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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/52028


    Title: Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions
    Authors: Juang,YT;Tung,SL;Chiu,HC
    Contributors: 電機工程學系
    Date: 2011
    Issue Date: 2012-03-28 10:13:39 (UTC+8)
    Publisher: 國立中央大學
    Abstract: This paper proposes an adaptive fuzzy PSO (AFPSO) algorithm, based on the standard particle swarm optimization (SPSO) algorithm. The proposed AFPSO utilizes fuzzy set theory to adjust PSO acceleration coefficients adaptively, and is thereby able to improve the accuracy and efficiency of searches. Incorporating this algorithm with quadratic interpolation and crossover operator further enhances the global searching capability to form a new variant, called AFPSO-Q1. We compared the proposed AFPSO and its variant AFPSO-QI with SPSO, quadratic interpolation PSO (QIPSO), unified PSO (UPSO), fully informed particle swarm (FIPS), dynamic multi-swarm PSO (DMSPSO), and comprehensive learning PSO (CLPSO) across sixteen benchmark functions. The proposed algorithms performed well when applied to minimization problems for most of the multimodal functions considered. (C) 2010 Elsevier Inc. All rights reserved.
    Relation: INFORMATION SCIENCES
    Appears in Collections:[電機工程學系] 期刊論文

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