Particle Swarm Optimization using Chaotic Dynamical System

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In this research, the chaoticness of the perturbation-based gradient model was shown under weaker assumptions, which can be used to solve the optimization problem. In addition, new particle swarm optimization(PSO) models were proposed, in which the perturbation-based model is exploited to restart stagnated particles or to update particles' positions according to the distances between their two best solutions, and their effectiveness were shown by the numerical experiments. Moreover, sufficient conditions of chaoticness of the both models were derived by making use of the structure of the PSOs, which is more useful than the conditions for the general metaheuristics.