Abstract
The Particle Swarm Optimization method is one of the most powerful optimization methods available for solving global optimization problems. However, knowledge of autonomous and adaptive strategies for tuning the parameters of the method for application to large-scale nonlinear non-convex optimization problems is as yet limited. This paper describes an autonomous and adaptive strategy for tuning the parameters of the PSO method based on some numerical analysis of the behavior of PSO. The proposed autonomous and adaptive tuning strategy is based on self-tuning of the parameters of PSO, which strategy utilize the information about the frequency of an updated global best of a swarm. The feasibility and advantages of the proposed autonomous adaptive PSO algorithm are demonstrated through some numerical simulations using three typical global optimization test problems.
