Parameter self-adjusting strategy for Particle Swarm Optimization

A new self-adjusting strategy for tuning parameters of Particle Swarm Optimization (PSO), which adaptive strategy is based on some numerical analysis of the behavior of PSO, is developed in this paper. The developed self-adjusting strategy for tuning parameters, a self-adjusting strategy of parameters of PSO, utilizes the information about the frequency of an updated group best of a swarm. The feasibility and advantages of the developed self-adjusting PSO (SAPSO) algorithm are demonstrated through some numerical simulations using four typical global optimization test problems.