Authors: Wenjun Wang; Hui Wang
Addresses: School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
Abstract: Particle swarm optimisation (PSO) is a global optimisation technique, which has shown a good performance on many problems. However, PSO easily falls into local minima because of quick losing of diversity. Some diversity-guided PSO algorithms have been proposed to maintain diversity, but they often slow down the convergence rate. In this paper, we propose an improved diversity-guided PSO algorithm, namely IDPSO, which employs a local search to enhance the exploitation. In addition, a concept of generalised opposition-based learning (GOBL) is utilised for population initialisation and generation jumping to find high quality of candidate solutions. Experiments are conducted on a set of benchmark functions. Results show that our approach obtains a promising performance when compared with other PSO variants.
Keywords: particle swarm optimisation; PSO; diversity; local search; generalised opposition-based learning; GOBL; numerical optimisation.
International Journal of Computing Science and Mathematics, 2014 Vol.5 No.1, pp.16 - 26
Received: 16 May 2013
Accepted: 27 Jun 2013
Published online: 30 Jun 2014 *