Comprehensive Learning Particle Swarm Optimization Algorithm with Local Search for Multimodal Functions

Yulian Cao, Han Zhang, Wenfeng Li, Mengchu Zhou, Yu Zhang, Wanpracha Art Chaovalitwongse

Research output: Contribution to journalArticlepeer-review

106 Scopus citations

Abstract

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO's strong global search capability and LS's fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants.

Original languageEnglish (US)
Article number8561256
Pages (from-to)718-731
Number of pages14
JournalIEEE Transactions on Evolutionary Computation
Volume23
Issue number4
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics

Keywords

  • Adaptive strategy
  • evolutionary algorithm
  • local search (LS)
  • multimodal function
  • particle swarm optimization (PSO)

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