A double particle swarm optimization for mixed-variable optimization problems

Chaoli Sun, Jianchao Zeng, Jengshyang Pan, Shuchuan Chu, Yunqiang Zhang

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    Abstract

    A double particle swarm optimization (DPSO), in which MPSO proposed by Sun et al. [1] is used as a global search algorithm and PSO with feasibility-based rules is used to do local searching, is proposed in this paper to solve mixed-variable optimization problems. MPSO can solve the non-continuous variables very well. However, the imprecise values of continuous variables brought the inconsistent results of each run. A particle swarm optimization with feasibility-based rules is proposed to find optimal values of continuous variables after the MPSO algorithm finishes each independent run, in order to obtain the consistent optimal results for mixed-variable optimization problems. The performance of DPSO is evaluated against two real-world mixed-variable optimization problems, and it is found to be highly competitive compared with other existing algorithms.

    Original languageEnglish
    Title of host publicationSemantic Methods for Knowledge Management and Communication
    EditorsRadoslaw Katarzyniak, Ngoc Thanh Nguyen, Tzu-Fu Chiu, Chao-Fu Hong
    Place of PublicationBerlin
    Pages93-102
    Number of pages10
    Volume381
    ISBN (Electronic)9783642234187
    DOIs
    Publication statusPublished - 7 Sep 2011

    Publication series

    NameStudies in Computational Intelligence
    Volume381
    ISSN (Print)1860-949X

    Keywords

    • feasibility-based rules
    • mixed-variable optimization problems
    • Particle swarm optimization

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