Abstract: The difficulties of designing a multi-strategy differential evolution (DE) algorithm are how to select the mutation strategies and allocate these strategies. A multi-strategy DE algorithm combined with the neighborhood search operator is proposed. The population is divided into three subpopulations according to the fitness values, and each subpopulation employs a different mutation strategy and parameter settings to complement the search ability, to balance the exploration and exploitation ability of the whole population. The subpopulation with the best fitness values employs the neighborhood search operator to exploit possible benefit information to guide the search. Extensive experiments are carried out on 34 test functions to compare with 12 different evolutionary algorithms, which include the 7 DE algorithms. The results show that the algorithm can perform better on most test functions.

Key words: differential evolution, multiple strategies, neighborhood search, exploration ability, exploitation ability