Accurate parameter extraction for solar cell models is crucial for photovoltaic system design, yet remains challenging due to the models’ nonlinear and multimodal nature. To address this, this paper proposes an Enhanced Mayfly Optimization Algorithm (EMOA). The enhancement incorporates two key mechanisms: (1) an Elite Opposition-Based Learning (EOBL) strategy, which expands exploration of promising solution regions to effectively evade local optima and accelerate initial convergence; and (2) a linearly decreasing gravitational coefficient, which dynamically balances global exploration and local exploitation throughout the iterative process. Experimental evaluations on four commercial photovoltaic devices demonstrate that EMOA consistently achieves statistically superior accuracy (verified by Wilcoxon signed-rank tests at α = 0 . 05 ) and higher reliability compared to several recent meta-heuristics, including the basic Mayfly Optimization algorithm (MOA), the Hippopotamus Optimization Algorithm, Puma Optimizer, and Newton–Raphson Based Optimizer across all test cases. Furthermore, executed in MATLAB on a standard workstation (Intel Xeon E5-2683V4 CPU, 128GB RAM), EMOA attains this precision with notable efficiency, requiring significantly fewer function evaluations. To facilitate reproducible research, our complete implementation, datasets, and execution scripts are publicly available at https://github.com/GuanBL78/PV .
Guan et al. (Mon,) studied this question.