Traditional differential evolution (DE) algorithm has a tendency to suffer from premature convergence. In this paper, we proposed an improved DE based on dynamic mutation operator and opposition learning strategy. These mechanisms can expand the search area and is helpful to balance exploration and exploitation of DE. Numerical experiments demonstrate that our algorithm is effective.