Figure plots the average hypervolume of each of the Bayesian optimisation strategies over the course of 50 iterations. GRYFFIN quickly improves the quality of its suggestions with less than ten iterations, but TSEMO has better terminal performance. Both strategies have large confidence intervals with approximately 10% of repeats resulting in zero hypervolume. Still, their average performance is better than other strategies evaluated. The outstanding performance of TSEMO is likely linked to the fact that it trains individual surrogate models for each objective, while the other strategies directly optimise the multi-objective transform value. As shown Figure and Table , there were not consistent patterns in which multi-objective transform worked best.