Improvement of particle swarm optimization: Proposal of R-best model and parameter adjustment with consideration to searching phase and state

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, the improvement of Particle Swam Optimization (PSO) is proposed. PSO is algorithm created by mimicking the food-seeking behavior of swarm of organisms, such as birds and fish. In recent years, PSO is drawing much attention as one of the evolutionary computation methods to obtain the approximate optimal solution for the continuous optimization problem with multi-peak objective function. But, one of the major weaknesses of PSO is trapped into local optima. To overcome this weakness, this paper proposes the new strategy of information sharing called Rbest model and the parameter adjustment with consideration to searching phase and state (Application of the mutation concept). Then, in the computational experiment, the benchmark problems are tested in order to validate the effectiveness of proposed method (2n-minima: 61% improvement from the original PSO, Rastrigin: 8% improvement from the AFPSO) [14] [15].

    Original languageEnglish
    Title of host publication21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings
    PublisherFraunhofer-Verlag
    ISBN (Print)9783839602935
    Publication statusPublished - 2011
    Event21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Stuttgart
    Duration: 2011 Jul 312011 Aug 4

    Other

    Other21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011
    CityStuttgart
    Period11/7/3111/8/4

    Fingerprint

    Particle swarm optimization (PSO)
    Birds
    Evolutionary algorithms
    Fish
    Experiments

    Keywords

    • Improve PSO
    • Optimization
    • Particle Swam Optimization (PSO)
    • Rbest model

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Computer Science Applications
    • Industrial and Manufacturing Engineering

    Cite this

    Choi, H., Ohmori, S., & Yoshimoto, K. (2011). Improvement of particle swarm optimization: Proposal of R-best model and parameter adjustment with consideration to searching phase and state. In 21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings Fraunhofer-Verlag.

    Improvement of particle swarm optimization : Proposal of R-best model and parameter adjustment with consideration to searching phase and state. / Choi, Hanyong; Ohmori, Shunichi; Yoshimoto, Kazuho.

    21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings. Fraunhofer-Verlag, 2011.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Choi, H, Ohmori, S & Yoshimoto, K 2011, Improvement of particle swarm optimization: Proposal of R-best model and parameter adjustment with consideration to searching phase and state. in 21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings. Fraunhofer-Verlag, 21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011, Stuttgart, 11/7/31.
    Choi H, Ohmori S, Yoshimoto K. Improvement of particle swarm optimization: Proposal of R-best model and parameter adjustment with consideration to searching phase and state. In 21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings. Fraunhofer-Verlag. 2011
    Choi, Hanyong ; Ohmori, Shunichi ; Yoshimoto, Kazuho. / Improvement of particle swarm optimization : Proposal of R-best model and parameter adjustment with consideration to searching phase and state. 21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings. Fraunhofer-Verlag, 2011.
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