Differential Evolution (DE) algorithm, first published in 1995, has proved to be a powerful tool for complicated optimization problems. Its outstanding performance makes it applicable to different application fields. However, since differential evolution belongs to the Evolutionary Algorithm, problems like trapping in local optimum may also occur in this algorithm. In order to improve the performance of differential evolution, a novel algorithm will generate a dynamical ”mutation factor”. By this approach, we can increase the performance of DE algorithm and avoid dropping into local optimums.