Comparison of Self-Adaptive Particle Swarm Optimizers

Particle swarm optimization (PSO) algorithms have a number of parameters to which their behaviour is sensitive. In order to avoid problem-specific parameter tuning, a number of self-adaptive PSO algorithms have been proposed over the past few years. This paper compares the behaviour and performance of a selection of self-adaptive PSO algorithms to that of time-variant algorithms on a suite of 22 boundary constrained benchmark functions of varying complexities. It was found that only two of the nine selected self-adaptive PSO algorithms performed comparably to similar time-variant PSO algorithms. Possible reasons for the poor behaviour of the other algorithms as well as an analysis of the more successful algorithms is performed in this paper.