Adaptative Clustering Particle Swarm Optimization

The performance of Particle Swarm Optimization (PSO) algorithms depends strongly upon the interaction among the particles. The existing communication topologies for PSO (e.g. star, ring, wheel, pyramid, von Neumann, clan, four clusters) can be viewed as distinct means to coordinate the information flow within the swarm. Overall, each particle exerts some influence among others placed in its immediate neighborhood or even in different neighborhoods, depending on the communication schema (rules) used. The neighborhood of particles within PSO topologies is determined by the particles' indexes that usually reflect a spatial arrangement. In this paper, in addition to position information of particles, we investigate the use of adaptive density-based clustering algorithm - ADACLUS - to create neighborhoods (i.e. clusters) that are formed considering velocity information of particles. Additionally, we suggest that the new clustering rationale be used in conjunction with Clan-PSO main ideas. The proposed approach was tested in a wide range of well known benchmark functions. The experimental results obtained indicate that this new approach can improve the global search ability of the PSO technique.