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Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed hybrid differential evolution algorithm can accelerate convergence in the late evolution, and jump out of local optimum, which are problems in the traditional DE. In this paper, several common formulas for mutation in DE are integrated together. It combines all the advantages to make the global searching ability better, thus jumping out of the local optimum. This algorithm introduces the idea of simulated annealing to improve the evolution efficiency by selecting the appropriate annealing control parameters. It also employs adaptive Gaussian immune algorithm which can help escape from local optimum. Experiments show that compared with the traditional DE on the same parameter conditions, the proposed algorithm has better performance, showing better convergence and stability.