The registration technology of medical three-dimensional (3D) images (such as CT, MRI, etc.) and two-dimensional (2D) images (such as X-ray) has been widely used in clinical diagnosis and surgical planning. The essence of medical image registration is to use an optimization algorithm to find some kind of spatial transformation so that two images are aligned in space and structure. Usually, the registration quality is low in the process of registration due to the problem that the optimization algorithm is not accurate and easy to fall into the local extremum. In order to solve this problem, an improved equilibrium optimizer based on the Logistic-Tent chaos map and Levy flight (LTEO) is proposed. First, in order to solve the problem that the population initialization is easy to be unevenly distributed, and the randomness is too high, the Logistic-Tent chaotic map is introduced to initialize the population, increase the diversity of the population, and make them distribute in the search space as much as possible; second, the iterative function is updated to make the optimization algorithm pay more attention to the global search, improve the convergence speed of the algorithm, and help to find the global optimum solution; third, Levy flight strategy is introduced to disturb the stagnant particles and thus prevent the algorithm from falling into local extremum. Finally, LTEO is used for 2D/3D medical image registration tasks, and the frequent transmission of data in the registration process is optimized to reduce the time consumption of registration. The algorithm is verified by benchmark function tests and clinical registration experiments. The LTEO can effectively improve optimization accuracy and stability and enhance the quality of medical image registration.