Dynamic Small World Network Topology for Particle Swarm Optimization

Abstract

A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.