Particle Swarm Optimization With Dynamic Neighborhood

Abstract

In this article we consider a particle swarm optimization (PSO) algorithm in which the neighbors of the particles or the neighborhood topology dynamically changes with time. We consider probabilistic and distance based approaches for determining the neighbors of the particles and represent the dynamic neighborhood topology by a time varying graph. Moreover, we test the performance of the algorithm with a simulation example.