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To solve the problem that the global extreme value tracking of photovoltaic array under multi-peak is easy to fall into local optimization, this paper proposes a multi-peak maximum power point tracking (MPPT) scheme of photovoltaic array based on an improved imperialist competitive algorithm (ICA) by exploring the applicability of standard ICA in the MPPT of photovoltaic array under local shadow. Firstly, in order to enhance the global search performance of the ICA, the assimilation process of the ICA is improved by using simulated binary cross learning method. Secondly, the adaptive revolution factor is introduced to improve the local optimization ability of the algorithm, and the optimal value selection mechanism is used to retain the excellent factor of the original empire to improve the convergence speed of the algorithm. Then, based on the improved ICA , a MPPT simulation model of photovoltaic array is constructed. Finally, static and dynamic performances of the improved algorithm for photovoltaic array MPPT are simulated and compared. The results show that the improved ICA can accurately track the static situation, with the tracking time of 0.18 s and the dynamic maximum power tracking time of 0.19 s, with an error of 0.9%. It shows that the proposed method can not only realize the dynamic and static MPPT under different shadows, but also improve the tracking accuracy and response speed.