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Multi-chaotic Approach for Particle Acceleration in PSO

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Hybrid Metaheuristics (HM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9668))

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Abstract

This paper deals with novel approach for hybridization of two scientific techniques: the evolutionary computational techniques and deterministic chaos. The Particle Swarm Optimization algorithm is enhanced with two pseudo-random number generators based on chaotic systems. The chaotic pseudo-random number generators (CPRNGs) are used to guide the particles movement through multiplying the accelerating constants. Different CPRNGs are used simultaneously in order to improve the performance of the algorithm. The IEEE CEC’13 benchmark suite is used to test the performance of the proposed method.

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Acknowledgements

This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Project no. IGA/Ceb-iaTech/2016/007.

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Michal Pluhacek, Roman Senkerik & Adam Viktorin

  2. Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. Listopadu 15, 708 33, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

Authors
  1. Michal Pluhacek
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  2. Roman Senkerik
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  3. Adam Viktorin
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  4. Ivan Zelinka
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Corresponding author

Correspondence to Michal Pluhacek .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  2. University of the Basque Country, San Sebastian, Spain

    Christian Blum

  3. Universiy of Plymouth, Plymouth, United Kingdom

    Angelo Cangelosi

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

  5. Computing, Sheffield Hallam University, SHEFFIELD, United Kingdom

    ALESSANDRO DI NUOVO

  6. University of Catania, Catania, Italy

    Mario Pavone

  7. University of Lille 1, Villeneuve d'Ascq, France

    El-Ghazali Talbi

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Pluhacek, M., Senkerik, R., Viktorin, A., Zelinka, I. (2016). Multi-chaotic Approach for Particle Acceleration in PSO. In: Blesa, M., et al. Hybrid Metaheuristics. HM 2016. Lecture Notes in Computer Science(), vol 9668. Springer, Cham. https://doi.org/10.1007/978-3-319-39636-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-39636-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39635-4

  • Online ISBN: 978-3-319-39636-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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