where HV improvement is null. We note that EGBO reaches its highest probability density between the unconstrained and constrained PF, especially near the region where the PF is discontinuous, since many data points are required to learn a disjointed POS. The probability maps show that qNEHVI-BO cannot handle constraints well, whereas EGBO manages to avoid 𝑐 2 violations, and some 𝑐 1 violations. This is supported by the evolution of constraints violation over the iterations (Fig. The sampling non-uniformity of qNParEGO is the lowest and, unsurprisingly, that of U-NSGA-III the highest. Indeed, U-NSGA-III tends to propose candidates that are in the vicinity of the parents.