Collaborative evolutionary swarm optimization with a gauss chaotic sequence generator

Abstract

A new hybrid approach to optimization in dynamical environments called Collaborative Evolutionary-Swarm Optimization (CESO) is presented. CESO tracks moving optima in a dynamical environment by combining the search abilities of an evolutionary algorithm for multimodal optimization and a particle swarm optimization algorithm. A collaborative mechanism between the two methods is proposed by which the diversity provided by the multimodal technique is transmitted to the particle swarm in order to prevent its premature convergence. The effect of changing the random number generator used for selection and for variation operators within CESO with a chaotic sequence generator is tested. © 2007 Springer-Verlag Berlin Heidelberg.