Authors: Qinghua Wu; Hanmin Liu; Xuesong Yan
Addresses: Hubei Provincial Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430073, China ' Wuhan Institute of Ship Building Technology, Wuhan, Hubei, 430050, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, 430074, China
Abstract: In design optimisation field, there are many non-linear optimisation problems and the traditional algorithms cannot deal with these problems well. In this paper, we improve the standard particle swarm optimisation (PSO) and propose a new algorithm to solve the overcome of standard PSO algorithm like being trapped easily into a local optimum. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Compared with standard PSO on the benchmark functions, the results show that the new algorithm is efficient. We also used the new algorithm to solve design optimisation problems and the experiment results show the new algorithm is effective for these problems.
Keywords: design optimisation; particle swarm optimisation; PSO; genetic algorithms; convergence speed; global search; swarm intelligence.
International Journal of Computing Science and Mathematics, 2014 Vol.5 No.1, pp.27 - 36
Received: 04 Jun 2013
Accepted: 01 Jul 2013
Published online: 30 Jun 2014 *