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

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...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 23; číslo 4; s. 718 - 731
Hlavní autoři: Cao, Yulian, Zhang, Han, Li, Wenfeng, Zhou, Mengchu, Zhang, Yu, Chaovalitwongse, Wanpracha Art
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive strategy
Algorithms
Convergence
evolutionary algorithm
Indexes
local search (LS)
Machine learning
multimodal function
Optimization
Parameter sensitivity
Particle swarm optimization
particle swarm optimization (PSO)
Robustness (mathematics)
Searching
Sensitivity analysis
Sociology
Statistics
ISSN:1089-778X, 1941-0026
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Shrnutí: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2018.2885075