Particle Swarm Optimization With Composite Particles in Dynamic Environments

In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Lili Liu, Shengxiang Yang, Dingwei Wang
• Keywords: Artificial Intelligence, Composite Particle, Dynamic Optimization Problems, Particle Swarm Optimization, TSMC 2010