Abstract
Among the variants of the basic Particle Swarm Optimization (PSO) algorithm as first proposed in 1995, EPSO (Evolutionary PSO), proposed by Miranda and Fonseca, seems to produce significant improvements. We analyze the effects of two modifications introduced in that work (adaptive parameter setting and selection based on an evolution strategies-like approach) separately, reporting results obtained on a set of multimodal benchmark functions, which show that they may have opposite and complementary effects. In particular, using only parameter adaptation when optimizing 'harder' functions yields better results than when both modifications are applied. We also propose a justification for this, based on recent analyses in which particle swarm optimizers are studied as dynamical systems.
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