Abstract: Photovoltaic power generation will produce multi-peak output power under partial shading conditions, which may lead to maximum power point tracking failure and power loss. In this paper, two kinds of maximum power intelligent tracking theory are summarized. The first type of method are the traditional general control algorithm, such as particle swarm optimization, and the second are the hybrid methods, such as particle swarm and conductance incremental hybrid algorithm. The results show that, although the first type of theory can optimize the complex nonlinear and multi-peak power in photovoltaic power generation, the convergence time is longer, and the convergence precision is not high enough. The hybrid methods can effectively develop its strengths and avoid its weaknesses, and greatly improve search performance. For example, the hybrid algorithm of particle swarm optimization and conductance increment can track to the maximum power point around 0.25 s with an accuracy of 98.2%, while the incremental conductance method only track to the maximum power point near 0.29 s with an accuracy of 88.5%, and the particle swarm algorithm only track to the maximum power point at 0.27 s with an accuracy of 95.9%. These review provide guidance for the development of global maximum power point tracking technology in the future.