Particle swarm optimization with hierarchical structure

This paper proposes a Particle Swarm Optimization (PSO) with hierarchical structure. In the proposed method, Particles are separated into some groups, and besides, in a group Particle of the best value is selected. Particles of the best value in each group are formed higher lank layer and applied to Gbest Model. Then, the proposed method is validated through numerical simulations with several functions which are well known as optimization benchmark problems comparing to the conventional PSO methods.