Composite particle swarm optimization with nonlinear effect in dynamic environment
Dingwei Wang · Control theory & applications · 2012
This paper presents a new particle swarm optimization model,called composite particle swarm optimization with nonlinear effect(CPSO–NE),to deal with dynamic optimization problems.CPSO–NE partitions the swarm into a set of composite particles based on their similarity using a worst-first principle.Inspired by the notion of 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 CPSO–NE is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.