In real-time high-dimensional optimization problem, how to quickly find the optimal solution and give a timely response or decisive adjustment is very important. This paper suggests a self-adaptive differential evolution algorithm (abbreviation for APDDE), which introduces the corresponding detecting values (the values near the current parameter) for individual iteration during the differential evolution. Then, integrating the detecting values into two mutation strategies to produce offspring population and the corresponding parameter values of champion are retained. In addition, the whole populations are divided into a predefined number of groups. The individuals of each group are attracted by the best vector of their own group and implemented a new mutation strategy DE/Current-to-lbest/1 to keep balance of exploitation and exploration capabilities during the differential evolution. The proposed variant, APDDE, is examined on several widely used benchmark functions in the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization (13 global numerical optimization problems) and 7 well-known basic benchmark functions, and the experimental results show that the proposed APDDE algorithm improves the existing performance of other algorithms when dealing with the high-dimensional and multimodal problems.