Similar to the previously simulated proof-of-concept, the MOBO algorithm improved the hypervolume significantly faster than random sampling (see Figure ). A summary of the random sampling can be found in Figure and its corresponding data set in Table . The hypervolume increases sharply during the first iteration and plateaus during the third iteration, suggesting that most non-dominated compositions are found during the first two optimization cycles. From Table , one can observe that all suggested sampling points for a potential 4 th MOBO iteration are very similar, implying that the algorithm is already trying to exploit a particular region. Figure shows how almost every sampled candidate is better than the predefined reference point. Compared to the grid search performed earlier, the MOBO was able to significantly improve the hypervolume and explore more non-dominated compositions that reside on the Pareto front, as indicated by Figure .