Summary: The optimization of complex problems has always been a difficult task in the realm of evolutionary computation, as complex problems often have a large search space. Adding more dimensions to the decision variables also makes the search space more complicated, which slows down the algorithm. However, as the number of locally optimal solutions to the complex problem grows exponentially, it becomes easy for the algorithm to land in a local optimal region. In light of this background, this paper proposes a paradigm-crossover-based differential evolution algorithm with search space reduction and diversity exploration. During the evolution process, the proposed algorithm obtains the correlation coefficient for each dimension of the problem. Based on this correlation, it generates a paradigm that participates in crossover, accelerating the population’s movement towards promising regions. When the algorithm faces premature convergence and stagnation, it executes search space reduction and diversity exploration at the dimensional level to discard the unpromising search space and enhance the population diversity in the promising search space. We compared our proposed algorithm with eight state-of-the-art evolutionary algorithms in the CEC2017-BC test set to confirm its effectiveness, and the experimental results demonstrated its notable advantages for solution accuracy and convergence speed on high-dimensional complex problems.