Title: Cooperative Co-evolutionary Differential Evolution for Function Optimization
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).
Publication Year: 2005
Publication Date: 2005-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 175
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