Advanced Meta-Heuristic Optimization for Accurate Photovoltaic Model Parameterization: A High-Accuracy Estimation Using Spider Wasp Optimization

Accurate parameter extraction of photovoltaic (PV) models plays a critical role in enabling precise performance prediction, optimal system sizing, and effective operational control under diverse environmental conditions. While a wide range of metaheuristic optimisation techniques have been applied to this problem, many existing methods are hindered by slow convergence rates, susceptibility to premature stagnation, and reduced accuracy when applied to complex multi-diode PV configurations. These limitations can lead to suboptimal modelling, reducing the efficiency of PV system design and operation. In this work, we propose an enhanced hybrid optimisation approach, the modified Spider Wasp Optimization (mSWO) with Opposition-Based Learning algorithm, which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization (SWO) metaheuristic with the diversity-enhancing mechanism of Opposition-Based Learning (OBL). The hybridisation is designed to dynamically expand the search space coverage, avoid premature convergence, and improve both convergence speed and precision in high-dimensional optimisation tasks. The mSWO algorithm is applied to three well-established PV configurations: the single diode model (SDM), the double diode model (DDM), and the triple diode model (TDM). Real experimental current–voltage (I–V) datasets from a commercial PV module under standard test conditions (STC) are used for evaluation. Comparative analysis is conducted against eighteen advanced metaheuristic algorithms, including BSDE, RLGBO, GWOCS, MFO, EO, TSA, and SCA. Performance metrics include minimum, mean, and maximum root mean square error (RMSE), standard deviation (SD), and convergence behaviour over 30 independent runs. The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models, achieving the lowest RMSE values of 0.000986022 (SDM), 0.000982884 (DDM), and 0.000982529 (TDM), with minimal SD values, indicating remarkable repeatability. Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods, with the performance gap widening as model complexity increases. These findings demonstrate that mSWO provides a scalable, computationally efficient, and highly reliable framework for PV parameter extraction. Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems, including performance monitoring, fault detection, and intelligent control, thereby contributing to the optimisation of next-generation solar energy solutions.