Particle Swarm Optimization with Directed Mutation  

作　　者：王杰 李红文 

机构地区：[1]School of Electrical Engineering,Zhengzhou University [2]College of Electrical Engineering,Zhengzhou University

出　　处：《Journal of Donghua University(English Edition)》2016年第5期774-780,共7页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.60905039)

摘　　要：In the standard particle swarm optimization(SPSO),the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate premature convergence problem,this paper presents a new algorithm,which is called particle swarm optimization(PSO) with directed mutation,or DMPSO.The main idea of this algorithm is to "let the best particle(the smallest fitness of the particle swarm) become more excellent and the worst particle(the largest fitness of the particle swarm) try to be excellent".The new algorithm is tested on a set of eight benchmark functions,and compared with those of other four PSO variants.The experimental results illustrate the effectiveness and efficiency of the DMPSO.The comparisons show that DMPSO significantly improves the performance of PSO and searching accuracy.In the standard particle swarm optimization（SPSO）,the big problem is that it suffers from premature convergence,that is,in complex optimization problems,it may easily get trapped in local optima.In order to mitigate premature convergence problem,this paper presents a new algorithm,which is called particle swarm optimization（PSO） with directed mutation,or DMPSO.The main idea of this algorithm is to ＂let the best particle（the smallest fitness of the particle swarm） become more excellent and the worst particle（the largest fitness of the particle swarm） try to be excellent＂.The new algorithm is tested on a set of eight benchmark functions,and compared with those of other four PSO variants.The experimental results illustrate the effectiveness and efficiency of the DMPSO.The comparisons show that DMPSO significantly improves the performance of PSO and searching accuracy.

关 键 词：swarm fitness benchmark illustrate premature mutation searching Mutation iteration trapped 

分 类 号：TP202.7[自动化与计算机技术—检测技术与自动化装置]