Abstract: This paper proposed an efficient dynamic self-adaptive differential evolution (EDSDE) algorithm based on the characteristics of premature convergence,low convergence accuracy.The algorithm starts with mutation factor,mutation strategy and crossover factor.It sets the mutation factor to a linear decreasing function,incorporates an amplitude coefficient into the base vector to balance the global search and the local search,and sets the crossover factor to a dynamic self-adaptive function that is constantly oscillated within [0,1] and updated every 50 generations.The simulation results show that EDSDE can obtain better optimization results and exhibit more desirable performance then the other algorithms.

Key words: Amplitude coefficient, Dynamic self-adaptive differential evolution algorithm, Dynamic self-adaptive function, Linear decreasing function, Premature convergence