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Soft Computing
Erschienen in: Soft Computing 2/2018

17.01.2017 | Foundations

Particle swarm optimization algorithm: an overview

verfasst von: Dongshu Wang, Dapei Tan, Lei Liu

Erschienen in: Soft Computing | Ausgabe 2/2018

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Abstract

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.
Vorheriger Artikel Fuzzy topologies generated by fuzzy relations
Nächster Artikel Enhancing point symmetry-based distance for data clustering

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Metadaten
Titel
Particle swarm optimization algorithm: an overview
verfasst von
Dongshu Wang
Dapei Tan
Lei Liu
Publikationsdatum
17.01.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 2/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2474-6

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