Self-adaptive Multi-swarm Particle Swarm Optimization Algorithm

doi:10.16182/j.issn1004731x.joss.201612002

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.

Key words: particle swarm optimization, multi-swarm, self-adaptive, detecting operator, local search

CLC Number:

TP301

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.

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