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Comparative Study of Particle Swarm Optimization Variants in Complex Mathematics Functions

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Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 574))

Abstract

Particle Swarm Optimization (PSO) is one of the evolutionary computation techniques based on the social behaviors of birds flocking or fish schooling, biologically inspired computational search and optimization method. Since first introduced by Kennedy and Eberhart (A new optimizer using particle swarm theory 39–43, 1995 [1]) in 1995, several variants of the original PSO have been developed to improve speed of convergence, improve the quality of solutions found, avoid getting trapped in the local optima and so on. This paper is focused on performing a comparison of different approaches of inertia weight such as constant, random adjustments, linear decreasing, nonlinear decreasing and fuzzy particle swarm optimization; we are using a set of 4 mathematical functions to validate our approach. These functions are widely used in this field of study.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Juan Carlos Vazquez, Fevrier Valdez & Patricia Melin

Authors
  1. Juan Carlos Vazquez
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  2. Fevrier Valdez
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  3. Patricia Melin
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Corresponding author

Correspondence to Fevrier Valdez .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

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Vazquez, J.C., Valdez, F., Melin, P. (2015). Comparative Study of Particle Swarm Optimization Variants in Complex Mathematics Functions. In: Castillo, O., Melin, P. (eds) Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics. Studies in Computational Intelligence, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-10960-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-10960-2_11

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  • Online ISBN: 978-3-319-10960-2

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