In order to accelerate the convergence speed of the traditional DE algorithms for some complex optimization problems, an adaptive bare-bones differential evolution based on cosine (CABDE) was proposed. In the proposed CABDE, a new adaptive mechanism for mutation strategy selection was presented. Moreover, a cosine adaptive factor was introduced to achieve the complementary advantages of the Gaussian mutation strategy and the DE/current-to-best/1 mutation strategy. The Gaussian mutation strategy has excellent global search ability, which is good for maintaining the population diversity; While the DE/current-to-best/1 mutation strategy exhibits good local search ability, which is helpful for accelerating the convergence speed. Therefore, the presented adaptive mechanism can maintain a balance between the exploration and exploitation. In addition, the information of the best individual in both Gaussian mutation strategy and DE/current-to-best/1 mutation strategy was utilized to guide the search directions. During the evolution process, the cosine adaptive factor was adjusted according to the increase of the iterations and then the suitable mutation strategies in the different evolutionary stages were adaptively chosen. As a result, the presented adaptive mechanism can maintain the population diversity as well as accelerate the convergence speed. To test the performance of CABDE, 18 test functions with different characteristics were used in the experiments. The effectiveness of the mutation strategies and the dynamic parameters were discussed. The experimental results showed that the mutation strategies and the dynamic parameters can improve the search performance. Moreover, CABDE was compared with several new variants of bare-bones algorithms, DE variants, PSO variants, and ABC variants. The comparisons indicated that CABDE can achieve better solutions and exhibit faster convergence speed.
