This work studied the effects of noises on four metaheuristic algorithms, namely the Self-Adaptive Differential Evolution (DE), Performance Guided JAYA (PGJAYA), Self-Adaptive Teaching-Learning Based Optimization (SATLBO) and Biogeography-based Heterogeneous Cuckoo Search (BHCS) for extracting the PV parameters from I-V curves considering the single-diode model. For this task, a benchmark, simulated noise-free, and noisy I-V curves, for noise levels between 0.001 and 10% were employed, considering four objective functions: RMSE, Huber loss function, MAPE, and MAE. On the benchmark curve, the PGJAYA algorithm outperformed all the others due to a better convergence speed, while on the noise-free curve the Self-Adaptive DE surpassed all the others, obtaining the lowest absolute relative errors for all parameters. On noisy curves it was found that the objective function can significantly impact the results. In this case, the Self-Adaptive DE, PGJAYA, and SATLBO with the RMSE and Huber loss function provided the lowest errors, while the BHCS showed the worst performance, with high relative errors even for small noise levels. Also, it was found that noise affects the extracted parameters distinctly: for the Self-Adaptive DE, PGJAYA and SATLBO with the RMSE and Huber loss function, the highest relative errors for I-ph were in the order of 1% for a 10% noise level; for n, in the range 7-10%; for R-s this number increased to 18.6% for a 10% noise level. R-sh and I-0 showed relative errors as high as 70 and 200%, respectively, for noise levels above 5%, being the most affected parameters.