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Comparison of Selected Fuzzy PSO Algorithms

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 717))

Abstract

This paper presents a comparison of selected fuzzy particle swarm optimization algorithms. Two non-fuzzy and four fuzzy algorithms are considered. The Takagi–Sugeno fuzzy system is used to change the parameters of these algorithms. A modified fuzzy particle swarm optimization method is proposed in which each of the particles has its own inertia weight and coefficients of the cognitive and social components. The evaluation is based on the common nonlinear benchmark functions frequently used for testing optimization methods. The ratings of the algorithms are assigned on the basis of the mean of the objective function final value.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland

    Tomasz Krzeszowski & Krzysztof Wiktorowicz

  2. Faculty of Physical Education, University of Rzeszow, ul. Towarnickiego 3, 35-959, Rzeszów, Poland

    Krzysztof Przednowek

Authors
  1. Tomasz Krzeszowski
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  2. Krzysztof Wiktorowicz
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  3. Krzysztof Przednowek
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Correspondence to Tomasz Krzeszowski .

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Editors and Affiliations

  1. Department of Parallel Algorithms, Institute of Information and Communication Technology, Bulgarian Academy of Sciences , Sofia, Bulgaria

    Stefka Fidanova

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Krzeszowski, T., Wiktorowicz, K., Przednowek, K. (2018). Comparison of Selected Fuzzy PSO Algorithms. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-319-59861-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-59861-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59860-4

  • Online ISBN: 978-3-319-59861-1

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