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2017 | OriginalPaper | Chapter

2. Particle Swarm Optimization

Author : A. Kaveh

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

Publisher: 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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Metadata
Title
Particle Swarm Optimization
Author
A. Kaveh
Copyright Year
2017
Book
Advances in Metaheuristic Algorithms for Optimal Design of Structures

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

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

Copyright Year: 2017

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

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