The diversity of population and the depth and way of information interaction have a profound impact on the ability of the shuffled leaping frog algorithm in climbing, exploring and development. Aiming at the shortcomings of the shuffled leaping frog algorithm, such as the inclination to local optimum, the slowness of convergence speed and the poor optimization accuracy, a new shuffled frog leaping algorithm based on reverse leaping in solution space and information interaction enhancement is proposed. Firstly, the information exchange between sub-inferior solutions and sub-optimal solutions is added to promote the utilization of information within sub-groups. In order to reduce the probability of inferior solutions in the later iteration period and to enhance the spatial development ability, the idea of reverse leaping is introduced to improve the local update mechanism. Then, the 2-opt method is used to realize the local optimal variation of sub-groups and increase the diversity of sub-groups. Finally, the local optimal crossover method is adopted to deepen the depth of interaction among subpopulations, and the reverse leaping mechanism is used to prevent population assimilation. The simulation experiments are carried out with a test sets includes 23 unimodal, multimodal and fixed-dimension complex functions. The results of simulation show that the proposed algorithm has better search performance, improves the diversity of population effectively, prevents premature convergence of the algorithm, and can adapt to different types of function optimization problems.