Adaptive Particle Swarm Optimization on Individual Level

摘要

An adaptive particle swarm optimization (PSO) on individual level is presented. By analyzing the social model of PSO, a replacing criterion based on the diversity of fitness between current particle and the best historical experience is introduced to maintain the social attribution of swarm adaptively by taking off inactive particles. The testing of three benchmark functions indicates it improves the average performance effectively.