Title: An improved particle swarm optimisation based on cellular automata
Authors: Yuntao Dai; Liqiang Liu; Ying Li; Jingyi Song
Addresses: Department of Science, Harbin Engineering University, 145 NanTong Street, Harbin, Heilongjiang, China ' Department of Automation, Harbin Engineering University, 145 NanTong Street, Harbin, Heilongjiang, China ' Department of Image Processing, Anhui Sun Create Electronics Co., Ltd., No. 199, XiangZhang Road, New and High Technology Development District, Hefei City, Anhui, China ' Department of Science, Harbin Engineering University, 145 NanTong Street, Harbin, Heilongjiang, China
Abstract: Particle swarm optimisation (PSO) algorithm is easy to fall into local optimum, so an improved PSO based on cellular automata is proposed by combining cellular automata (CA) with PSO. In the proposed CAPSO, each particle of particle swarm is considered as cellular automata, and is distributed in two-dimensional grid. The state update of each cell is not only related to its own state and the neighbour state, but also related with the state of the optimal cell. If the state is too close with the optimal cell, then the cell state is re-update. Simulation experiments on typical test functions show that, compared with other algorithms, the proposed algorithm has good robustness, strong local search ability and global optimisation ability, and can solve the optimisation problems effectively.
Keywords: particle swarm optimisation; PSO; cellular automata; function optimisation; simulation.
International Journal of Computing Science and Mathematics, 2014 Vol.5 No.1, pp.94 - 106
Received: 04 May 2013
Accepted: 10 Jul 2013
Published online: 30 Jun 2014 *