Journal of Computers, Vol 6, No 3 (2011), 524-531, Mar 2011
doi:10.4304/jcp.6.3.524-531

A Self-Adaptive Differential Evolution Algorithm with Dimension Perturb Strategy

Wei-Ping Lee, Chang-Yu Chiang

Abstract


Differential Evolution (DE) has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In this paper that proposed a dimension perturb strategy and self-adaptive F value in original DE to increase the exploration ability and exploitation ability. Self-adaptive has been found to be highly beneficial for adjusting control parameters. The performance of self-adaptive differential evolution algorithm with dimension perturb strategy (PSADE) is showed on the following performance measures by benchmark functions: the solution quality and solution stability. This paper has found that PSADE can efficiently find the global value of these functions.


Keywords


Differential Evolution; Dimension Perturb Strategy; Self-adaptive

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