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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Vydané v:	IEEE transactions on evolutionary computation Ročník 23; číslo 4; s. 718 - 731
Hlavní autori:	Cao, Yulian, Zhang, Han, Li, Wenfeng, Zhou, Mengchu, Zhang, Yu, Chaovalitwongse, Wanpracha Art
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
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