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Identifying Species for Particle Swarm Optimization under Dynamic Environments

The species techniques have been widely used in multimodal optimization, but not paid enough attention in dynamic optimization. In this paper, a novel Nearest-Better Clustering (NBC) method, called psfNBC, is proposed, which is used to identify the species for Particle Swarmoptimization (PSO) under dynamic environments. The proposed psfNBC includes three novel properties. First, we propose the strategy p to cope with the effects of outliers. Second, the strategy s is introduced to deal with the “long-tail phenomenon Third, a random scale factor, which is named as the strategy f, is adopted. In order to evaluate the performance of the psfNBC, a framework of species-based particle swarm optimization for Dynamic optimization Problems (DOPs) is given. Within this framework, the proposed psfNBC is compared with the basic NBC as well as different combinations of the proposed strategies. The experimental results on the moving peak benchmark problems show that psfNBC has better performance.