Enhancing particle swarm optimization using generalized opposition-based learning

  • Authors:

  • Hui Wang;Zhijian Wu;Shahryar Rahnamayan;Yong Liu;Mario Ventresca

  • Affiliations:

  • State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, PR China and School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China;State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, PR China;Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Canada ON L1H 7K4;University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu, Fukushima 965-8580, Japan;Centre for Pathogen Evolution, Department of Zoology, University of Cambridge, Downing Street, Cambridge, UK

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

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Abstract

Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems.