Differential evolution with k-nearest-neighbour-based mutation operator
Cong Wu, Gang Liu · International Journal of Computational Science and Engineering · 2019
Differential evolution (DE) is one of the most powerful global numerical optimisation algorithms in the evolutionary algorithm family and it is popular for its simplicity and effectiveness in solving numerous real-world optimisation problems in real-valued spaces. The performance of DE depends on its mutation strategy. However, the traditional mutation operators are difficult to balance the exploration and exploitation. To address these issues, in this paper, a k-nearest-neighbour-based mutation operator is proposed for improving the search ability of DE. The k-nearest-neighbour-based mutation operator is used to search in the areas which the vector density distribution is sparse. This method enhances the exploitation of DE and accelerates the convergence of the algorithm. In order to evaluate the effectiveness of our proposed mutation operator on DE, this paper compares other state-of-the-art evolutionary algorithms with the proposed algorithm. Experimental verifications are conducted on the CEC '05 competition and two real-world problems. Experimental results indicate that our proposed mutation operator is able to enhance the performance of DE and can perform significantly better than, or at least comparable to, several state-of-the-art DE variants.