Concerning premature convergence and low searching capability of Differential Evolutionary (DE) algorithm, the dynamic adjustment of control parameters was dicussed, and a self-adaptive differential evolution algorithm based on opposition-based learning was proposed. In the proposed algorithm, opposition-based elite learning was used to enhance the local search ability of the population and obtain more accurate optimal individuals; meanwhile, Gaussian distribution was used to improve the exploitation ability of each individual and increase the diversity of the population, which avoids premature convergence of the algorithm and achieves the balance of the global exploitation and local exploitation. Comparison experiments with some other differential evolution algorithms were conducted on six test functions in CEC 2014. The experimental results show that the proposed algorithm outperforms the compared differential evolution algorithms in terms of convergence speed, solution accuracy and reliability.