Accurate parameter estimation of photovoltaic (PV) models is essential for performance evaluation, efficiency enhancement, and reliable energy forecasting in solar energy systems. However, the nonlinear, multimodal, and implicit nature of the current–voltage (I–V) relationship makes this task challenging for conventional optimization methods, which often suffer from premature convergence and sensitivity to initial conditions. In this study, a novel variant of the Artificial Circulatory System Algorithm, termed Dynamic Elite Cooperative ACSA (DEC-ACSA), is proposed for estimating the unknown parameters of the Single-Diode Model (SDM). The proposed approach extends the original ACSA by incorporating dynamic population grouping, elite-guided local search, and cooperative interaction mechanisms, thereby enhancing the balance between exploration and exploitation. Additionally, a Gaussian-based perturbation strategy is integrated into the neural update phase to improve local search capability. The objective is to minimize the root mean square error between measured and modeled current values using experimental I–V data from a standard photovoltaic module. The performance of DEC-ACSA is evaluated in comparison with the original ACSA to explicitly demonstrate the impact of the proposed enhancements. Experimental results indicate that DEC-ACSA achieves faster convergence, improved solution stability, and lower estimation error compared to the baseline ACSA. These findings suggest that the proposed method provides a robust and effective optimization framework for solving nonlinear photovoltaic parameter estimation problems and offers a promising alternative for energy system modeling applications.