The Comparative Study of Different Number of Particles in Clustering Based on Two-Layer Particle Swarm Optimization

Abstract

To study how the different number of particles in clustering affect the performance of two-layer particle swarm optimization (TLPSO) that set the global best location in each swarm of the bottom layer to be the position of the particle in the swarm of the top layer, fourteen configurations of the different number of particles are compared. Fourteen benchmark functions, being in seven types with different circumstance, are used in the experiments. The experiments show that the searching ability of the algorithms is related to the number of particles in clustering, which is better with the number of particles transforming from as little as possible to as much as possible in each swarm of the bottom layer when the function dimension is increasing from low to high.