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Generalized Self-adapting Particle Swarm Optimization Algorithm

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Parallel Problem Solving from Nature – PPSN XV (PPSN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11101))

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Abstract

This paper presents a generalized view on the family of swarm optimization algorithms. Paper focuses on a few distinct variants of the Particle Swarm Optimization and also incorporates one type of Differential Evolution algorithm as a particle’s behavior. Each particle type is treated as an agent enclosed in a framework imposed by a basic PSO. Those agents vary on the velocity update procedure and utilized neighborhood. This way, a hybrid swarm optimization algorithm, consisting of a heterogeneous set of particles, is formed. That set of various optimization agents is governed by an adaptation scheme, which is based on the roulette selection used in evolutionary approaches. The proposed Generalized Self-Adapting Particle Swarm Optimization algorithm performance is assessed a well-established BBOB benchmark set and proves to be better than any of the algorithms its incorporating.

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Notes

  1. 1.

    https://bitbucket.org/pl-edu-pw-mini-optimization/corpoalgorithm.

  2. 2.

    http://coco.gforge.inria.fr/.

  3. 3.

    Detailed outcomes are available at http://pages.mini.pw.edu.pl/~zychowskia/gapso.

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Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

    Mateusz Uliński, Adam Żychowski, Michał Okulewicz, Mateusz Zaborski & Hubert Kordulewski

Authors
  1. Mateusz Uliński
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  2. Adam Żychowski
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  3. Michał Okulewicz
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  4. Mateusz Zaborski
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  5. Hubert Kordulewski
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Corresponding author

Correspondence to Michał Okulewicz .

Editor information

Editors and Affiliations

  1. Inria Saclay, Palaiseau, France

    Anne Auger

  2. University of Coimbra, Coimbra, Portugal

    Carlos M. Fonseca

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  5. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  6. Colorado State University, Fort Collins, Colorado, USA

    Darrell Whitley

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Uliński, M., Żychowski, A., Okulewicz, M., Zaborski, M., Kordulewski, H. (2018). Generalized Self-adapting Particle Swarm Optimization Algorithm. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-99253-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99252-5

  • Online ISBN: 978-3-319-99253-2

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