Differential Evolution Algorithm of Basis Vector Scaling and Mirror Crossover
Chunmei Zhang, Haibin Yang, Hongge Guo, Yanjun Shi · 2020
In order to avoid the problem of local optimality and low convergence accuracy, an improved differential evolution algorithm was proposed by combining the base vector scaling with the mirror crossover operation. Based on the basic difference algorithm, the base vector scaling coefficient is introduced to jump out of the local optimal value. In the process of crossover, mirror crossover is introduced to improve the probability of the better trial vector entering the selection step and promote the algorithm evolution. In order to verify the effectiveness of the improved differential evolution algorithm, the simulation results of 10 benchmark functions are compared with those of other algorithms in this paper. The results show that the proposed algorithm can avoid the algorithm from falling into local optimum and achieve better convergence performance.