Research on Maximum Power Point Tracking Based on an Improved Harris Hawks Optimization Algorithm

This paper proposes an improved Harris Hawks Optimization (IHHO) algorithm to enhance maximum power point tracking (MPPT) performance in photovoltaic (PV) systems operating under various conditions. The IHHO introduces Tent chaotic mapping to improve population diversity and avoid premature convergence, a nonlinear decreasing inertia weight to dynamically balance exploration and exploitation, and a hybrid perturbation mechanism based on differential evolution to enhance local refinement. Additionally, a dynamic step-size adjustment and an escape energy mechanism responsive to irradiance changes improve real-time tracking adaptability. Parallel computing is employed to accelerate fitness evaluations and improve computational efficiency. Simulation results under multiple static and dynamic shading scenarios demonstrate that the proposed IHHO algorithm consistently achieves faster convergence, higher tracking accuracy, and stronger robustness than conventional methods such as particle swarm optimization (PSO) and Jaya. These results confirm the effectiveness of IHHO for reliable MPPT control in practical PV applications under diverse and challenging environmental conditions.