###### Abstract  
Solar generation has become increasingly important in grid applications. In order to improve the energy efficiency of the photovoltaic array (PV), factors such as temperature, nonlinear characteristics, and partial shadow conditions (PSCs) of the PV must be fully considered. An excellent maximum power point tracking (MPPT) control strategy can effectively improve the energy utilization efficiency of photovoltaic cells and provide strong support for the construction of smart campuses in terms of environmental protection and energy saving. A traditional method such as Perturb & Observe (P&O) and incremental conductance (INC) will fall into the local maximum power point (LMPP). In the past decade, researchers have proposed many MPPT methods to solve the difficulties of the PV system. However, they have failed to fully consider dynamic changes in irradiance conditions. Changes in the irradiance of photovoltaic arrays can lead to an extension of the convergence time and an increase in the oscillation amplitude. Many current MPPT methods have shortcomings such as requiring a long convergence time, large oscillation amplitude, and being prone to falling into LMPP. In order to reduce the oscillation amplitude and improve the convergence speed, a novel Multi-strategy Improved Tuna Swarm Optimization hybrid INC (ITSO-INC) method is introduced in this article. This strategy involves improving the Tuna Swarm Optimization (TSO) through Levy Flight and a linear weight coefficient. In addition, the INC method is added in the later stage to improve the accuracy of MPPT tracking. The proposed algorithm can extract the global maximum power point under different partial shading. In order to verify the effectiveness of the proposed method, the proposed method was compared with other metaheuristic algorithms such as Cuckoo Search (CS) and TSO. The proposed ITSO-INC technique was tested over four different patterns of partial shading conditions. Modulation was performed by tracking the sudden change in the shadow pattern of the MPP. These simulation results confirm that the proposed method has fast convergence, high accuracy, zero steady state oscillation, and a rapid response to dynamic change. An analysis of the output characteristics of PV cells shows that changes in light intensity and temperature can cause changes in the maximum power point (MPP), but there is always a GMPP for a particular light intensity and temperature [3]. The task of MPPT is to find that point and keep the PV cell in that state for continuous operation and maximum external power output to improve PV cell utilization. Traditional MPPT methods have good optimization finding characteristics under stable conditions such as constant illumination and constant temperature. However, local shadows, for example from birds and trees, often disturb PV modules when they are arranged [4]. These partial shadow conditions (PSCs) can lead to different levels of solar irradiance for each part of the PV module. These methods often suffer from the continuous oscillation of the P-U characteristic curve and tend to fall into local power maxima when dealing with complex shading changes, thus reducing the PV system output power and affecting the system efficiency. A metaheuristic algorithm ensures that the PV module operates at the GMPP, minimizing the effect of shadows on the PV cells. At the same time, the algorithm has good adaptability in dealing with multi-peak and non-linear function-seeking problems, and has been widely studied and applied by many scholars in the MPPT control of photovoltaic power generation [5]. Traditional MPPT methods include perturbation observations (P&O) and conductance increment algorithms (INC), which are widely used because of their simple design and easy implementation. However, P&O generates large oscillations near the MPP, and the solution accuracy is poor. The INC method does not easily converge to a stable MPP, generating continuous oscillations when disturbed [6].