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Journal of Information Science and Engineering, Vol. 21 No. 4, pp. 809-818 (July 2005)

A Parallel Particle Swarm Optimization Algorithm with Communication Strategies

Jui-Fang Chang1, Shu-Chuan Chu2, John F. Roddick3 and Jeng-Shyang Pan4
 1Department of International Trade
National Kaohsiung University of Applied Sciences
Kaohsiung, 807 Taiwan
2Department of Information Management
Cheng Shiu University
Kaohsiung, 833 Taiwan
3School of Informatics and Engineering
Flinders University of South Australia
Adelaide 5001, South Australia

Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.

Keywords: particle swarm optimization (PSO), parallel particle swarm optimization (PPSO), communication strategies, Rosenbrock and Rastrigrin functions, Griewank function

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Received August 26, 2003; revised August 9, 2004; accepted September 23, 2004.
Communicated by Chin-Teng Lin.