Varying the Topology and Probability of Re-Initialization in Particle Swarm Optimization

This paper introduces two new versions of dissipative particle swarm optimization. Both of these use a new time-dependent strategy for randomly re-initializing the positions of the particles. In addition, one variation also uses a novel dynamic neighbourhood topology based on small world networks. We present results from applying these algorithms to two well-known function optimization problems. Both algorithms perform considerably better than both standard PSO and the original dissipative PSO algorithms. In particular one version performs significantly better on high-dimensional problems that are inaccessible to traditional methods.