Abstract The method of population generation in Gaussian dynamic particle swarm optimization algorithm (GDPSO) is analyzed detailedly. Aiming at the problem of premature convergence of Gbest version and the slow search speed of Lbest version in original particle swarm optimization, a novel neighborhood topology structure called multi-cluster structure is proposed. In the proposed population structure, particles in one cluster share the information with each other, and clusters exchange their experiences through loose connection between particles. Thus, neighborhood topology is designed to coordinate exploration and exploitation. GDPSO, with several population topologies including the multi-cluster structure, is tested on four benchmark functions which are commonly used in the evolutionary computation. Experimental results show that the GDPSO with the proposed neighborhood topology can significantly speed up the convergence and efficiently improve the global search ability.
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2008, Vol. 21 
