Performance Evaluation of an Improved Particle Swarm Optimization Algorithm Against Nature-Inspired Methods for Photovoltaic Parameter

Abstract

Accurate parameter extraction is essential for reliable photovoltaic (PV) modeling and performance assessment. This study proposes an improved Particle Swarm Optimization (IPSO) algorithm and presents a comparative evaluation against particle swarm optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Artificial Bee Colony (ABC), simulated annealing (SA), and Nelder–Mead (NM) for estimating the parameters of single-, double-, and triple-diode PV models. All algorithms are tested using identical experimental I–V data and evaluated in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), coefficient of determination (R2), and computational time. The proposed IPSO significantly enhances convergence accuracy and stability for SDMs and DDMs, achieving very low best-case RMSE values with R2 exceeding 0.9999. For the more complex TDM, IPSO attains the lowest best-case error, while DE and ABC exhibit superior robustness in terms of mean error and variance. Overall, the results demonstrate the effectiveness of the proposed IPSO and highlight the trade-off between accuracy and robustness when selecting optimization algorithms for PV parameter extraction.