Dimension-wise adaptive snake optimizer with Sobol initialization and Lévy spiral search for solar photovoltaic cell model optimization.

Metaheuristic algorithms have received significant attention given their ability to handle complex, nonlinear, and high-dimensional optimization problems. Precise parameter identification of photovoltaic (PV) models is crucial for enhancing the performance and reliability of solar energy systems. This study introduces a modification to the Snake Optimizer (SO) algorithm called Dimension-wise Adaptive Snake Optimizer (DASO) with Sobol initialization and Lévy-Spiral search operators to enhance the balance between the exploration and exploitation phases and to strengthen the convergence accuracy of the algorithm. The performance of the proposed approach is tested using a variety of benchmark functions, covering unimodal, multimodal, and composite functions from the CEC test suite. DASO was tested on CEC2017 with various dimensions, and CEC2020. Additionally, non-parametric statistical tests, such as the Wilcoxon signed-rank test for pairwise assessment and the Friedman test for overall comparison, are utilized to confirm the significance of the results. The experimental results indicate that the suggested approach exhibits superior precision, robustness, and convergence capabilities compared to several established metaheuristic techniques. The statistical analysis verifies that the found enhancements are significant at the 5% significance level on the benchmarks. In addition, an ablation study together with exploration-exploitation and population diversity analyses is conducted to validate the contribution of each enhancement mechanism and provide deeper insight into the search behavior of DASO. The proposed method is employed in the estimation of the parameters of the single and double diode photovoltaic cell models. The experimental findings indicate that the proposed method attains lower root mean square error (RMSE) values and enhanced convergence stability in comparison to competing algorithms. The experimental results indicate that the suggested algorithm offers an efficient framework for photovoltaic parameter identification and nonlinear optimization problems.