Parameter Establishment of Differential Evolution Algorithm Based on Uniform Design and the Effect of Different Number of Levels
Tianjun Zhang, Gaochang Zhao, Yun Bai, Jie Liu · 2016
The parameter establishment of differential evolution algorithm (DE) is generally determined by the experience selection method, whose shortcomings include the massive operational parameters, the difficulty in obtaining the best parameter combination, and further obstacle to improve the optimization ability of the algorithm to a great extent. The article introduces the uniform design method of differential evolution algorithm in parameter establishment. The optimal parameters combination which can be applied to different types of standard test functions is discovered by means of the uniform design test for three different types of standard test function: the unimodal function, multimodal function, and morbid function. Finally the differential evolutionary algorithm for parameter establishment can be specified. The result is as follows. When the two groups of optimal parameter combination obtained by the uniform design experiment are applied to the differential evolution, the average global optimal solution is 3.3597 and the average standard deviation is 6.1243. It follows that the method of uniform experimental design is feasible and effective to set the parameters of the differential evolution algorithm and the method offers good stability.