The process of MOBO starts by sampling a small number of initial data points to construct a surrogate model for each objective, which is often a Gaussian process (GP) model. The posterior of the GP provides information for an acquisition function to decide the next point to evaluate by balancing where the uncertainty of the surrogate model is large (exploration) and where the current model prediction is good (exploitation). New data are then collected, and the GP model is updated iteratively until the optimal solutions are found or the computational budget is depleted. However, one thing that cannot be neglected in reality is that the data is not always perfect and is likely to be corrupted by sometimes very high noise.