An improved diversity-guided particle swarm optimisation for numerical optimisation
Wenjun Wang, Hui Wang · International Journal of Computing Science and Mathematics · 2014
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.