Cooperative co-evolutionary differential evolution for function optimization

The differential evolution (DE) is a stochastic, population-based, and relatively unknown evolutionary algorithm for global optimization that has recently been successfully applied to many optimization problems. This paper presents a new variation on the DE algorithm, called the cooperative coevolutionary differential evolution (CCDE). CCDE adopts the cooperative coevolutionary architecture, which was proposed by Potter and had been successfully applied to genetic algorithm, to improve significantly the performance of the DE. Such improvement is achieved by partitioning a high-dimensional search space by splitting the solution vectors of DE into smaller vectors, then using multiple cooperating subpopulations (or smaller vectors) to co-evolve subcomponents of a solution. Applying the new DE algorithm to on 11 benchmark functions, we show that CCDE has a marked improvement in performance over the traditional DE and cooperative co-evolutionary genetic algorithm (CCGA).