It shows that MO-E-EQI, qNEHVI, qNParEGO, TSEMO show significant efficiency than simple sequential strategy LHS Space Filling. MO-E-EQI finds the optimal solutions quickly followed by qNEHVI, while qNParEGO and TSEMO show relatively slower HV distance decrease. Comparing between single-and multipleruns, MO-E-EQI works better with multiple model runs. When increasing the noise level from 0.01 to 0.20, all algorithms show worse performance. It is noticeable for a high noise setting, that the HV distance for the qNEHVI algorithm drops and then starts growing again after about 10 iterations. This is potentially due to the overestimation of the true Pareto front. In that case, when a new input is suggested and the noisy observation over-estimates the true response, the Pareto front identified by the algorithm can start moving further away from the true Pareto front.