The reliable monitoring of vital signs, such as heart rate, is highly dependent on the reliability and robustness of the estimation method used, besides other factors such as the choice of sensing technology and sensor location. Photo-plethysmography (PPG) signals are typically used for vital signs monitoring especially in domestic settings. In this work, the dominance-scoring based and exponentially-weighted-moving-average driven (DS-EWMA) computational algorithm —which was introduced in a recent publication— was evaluated against 6 state-of-the-art pulse rate estimation algorithms: HeartPy, BioSPPy, Msptd, Qppg, Ampd, and Erma, using 4 different PPG datasets. Those algorithms were compared using a variety of metrics, namely; root mean squared error (RMSE), mean absolute error (MAE), error standard deviation (STD), Pearson correlation coefficient (PCC), mean absolute percentage error (MAPE), percentage output, and ±5 BPM percentage accuracy. The results show that, overall, in stationary conditions and when using finger PPG signals, HeartPy and Msptd often outperformed the remaining methods, while DS-EWMA, Ampd and Erma performed moderately well. On the other hand, when using PPG signals with challenging characteristics such as neck PPG or finger PPG during motion, DS-EWMA was able to generally outperform the other methods, while HeartPy, Qppg, and BioSPPy suffered the most. This work demonstrates the potential robustness of the DS-EWMA method, which can be refined and adapted to further improve its performance.