Authors: Zhihua Cui, Zhongzhi Shi
Addresses: The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Rd., Zhongguancun, Haidian District, Beijing, 100190, China; Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66 Waliu Rd., Wanbailin District, Taiyuan, Shanxi, 030024, China. ' The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, No.6 Kexueyuan South Road, Zhongguancun, Haidian District, Beijing, 100190, China
Abstract: Particle swarm optimisation (PSO) is a novel population-based stochastic optimisation algorithm inspired by the Reynolds| boid model. The original biological background of boid obeys three basic simple steering rules: separation, alignment and cohesion. However, to promote a simple update equation, none of these rules of boid model is employed by PSO methodology. Due to the weakness of biological background of PSO, in this paper, a new variant of PSO, boid particle swarm optimisation (BPSO), is designed in which cohesion rule and alignment rule are both employed to improve the performance. In BPSO, each particle has two motions: divergent motion and convergent motion. For divergent motion, each particle adjusts its moving direction according to the alignment direction and the cohesion direction, as well as in convergent motion, the original update equation of the standard version of PSO is used. To make a motion transition, a threshold is introduced to make the divergent motion is employed in the first period, whereas the convergent motion is used in the final stage. To testify the efficiency, several unconstrained benchmarks are used to compare. Simulation results show the proposed variant is more effective and efficient than other two variants of PSO when solving multi-modal high-dimensional numerical problems.
Keywords: boid particle swarm optimisation; BPSO; separation rules; alignment rules; cohesion rules.
International Journal of Innovative Computing and Applications, 2009 Vol.2 No.2, pp.77 - 85
Published online: 24 Feb 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article