We further note that these algorithms do not include native constraint handling as well, making their implementation to cMOOPs less suitable. qNEHVI-BO performance remains nonetheless limited when considering constrained MOOPs. Indeed, studies from Low et al. on high dimensional problems show that qNEHVI-BO can efficiently reach the PF but will fail to sample it uniformly. In contrast, evolutionary algorithms are better at exploiting the PF but require more evaluations before reaching it. In both cases, sampling wastage will occur, as sub-optimal or infeasible candidates are suggested. Sampling wastage will even increase at larger sampling batches.