Particle Swarm Optimization With Dynamic Neighborhood Topology: Three Neighborhood Strategies and Preliminary Results

Abstract

In this article we consider a particle swarm optimization (PSO) algorithm in which the neighbors of the particles or basically 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. Simulations of several benchmark functions are performed in order to verify the effectiveness of the algorithm.