A hybrid optimization algorithm based on Levy-flight improved tuna swarm opimization and variable step size perturbation observation method is proposed to solve the problem that the traditional maximum power point tracking algorithm is prone to local optimality due to the multi-peak power of photovoltaic arrays under local shade conditions. The real-time position update law of Levy-flight improved tuna swarm optimization algorithm is introduced to reduce the possibility of falling into local optimal. A new step change law which changes with the slope of power characteristic is designed to improve the conventional perturbation observation method and increase the maximum power tracking speed. Combining Levy-flight improved tuna swarm opimization and variable step size perturbation observation method, a hybrid optimization algorithm is constructed to further improve tracking accuracy and speed, and suppress the influence of disturbance signals. Simulation results show that the optimization time and tracking error of the proposed algorithm are 0.036s and 0%, 0.04s and 1.06%, and 0.05s and 1.06%, respectively, under the three lighting conditions of uniform full illumination, static local shading and dynamic local shading, which are superior to other comparison algorithms. And more accurate and fast to achieve the maximum power tracking of photovoltaic systems.