title: Particle swarm optimization with composite particles in dynamic environments.
abstract: 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-the-art PSO algorithms for dynamic optimization problems.
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang 110004, China. liulili@ise.neu.edu.cn
authors.forename: Lili
authors.initials: L
authors.lastname: Liu
authors.forename: Shengxiang
authors.initials: S
authors.lastname: Yang
authors.forename: Dingwei
authors.initials: D
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001522:Behavior, Animal; D003198:Computer Simulation; D003441:Crowding; D017753:Ecosystem; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*