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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

2. Particle Swarm Optimization

verfasst von : A. Kaveh

Erschienen in: Advances in Metaheuristic Algorithms for Optimal Design of Structures

Verlag: Springer International Publishing

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Abstract

Particle swarm optimization (PSO) algorithms are nature-inspired population-based metaheuristic algorithms originally accredited to Eberhart, Kennedy, and Shi [1, 2]. The algorithms mimic the social behavior of birds flocking and fishes schooling. Starting form a randomly distributed set of particles (potential solutions), the algorithms try to improve the solutions according to a quality measure (fitness function). The improvisation is preformed through moving the particles around the search space by means of a set of simple mathematical expressions which model some interparticle communications. These mathematical expressions, in their simplest and most basic form, suggest the movement of each particle toward its own best experienced position and the swarm’s best position so far, along with some random perturbations. There is an abundance of different variants using different updating rules, however.

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Vorheriges Kapitel Introduction
Nächstes Kapitel Charged System Search Algorithm
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Metadaten
Titel
Particle Swarm Optimization
verfasst von
A. Kaveh
Copyright-Jahr
2017
Buch
Advances in Metaheuristic Algorithms for Optimal Design of Structures

Print ISBN: 978-3-319-46172-4

Electronic ISBN: 978-3-319-46173-1

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-46173-1_2

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