Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions. (English) Zbl 1242.68288

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

Inf. Sci. 181, No. 20, 4539-4549 (2011).

Summary: 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.

MSC:

68T20	Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68T05	Learning and adaptive systems in artificial intelligence
90C59	Approximation methods and heuristics in mathematical programming

Keywords:

particle swarm optimization; fuzzy; adaptation; quadratic interpolation; multimodal

Software:

CEC 05

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