Abstract: To overcome the slow convergence speed, premature convergence and tedious parameter settings of the differential evolution when solving complex optimization problems, a dynamic adaptive differential evolution, called p-ADE, based on a novel mutation strategy, is proposed. Firstly, the best global solution and the best previous solution of each individual are utilized in the new mutation strategy to guide the search direction by introducing more effective directional information, avoiding the search blindness brought by the random selection of individuals in the difference vector. Secondly, a self-adaptive parameter setting strategy is designed, which is utilized to balance the global and local search dynamically. Experimental results on 10 benchmark functions show that p-ADE can effectively improve the global search ability of DE and outperforms several state-of-the-art optimization algorithms in terms of the main performance indexes.