Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions

  • Authors:

  • Yau-Tarng Juang;Shen-Lung Tung;Hung-Chih Chiu

  • Affiliations:

  • Department of Electrical Engineering, National Central University, Chung-Li, Taoyuan 320, Taiwan, ROC;Department of Electrical Engineering, National Central University, Chung-Li, Taoyuan 320, Taiwan, ROC and Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., Taoyuan 326, Taiwan, ROC;Department of Electrical Engineering, National Central University, Chung-Li, Taoyuan 320, Taiwan, ROC

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2011

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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-QI. 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.