The in silico study shows that noise does make a difference and affects the algorithm performances. High noise degrades the performance of all algorithms involved in the study. However, no significant difference was observed for different noise structures with linear and loglinear cases. For MOBO under noise, it is shown that metrics to evaluate algorithm performances can be problematic. Overall, MO-E-EQI shows robust performances in terms of HV distance and solution coverage compared to other algorithms. The selection of aggressive and non-aggressive criteria of MO-E-EQI can tune the number of solutions on the Pareto front based on the need.