Adaptive particularly tunable fuzzy particle swarm optimization algorithm

Authors

  • A. R. Fathi Department of Mechanical Engineering, Babol Noshirvani University of Technology, Shariati Ave., Babol, Mazandaran, Iran.
  • H. R. Mohammadi Daniali Department of Mechanical Engineering, Babol Noshirvani University of Technology, Mazandaran, Iran, P.O. Box: 484
  • N. Bakhshinezhad Department of Mechanical Engineering, Babol Noshirvani University of Technology, Mazandaran, Iran, P.O. Box: 484.
  • S. A. Mir Mohammad Sadeghi Department of Mechanical Engineering, Babol Noshirvani University of Technology, Mazandaran, Iran, P.O. Box: 484.
Abstract:

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms have been being studied extensively in recent years. In this study, a modified version of PSO algorithms is presented and is named as Adaptive Particularly Tunable Fuzzy Particle Swarm Optimization (APT-FPSO). In it, the global and personal learning coefficients of every single particle are tuned adaptively and particularly, at an individual extent, within each iteration with the aid of fuzzy logic concepts. Ample statistical evidence is provided indicating that the proposed algorithm further improves the potentialities and capabilities of the standard PSO.

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Journal title

volume 17  issue 1

pages  65- 75

publication date 2020-02-01

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