Particle swarm optimisation with simple and efficient neighbourhood search strategies

  • Authors:

  • Hui Wang;Zhijian Wu;Shahryar Rahnamayan;Changhe Li;Sanyou Zeng;Dazhi Jiang

  • Affiliations:

  • State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China.;State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China.;Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada.;Department of Computer Science, University of Leicester, Leicester, LE1 7RH, UK.;School of Computer Sciences, China University of Geosciences, Wuhan 430074, China.;Department of Computer Science, Shantou University, Shantou 515063, China

  • Venue:
  • International Journal of Innovative Computing and Applications
  • Year:
  • 2011

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Abstract

This paper presents a novel particle swarm optimiser (PSO) called PSO with simple and efficient neighbourhood search strategies (NSPSO), which employs one local and two global neighbourhood search strategies. By this way, one strong and two weak locality perturbation operators are embedded in the standard PSO. The NSPSO consists of two main steps. First, for each particle, three trail particles are generated by the mentioned three neighbourhood search strategies, respectively. Then, the best one among the three trail particles is selected to compete with the current particle, and the fitter one is accepted as a current particle. In order to verify the performance of NSPSO, it experimentally has been tested on 12 unimodal and multimodal benchmark functions. The results show that NPSO significantly outperforms other seven PSO variants.