Abstract
The particle swarm optimization (PSO) method is one of the most powerful methods for solving unconstrained and constrained global optimization problems. Little is, however, known about an adaptive strategy for tuning the parameters of the PSO method in order to apply the PSO method to large-scale nonlinear nonconvex optimization problems. This paper deals with an adaptive strategy for tuning the parameters of the PSO method based on the analysis of the dynamics of PSO. While the relation between the dynamics of average velocity of the particles and successful search processes is analyzed, an adaptive tuning strategy for adaptive search is proposed based on the investigated relation. The feasibility and the advantage of the proposed adaptive PSO method are demonstrated through some numerical simulations using a typical global optimization test problem.
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