Diversity enhanced particle swarm optimization with neighborhood search

  • Authors:

  • Hui Wang;Hui Sun;Changhe Li;Shahryar Rahnamayan;Jeng-Shyang Pan

  • Affiliations:

  • School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China;School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China;School of Computer, China University of Geosciences, Wuhan 430072, PR China;Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON, Canada L1H 7K4;School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China and Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, PR China and Departmen ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

Particle Swarm Optimization (PSO) has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its performance, this paper proposes a hybrid PSO algorithm, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities. A comprehensive experimental study is conducted on a set of benchmark functions, including rotated multimodal and shifted high-dimensional problems. Comparison results show that DNSPSO obtains a promising performance on the majority of the test problems.