Abstract An organizational evolutionary particle swarm optimization (OEPSO) is presented. The evolutional operations are acted on organizations directly in the algorithm. The global convergence is gained through competition and cooperation among the organizations, and the mathematic convergence is given. In the experiments, OEPSO is tested on 12 unconstrained benchmark problems, and compared with FEP and three algorithms based on the PSO. In addition, the effects of parameters in the algorithm are analyzed. The results indicate that OEPSO performs better than other algorithms both in solution quality and computational complexity. The analyses of parameters show OEPSO has stable performance and high success ratio, and it is insensitive to parameters.
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2007, Vol. 20 
