An Adaptive Mechanism With Cooperative Coevolution and Covariance for Differential Evolution

Abstract

Differential evolution (DE) is an evolutionary algorithm widely used to solve optimization problems with different characteristics in fields where actions and decisions depend on numerical data such as engineering, economics, and logistics. In this paper, an adaptive differential evolution mechanism with cooperative co-evolution and covariance (A-CC/COV-DE) is proposed to overcome the low efficiency of differential evolution when solving large-scale numerical optimization problems, especially when the correlation between the variables of the problem is unknown. An unknown correlation of variables hinders DE from achieving an optimal search process since different types of correlations ideally require distinct optimization strategies. According to the separability of variables, the appropriate evolutionary strategy is selected adaptively. For separable functions, cooperative coevolution is adopted. After using extended differential grouping to split the problem, the sub-components are optimized by differential evolution. This reduces the dimensionality and complexity of the problem, improving its convergence speed and global search ability. For non-separable functions, a covariance matrix is calculated, and then the eigenvector is used to rotate the coordinate system. This leads to eliminate the correlation between variables and improve the search efficiency of differential evolution. We evaluated the performance of A-CC/COV-DE on the CEC 2014 test suite and compared it with state-of-the-art differential evolution algorithms. The experimental results show that our proposal is quite competitive with recent algorithms.