%0 Journal Article
%T Covariance Matrix Learning Differential Evolution Algorithm Based on Correlation
%A Sainan Yuan
%A Quanxi Feng
%J International Journal of Intelligence Science
%P 17-30
%@ 2163-0356
%D 2021
%I Scientific Research Publishing
%R 10.4236/ijis.2021.111002
%X Differential evolution
algorithm based on the covariance matrix learning can adjust the coordinate
system according to the characteristics of the population, which make<span style=\"font-family:Verdana;\">s</span><span style=\"font-family:Verdana;\"> the search move in a more
favorable direction. In order to obtain more accurate information about the
function shape, this paper propose</span><span style=\"font-family:Verdana;\">s</span><span style=\"font-family:;\" \"=\"\"> <span style=\"font-family:Verdana;\">covariance</span><span style=\"font-family:Verdana;\"> matrix learning
differential evolution algorithm based on correlation (denoted as RCLDE)</span></span><span style=\"font-family:;\" \"=\"\"> </span><span style=\"font-family:Verdana;\">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,</span><span style=\"font-family:;\" \"=\"\"> </span><span style=\"font-family:;\" \"=\"\"><span style=\"font-family:Verdana;\">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 </span><span style=\"font-family:Verdana;\">an effective algorithm</span><span style=\"font-family:Verdana;\">.</span></span>
%K Differential Evolution Algorithm
%K Correlation
%K Covariance Matrix
%K Parameter Self-Adaptive Technique
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=105818