A Differential Evolution Algorithm with Minimum Distance Mutation Operator

This paper proposes a novel mutation operator named minimum distance mutation for differential evolution (DE) algorithm. We try to improve the local search ability of the algorithm in the mutation operation. During the mutation operation, the selected base particle will be compared with the nearest particle. The better particle will be selected for the mutation operation in this way the neighborhood information can be applied. A set of famous benchmark functions has been used to test and evaluate the performance of the proposed algorithm. The experimental results show that the proposed algorithm has achieved good improvement.