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2017 | OriginalPaper | Chapter

2. Particle Swarm Optimization

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Author: A. Kaveh

Publisher: Springer International Publishing

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

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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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Title

Particle Swarm Optimization

Book

Advances in Metaheuristic Algorithms for Optimal Design of Structures

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

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

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

DOI

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

Author:

A. Kaveh

Publisher