Improvement of particle swarm optimization application of the mutation concept for the escape from local minima

Hanyorig Choi, Shunichi Ohmori, Kazuho Yoshimoto, Hiroaki Ohtake

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

    4 Citations (Scopus)

    Abstract

    In this paper, the improvement of Particle Swam Optimization (PSO) is proposed. PSO is one of the evolutionary computation methods to obtain the approximate optimal solution for the continuous optimization problem with multi-peak objective function. It can be applied to solve many optimization problems arising in the Supply Chain Management, such as Facility Location Problem, Inventory Portfolio Problem or Dynamical Lot Sizing Problem. One of the major weaknesses of PSO is trapped into local optima. To overcome this weakness, in this paper, the introduction of concept of mutation in Genetic Algorithm (GA) to PSO is proposed. In the computational experiment, the three benchmark problems are tested in order to validate the effectiveness of proposed method.

    Original languageEnglish
    Title of host publicationSCMIS 2010 - Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering
    Publication statusPublished - 2010
    Event2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering, SCMIS 2010 - Hong Kong
    Duration: 2010 Oct 62010 Oct 8

    Other

    Other2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering, SCMIS 2010
    CityHong Kong
    Period10/10/610/10/8

    Fingerprint

    Particle swarm optimization (PSO)
    Supply chain management
    Evolutionary algorithms
    Mutation
    Particle swarm optimization
    Genetic algorithms
    Optimization problem
    Experiments

    Keywords

    • Component
    • Genetic algorithm(ga)
    • Mutation
    • Particle swarm optimimtion(pso)
    • Stability analysis(sa)
    • Supply chain mnagement(scm)

    ASJC Scopus subject areas

    • Management Information Systems
    • Information Systems
    • Information Systems and Management

    Cite this

    Choi, H., Ohmori, S., Yoshimoto, K., & Ohtake, H. (2010). Improvement of particle swarm optimization application of the mutation concept for the escape from local minima. In SCMIS 2010 - Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering [5681798]

    Improvement of particle swarm optimization application of the mutation concept for the escape from local minima. / Choi, Hanyorig; Ohmori, Shunichi; Yoshimoto, Kazuho; Ohtake, Hiroaki.

    SCMIS 2010 - Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering. 2010. 5681798.

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

    Choi, H, Ohmori, S, Yoshimoto, K & Ohtake, H 2010, Improvement of particle swarm optimization application of the mutation concept for the escape from local minima. in SCMIS 2010 - Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering., 5681798, 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering, SCMIS 2010, Hong Kong, 10/10/6.
    Choi H, Ohmori S, Yoshimoto K, Ohtake H. Improvement of particle swarm optimization application of the mutation concept for the escape from local minima. In SCMIS 2010 - Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering. 2010. 5681798
    Choi, Hanyorig ; Ohmori, Shunichi ; Yoshimoto, Kazuho ; Ohtake, Hiroaki. / Improvement of particle swarm optimization application of the mutation concept for the escape from local minima. SCMIS 2010 - Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering. 2010.
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