Cooperative co-evolutionary differential evolution for function optimization

Authors:
Yan-jun Shi;Hong-fei Teng;Zi-qiang Li
Affiliations:
Department of Computer Science and Engineering, Dalian University of Technology, Dalian, P.R. China;School of Mechanical Engineering, Dalian University of Technology, Dalian, P.R. China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

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 co-evolutionary differential evolution (CCDE). CCDE adopts the cooperative co-evolutionary 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).