A collaborative model for tracking optima in dynamic environments

Abstract

A new hybrid approach to optimization in dynamic environments called collaborative evolutionary-swarm optimization (CESO) is presented. CESO is a simple method for tracking moving optima in a dynamic environment by combining the search abilities of an evolutionary algorithm for multimodal optimization and a particle swarm optimization algorithm. A collaborative mechanism is designed for the two methods. Numerical experiments indicate CESO to be an efficient method for the selected test problems compared with other evolutionary approaches.