Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm

Authors

Xuewen Xia, 1. Intelligence optimization & Information Process Lab., East China Jiaotong University, School of Software, Nanchang 330013, China;;
Bojian Wang, 1. Intelligence optimization & Information Process Lab., East China Jiaotong University, School of Software, Nanchang 330013, China;;
Jin Chang, 1. Intelligence optimization & Information Process Lab., East China Jiaotong University, School of Software, Nanchang 330013, China;;
Guoliang He, 2. School of Computer, Wuhan University, Wuhan 430079, China;
Chengwang Xie, 1. Intelligence optimization & Information Process Lab., East China Jiaotong University, School of Software, Nanchang 330013, China;;
Wei Bo, 1. Intelligence optimization & Information Process Lab., East China Jiaotong University, School of Software, Nanchang 330013, China;;

Abstract

Abstract: To overcome the shortcomings of poor ability to escape a local optimal, premature convergence and low precision of the traditional particle swarm optimization algorithm (PSO), a self-adaptive multi-swarm particle swarm optimization (SMPSO) was proposed. In SMPSO, the whole population was divided into many parallel-evolution multi-swarms, the aim of which was to keep diversity of the population. Furthermore, a self-adaptive regrouping operator was proposed to reinforce the information sharing and interaction between different swarms. In addition, particles’ historical information were periodic sampling and the statistics results were used to direct the best solution to carry out a detecting operator. The aim of the strategy was to improve PSO’s global searching ability and to help the population escape a local optimal solution. To accelerate convergence speed and improve solutions’ accuracy of PSO, two local search strategies were proposed. The comparisons of SMPSO with other five PSO algorithms on some benchmark functions and an engineering application indicate that the proposed strategies can effectively enhance the ability of escaping local optimal solution, and speed up the convergence and raised solutions’ accuracy.

Recommended Citation

Xia, Xuewen; Wang, Bojian; Chang, Jin; He, Guoliang; Xie, Chengwang; and Bo, Wei (2020) "Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm," Journal of System Simulation: Vol. 28: Iss. 12, Article 2.
DOI: 10.16182/j.issn1004731x.joss.201612002
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss12/2

First Page

2887

Revised Date

2016-03-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201612002

Last Page

2896

CLC

TP301

Recommended Citation

Xia Xuewen, Wang Bojian, Jin Chang, He Guoliang, Xie Chengwang, Wei Bo. Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm[J]. Journal of System Simulation, 2016, 28(12): 2887-2896.

DOI

10.16182/j.issn1004731x.joss.201612002