Particle swarm optimisation with simple and efficient neighbourhood search strategies. (1st January 2011)

• Record Type: Journal Article
• Authors: Wang, Hui; Wu, Zhijian; Rahnamayan, Shahryar; Li, Changhe; Zeng, Sanyou; Jiang, Dazhi
• Is Part Of: International journal of innovative computing and applications. Volume 3:Number 2(2011)
• Page Start: 97
• Page End: 104
• Publication Date: 2011-01-01
• Subjects: particle swarm optimisation -- PSO -- neighbourhood search -- evolutionary computation -- global optimisation; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• DOI: 10.1504/IJICA.2011.039593
• Languages: English
• ISSNs: 1751-648X

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.