Abstract
This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm using the information defined as the average absolute value of velocity of all of the particles, which information can be used as an index to understand the activity of all of the particles. While a stability analysis of PSO algorithm is carried out based on the stability theory of modern control theory, an adaptive strategy for tuning one of its parameters is introduced so as to follow a given ideal average velocity by feedback control. The feasibility and advantages of the proposed adaptive PSO algorithm are verified through numerical simulations using some typical global optimization problems.
