In terms of MOBO with noisy observations, challenges are two-fold. Firstly, instead of a single solution, the algorithm should be able to identify the trade-offs between objectives and construct a Pareto front. The more diverse the solutions are (i.e. the more they cover the entire spectrum of the trade-off) the better. Secondly, evaluating the performances of MOBO algorithms is not always straightforward under noisy conditions. Some popular performance metrics may be misguided by the noisy points in the current Pareto front and suggest a wrong direction of optimization. Therefore, designing MOBO algorithms that can deal with noise for robust applications is in high demand.