheme for the evolution of CR based on a cloud model is proposed to enhance the convergence performance of DE. Experimental results confirm the superiority of the new scheme over several state-ofthe-art self-adaptive DE versions.
Differential evolution cloud model self-adaption strategy
Xiaojun Bi Guoan Liu Jing Xiao
the College of Information and Communication Engineering Harbin Engineering University Harbin, China the Information Engineering Department Liaoning Provincial College of Communications,Shenyang, China
国际会议
三峡
英文
675-679
2012-05-18（万方平台首次上网日期，不代表论文的发表时间）