期刊名称：International Journal of Intelligence Science
印刷版ISSN：2163-0283
电子版ISSN：2163-0356
出版年度：2021
卷号：11
期号：1
页码：17-30
DOI：10.4236/ijis.2021.111002
语种：English
出版社：Scientific Research Publishing
摘要：Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which makes the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper proposes covariance matrix learning differential evolution algorithm based on correlation (denoted as RCLDE) to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population; secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation; then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally, the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is an effective algorithm.
