Particle Swarm Optimization with Parameter Self-Adjusting Mechanism

This paper presents a self-adjusting strategy for tuning the parameters of particle swarm optimization (PSO) based on some numerical analysis of the behavior of PSO. The proposed adaptive tuning strategy is based on self-tuning of the parameters of PSO, a strategy that utilizes the information about the frequency of an updated group best of a swarm. The feasibility and advantages of the proposed self-adjusting PSO (SAPSO) algorithm are demonstrated through some numerical simulations using four benchmark problems. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.