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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.