作者: Yan-jun Shi , Hong-fei Teng , Zi-qiang Li
DOI: 10.1007/11539117_147
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摘要: 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 problems. This paper presents new variation on the DE algorithm, called cooperative co-evolutionary (CCDE). CCDE adopts architecture, which was proposed by Potter had genetic improve significantly performance of DE. Such improvement achieved partitioning high-dimensional search space splitting solution vectors into smaller vectors, then using multiple cooperating subpopulations (or vectors) co-evolve subcomponents solution. Applying 11 benchmark functions, we show marked in over traditional (CCGA).