Self-learning Particle Swarm Optimization Based on Environmental Feedback
Xingjuan Cai, Zhihua Cui, Jianchao Zeng, Ying Tan · 2007
Particle swarm optimization (PSO) simulates the behaviors of birds ocking and poundsh schooling. However, its biological background does not concern the environmental affection. Inspired by the interaction between environment and individuals, a new version - self-learning particle swarm optimization based on environmental feedback (SL-PSO), is proposed, in which two self-learning strategies are designed so that each particle adjusts its moving direction according to the feedback information from the environment. Furthermore, a mutation operator is introduced to avoid premature convergence phenomenon. Simulation results show the proposed algorithm is effective and efpoundcient.