An improved diversity-guided particle swarm optimisation for numerical optimisation
by Wenjun Wang; Hui Wang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 5, No. 1, 2014

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.

Online publication date: Mon, 30-Jun-2014

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