Yi-Inspired Metaheuristic-based Optimization for Maximum Power Point Tracking of a PV System

This research integrates the Yi Algorithm, a metaheuristic optimization method inspired by ancient Chinese philosophy, into the Maximum Power Point Tracking (MPPT) in photovoltaic (PV) system to enhance tracking efficiency and adaptability under varying environmental conditions. Hybrid approaches combining the Yi Algorithm with other optimization techniques, such as Genetic Algorithms (GA) and Perturb & Observe (P&O), were explored to improve convergence speed and prevent local maxima. The effectiveness of the proposed algorithms was evaluated through simulation-based analysis in a MATLAB Simulink environment. The results demonstrate that hybrid approaches significantly outperform conventional methods, achieving higher power output and improved stability. Among the methods, the hybrid Yi-GA algorithm exhibited superior global search capabilities, while the hybrid Yi-P&O approach demonstrated faster convergence to the maximum power point. This study highlights the potential of metaheuristic-enhanced MPPT algorithms for optimizing PV system performance, ensuring sustainable and reliable energy generation.