Particle Swarm Optimization

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

Particle Swarm Optimization (PSO) [Kennedy, 2010, Kennedy and Eberhart, 1995] is an optimization algorithm designed for continuous optimization. Like GAs, it is a population-based stochastic method, but unlike GAs it does not take its inspiration from the Theory of Evolution of Darwin, but from the social behavior of bird flocking or fish schooling [Reynolds, 1987]. For instance, one may imagine a flock of birds flying over an area, to find a point to land. In such a situation, defining where the whole swarm should land is a complex problem, since it depends on many pieces of information, such as, for instance, maximizing the availability of food or minimizing the risk of existence of predators.

Original languageEnglish
Title of host publicationLectures on Intelligent Systems
Place of PublicationCham, Switzerland
PublisherSpringer, Cham
Pages105-111
Number of pages7
ISBN (Electronic)978-3-031-17922-8
ISBN (Print)978-3-031-17921-1, 978-3-031-17924-2
DOIs
Publication statusPublished - 13 Jan 2023

Publication series

NameNatural Computing Series
ISSN (Print)1619-7127

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