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Dynamic Differential Evolution for Constrained Real-Parameter Optimization

Youyun Ao1 and Hongqin Chi2
1. School of Computer and Information, Anqing Teachers College, Anqing, China
2. Department of Computer, Shanghai Normal University, Shanghai, China
Abstract—Differential evolution (DE) has been shown to be a simple and effective evolutionary algorithm for global optimization both in benchmark test functions and many real-world applications. This paper introduces a dynamic differential evolution (D-DE) algorithm to solve constrained optimization problems. In D-DE, a novel mutation operator is firstly designed to prevent premature. Secondly, the scale factor F and the crossover probability CR are dynamic and adaptive to be beneficial for adjusting control parameters during the evolutionary process, especially, when done without any user interaction. Thirdly, D-DE uses orthogonal design method to generate initial population and reinitialize some solutions to replace some worse solutions during the search process. Finally, D-DE is validated on 6 benchmark test functions provided by the CEC 2006 special session on constrained real-parameter optimization. The experimental results obtained by D-DE are explained and discussed, and some conclusions are also drawn.

Index Terms—constrained optimization, mutation scheme, differential evolution, evolutionary algorithm, constraint handling

Cite: Youyun Ao and Hongqin Chi, "Dynamic Differential Evolution for Constrained Real-Parameter Optimization," Journal of Advances in Information Technology, Vol. 1, No. 1, pp. 43-51, February, 2010.doi:10.4304/jait.1.1.43-51
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