Finally, we selected a few other synthetic problems from the MW test suite which provide a range of 2 and 3-objective problems with scalable number of variables and complex constraints from the ZDT suite (see cMOOPs definition in SI 1.3). These cMOOPs were selected because their constraint domains intersect the unconstrained PF, forming a disjointed constrained PF, which increases the difficulty of the optimization process. They are representative of the variety of complex problems that can be found in the real world. We report our results for these cMOOPs in SI 3.2. Like MW7, the optimization was run at 𝑛 = 8, but the batch size further increased to 𝑞 = 8. We demonstrate that EGBO outperforms qNEHVI-BO in terms of hypervolume for all the selected problems (see SI 3.2, Fig. ), even at larger batch sizes. Furthermore, EGBO is less affected by the dimensionality of the decision space than qNEHVI-BO (see SI 3.2, Fig. ).