A Particle Swarm Optimization Algorithm Based on the Pattern Search Method
For the purpose of overcoming the premature property and low execution efficiency of the Particle Swarm Optimization (PSO) algorithm,this paper presents a particle swarm optimization algorithm based on the pattern search.In this algorithm,personal and global optimum particles are chosen in every iteration by a probability.Then,local optimization will be performed by the pattern search and then the original individuals will be replaced.The strong local search function of the pattern search provides an effective mechanism for the PSO algorithm to escape from the local optimum,which avoids prematurity of the algorithm.Simulation shows that this algorithm features a stronger function of global search than conventional PSO,so that the optimization process can be improved remarkably.
PSO Pattern search Swam intelligent
Junli Zhang Dawei Dai
College of Automation Science and Engineering, South China University of Technology,Guangzhou, China College of Automation Science and Engineering, South China University of Technology,Guangzhou, China
国际会议
西安
英文
1664-1669
2012-08-24（万方平台首次上网日期，不代表论文的发表时间）