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On the Use of Random Variables in Particle Swarm Optimizations: A Comparative Study of Gaussian and Uniform Distributions

The particle swarm optimization (PSO), now widely used in the electromagnetics community, is a robust evolutionary method based on the property of swarm intelligence. This paper focuses on the random variable effect in the PSO algorithm, and two random distribution functions, namely, the uniform distribution and Gaussian distribution, are studied and compared in details. It is revealed through the statistic analysis that the Gaussian distributed random variables increase the efficiency of the PSO algorithm as compared to the widely used uniformly distributed random variables. This conclusion has been demonstrated through both a mathematical benchmark function and an antenna array optimization.