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Towards a repulsive and adaptive particle swarm optimization algorithm

Published: 06 July 2013 Publication History

Abstract

This paper proposes a Repulsive Adaptive PSO (RAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. RAPSO optimizes the velocity weights during every outer PSO iteration, and optimizes the solution of the problem in an inner PSO iteration. We compare RAPSO to Global Best PSO (GBPSO) on nine benchmark problems, and the results show that RAPSO out-performs GBPSO on difficult optimization problems.

References

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T. Yamaguchi and K. Yasuda. Adaptive particle swarm optimization; self-coordinating mechanism with updating information. IEEE International Conference on Systems, Man and Cybernetics, 2006. SMC '06, 3:2303-2308, 2006.
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T. Yamaguchi, N. Iwasaki, and K. Yasuda. Adaptive particle swarm optimization using information about global best. IEEE Transactions on Electronics, Information and Systems, 126:270-276, 2006.
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A. Ide and K. Yasuda. A basic study of adaptive particle swarm optimization. Denki Gakkai Ronbunshi / Electrical Engineering in Japan, 151(3):41-49, 2005.
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  1. Towards a repulsive and adaptive particle swarm optimization algorithm

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    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 06 July 2013

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    Author Tags

    1. adaptive particle swarm optimization
    2. benchmark problems
    3. evolutionary computation

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    GECCO '13
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    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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