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

Document Type

Article

Publication Date

1-1-2019

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.

Identifier

85058071470 (Scopus)

Publication Title

IEEE Transactions on Evolutionary Computation

External Full Text Location

https://doi.org/10.1109/TEVC.2018.2885075

e-ISSN

19410026

ISSN

1089778X

First Page

718

Last Page

731

Issue

4

Volume

23

Grant

61571336

Fund Ref

National Natural Science Foundation of China

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