<p>Maximum Power Point Tracking (MPPT) techniques play a crucial role in improving the efficiency of photovoltaic(PV) systems and have therefore gained widespread application. However, traditional MPPT approaches are significantly affected by environmental changes; their tracking performance and power quality often deteriorate, especially under partial shading conditions. In such scenarios, not only is the energy output reduced, but extreme conditions may also pose serious safety risks. To address the issue of swarm intelligence algorithms becoming trapped in local optima under partial shading, this paper proposes an MPPT strategy based on an improved Rime Optimization Algorithm (IRIME).In addition, RIME suffers from inherent limitations such as low convergence precision, slow response speed, and a tendency to fall into local optima. To overcome these drawbacks, a local search mechanism based on Icicle Growth Strategy is introduced, which enhances convergence behavior and reduces power fluctuations,and make the system track the maximum power point faster. Finally, the proposed MPPT control strategy was validated through MATLAB/Simulink simulations, demonstrating superior tracking efficiency and convergence speed compared to several widely-used swarm intelligence algorithms.</p>