To address the issue of traditional algorithms being prone to local optima during the maximum power point tracking (MPPT) process due to the multipeak characteristic of photovoltaic array output power curves under partial shading conditions, this paper proposes an MPPT control strategy combining the improved Black-winged kite algorithm (TBKA) and the Perturb and observe method (P&O), referred to as TBKA-P&O. In the global search phase, the population is first initialized using the Tent-Logistic-Cosine chaotic mapping. Then, a tangent flight strategy is introduced to enhance the search efficiency and convergence accuracy of the TBKA. Additionally, a dynamic lens imaging reverse learning strategy based on a greedy approach is designed to improve search diversity and prevent local optima. In the local search phase, the P&O method is incorporated to achieve rapid localization and high-precision tracking of the maximum power point. To verify the effectiveness of the proposed algorithm, a photovoltaic power generation system simulation model was constructed, incorporating the traditional P&O algorithm, the BKA-P&O algorithm, the quantum CS-P&O algorithm, and the TBKA-P&O algorithm. Experimental results demonstrate that the TBKA-P&O algorithm achieved tracking accuracies of 100%, 99.97%, 99.96% and 99.96% under four operating conditions, with corresponding tracking times of 0.093, 0.090, 0.077 and 0.047 s. Compared to other algorithms, the TBKA-P&O algorithm exhibited significant advantages in terms of dynamic tracking speed, steady-state tracking accuracy, and power oscillation control.