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Particle Swarm Optimization

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Advances in Metaheuristic Algorithms for Optimal Design of Structures

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 inter-particle communications. These mathematical expressions, in their simplest and most basic form, suggest the movement of each particle towards 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.

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Authors and Affiliations

  1. School of Civil Engineering, Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Tehran, Iran

    A. Kaveh

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Kaveh, A. (2014). Particle Swarm Optimization. In: Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, Cham. https://doi.org/10.1007/978-3-319-05549-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-05549-7_2

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  • Print ISBN: 978-3-319-05548-0

  • Online ISBN: 978-3-319-05549-7

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