Particle swarm optimization incorporating simplex search and center particle for global optimization

Chen Chien Hsu*, Chun Hwui Gao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper proposes a hybrid approach incorporating an enhanced Nelder-Mead simplex search scheme into a particle swarm optimization (PSO) with the use of a center particle in a swarm for effectively solving multi-dimensional optimization problems. Because of the strength of PSO in performing exploration search and NM simplex search in exploitation search, in addition to the help ofa center particle residing closest to the optimum during the optimization process, both convergence rate and accuracy of the proposed optimization algorithm can be significantly improved. To show the effectiveness of the proposed approach, 18 benchmark functions will be adopted for optimization viathe proposed approach in comparison to existing methods.

Original languageEnglish
Title of host publicationSMCia/08 - Proceedings of the 2008 IEEE Conference on Soft Computing on Industrial Applications
Pages26-31
Number of pages6
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE Conference on Soft Computing on Industrial Applications, SMCia/08 - Muroran, Japan
Duration: 2008 Jun 252008 Jun 27

Publication series

NameSMCia/08 - Proceedings of the 2008 IEEE Conference on Soft Computing on Industrial Applications

Other

Other2008 IEEE Conference on Soft Computing on Industrial Applications, SMCia/08
Country/TerritoryJapan
CityMuroran
Period2008/06/252008/06/27

Keywords

  • Evolutionary algorithm
  • Hybrid optimization
  • NM simplex search
  • Optimization
  • Particle swarm optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software
  • Industrial and Manufacturing Engineering

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