An improved particle swarm optimization with re-initialization mechanism

Jie Guo*, Sheng Jing Tang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

An improved Particle Swarm Optimization with reinitialization mechanism, which is based on the estimation of the varieties and activities of the particles, is proposed to balance the global search ability of the Standard Swarm Optimization (SPSO). Firstly the motion behavior of single particle is discussed, including the motion mode, convergence and the relationship between motion characteristic and the performance of SPSO. Then, a new variable named "steplength" is employed to represent the variety and activity of the particle population. The group of particles which satisfied the re-initialization conditions will be reinitialized in probability so that the variety and activity of the particle population can be hold in a reasonable level. Experiment results indicate that the improved Particle Swarm Optimization proposed in this paper has better performance compared with the other three PSO algorithms.

Original languageEnglish
Title of host publication2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009
Pages437-441
Number of pages5
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009 - Hangzhou, Zhejiang, China
Duration: 26 Aug 200927 Aug 2009

Publication series

Name2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009
Volume1

Conference

Conference2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009
Country/TerritoryChina
CityHangzhou, Zhejiang
Period26/08/0927/08/09

Keywords

  • Motion characteristic
  • Particle swarm optimization
  • Re-initialization mechanism

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