On The Convergence of Information Exchange Methods in Multiple Cooperating Swarms

Different cooperative models have been proposed in the past few years using particle swarm optimization (PSO). These models relied on having more than one swarm running in a parallel or serial fashion while exchanging information among them. The information shared among the cooperating swarms was mostly their global bests. This work adopts the approach of selecting a particle from one swarm replacing a particle in another swarm. The work compares four different approaches for information exchange based on three different performance measures. The work also tests how this behavior changes while increasing the number of exchanged particles. Experiments are run using four benchmark optimization functions.