Parameter extraction is a critical process in photovoltaic (PV) modeling, directly impacting the accuracy and efficiency of solar energy systems. In this paper, we employ the Four Vector Intelligent Metaheuristic (FVIM) to extract parameters of the RTC France PV cell using the Single Diode Model (SDM). FVIM, an advanced optimization algorithm, leverages the dynamic interaction of four guiding vectors—Alpha, Beta, Delta, and Gamma—to balance exploration and exploitation, thereby avoiding premature convergence and improving solution accuracy. The performance of FVIM is rigorously evaluated against three widely known metaheuristics: Grey Wolf Optimizer (GWO), Atomic Orbital Search (AOS), and Whale Optimization Algorithm (WOA). Each algorithm is executed 30 independent runs in MATLAB, and key metrics such as minimum, maximum, mean, and standard deviation of the Root Mean Square Error (RMSE), as well as execution time, are analyzed. Experimental results demonstrate that FVIM outperforms the comparative algorithms, yielding superior accuracy and stability with significantly competitive execution times. These findings highlight the robustness of FVIM and suggest its potential as a highly effective tool for parameter extraction in PV cells and modules. Future studies may explore its generalization to a broader range of PV technologies.