Abstract
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.
Highlights
Optimization problems are everywhere, and how to better solve optimization problems has become a current research hotspot
In order to obtain more accurate information about the function shape, this paper proposes covariance matrix learning differential evolution algorithm based on correlation to improve the search efficiency of the algorithm
In order to solve the problem of information duplication between individuals and reduce algorithm efficiency, this paper proposes a differential evolution algorithm for covariance matrix learning based on correlation research
Summary
Optimization problems are everywhere, and how to better solve optimization problems has become a current research hotspot. Wang Yong et al [5] proposed a differential evolution algorithm based on covariance matrix learning (CoBiDE) in 2014. This algorithm combines eigenvectors with crossover operators to make the algorithm rotate and not deform. Awad et al [7] proposed a covariance matrix learning constraint optimization algorithm based on euclidean distance (LSHADE-cnEpSin) in 2017. In order to solve the problem of information duplication between individuals and reduce algorithm efficiency, this paper proposes a differential evolution algorithm for covariance matrix learning based on correlation research. The algorithm calculates the correlation coefficient between all individuals in the population, eliminates the individuals with strong correlation, uses the remaining individuals to construct a covariance matrix, realizes coordinate rotation, and improves the search efficiency of the algorithm. The rest of this paper is organized as follows: Section 2 introduces the preparatory work, Section 3 introduces the algorithm proposed in this paper, Section 4 analyzes the numerical experiment results, and Section 5 summarizes this paper
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