Boosting Walrus Optimizer Algorithm based on ranking-based update mechanism for parameters identification of photovoltaic cell models

This study introduces the Improved Walrus Optimizer (m_WO), an enhanced meta-heuristic algorithm for precise parameter estimation of photovoltaic (PV) models, including single-diode, double-diode, and triple-diode configurations. By incorporating ranking-based update mechanisms, m_WO achieves superior search efficiency, convergence speed, and robustness compared to established algorithms like Grey Wolf Optimizer (GWO) and Hunger Games Search (HGS). Experimental results demonstrate that m_WO significantly minimizes root mean square error (RMSE) values while maintaining fast computational times. For example, RMSE values of 0.0020424, 0.0016602, and 0.014584 were achieved for Photowatt-PWP201, STM6-40/36, and STP6-120/36 models, respectively, with an average computation time of 3 s. These findings position m_WO as a powerful tool for advancing solar energy research and improving PV system efficiency.