Particle Swarm Optimization with Hierarchical Structure

Abstract

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 particles are parted the particle of the best value from other particles. Particles of the best value in each group are applied to Gbest-Model, and other particles are applied to Lbest-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.