Authors: Xiaobo Zhou, Ying Tan
Addresses: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024, China. ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024, China
Abstract: Particle swarm optimisation (PSO) is a novel swarm intelligent algorithm inspired by fish schooling and birds flocking. Due to the complex nature of engineering optimisation tasks, the standard version can not always meet the optimisation requirements. Therefore, in this paper, a new group decision mechanism is introduced to PSO to enhance the escaping capability from local optimum. Furthermore, a Watts-Strogatz small-world model is incorporated into PSO to increase the population diversity. Seven famous numerical benchmarks are used to test the new algorithm. Simulation results show that it achieves the best performance when compared with three other variants of particle swarm optimisation especially for multi-modal problems.
Keywords: social parameters; particle swarm optimisation; Watts-Strogatz PSO; nonlinear manner; exponential curves; group decisions; population diversity; simulation.
International Journal of Computational Science and Engineering, 2011 Vol.6 No.1/2, pp.52 - 59
Published online: 13 Jul 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article