Improved tunicate swarm optimizer for solar cells parameter identification

Renewable energy, particularly solar energy, is increasingly recognized as a sustainable alternative to fossil fuels owing to its clean and pollution-free nature. Accurate modelling of PhotoVoltaic (PV) cells or modules is essential for optimizing their performance, where precise parameter identification plays a crucial role. Traditional analytical and numerical techniques often suffer from limited accuracy and convergence issues. Metaheuristic algorithms offer superior capabilities for solving such complex optimization problems. This article proposes an Improved Tunicate Swarm Algorithm (ImTSA) that addresses the limitations of the original TSA, including irregular search dynamics and susceptibility to local optima. ImTSA incorporates three key enhancements: an adaptive social force mechanism; a probabilistic position update with food source selection; and stochastic mean position updates to maintain swarm diversity. These modifications enable a balanced exploration–exploitation trade-off and reduce premature convergence. The performance of ImTSA is validated through the parameter identification of multiple PV models, including the one-diode, two-diode and PV module electrical models. In addition to convergence analysis, the method is evaluated using statistical metrics, noise-robustness tests, hyperparameter sensitivity and confidence intervals. Results demonstrate that ImTSA consistently outperforms TSA and other state-of-the-art metaheuristics in terms of convergence speed, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R2 and Maximum Power Point (MPP) deviation, offering a robust and reliable approach for accurate PV model parameter estimation.