To illustrate the improvement with more experiments, we plot a measure of the quality of the pareto front called hypervolume. Hypervolume is a measure of the volume of a set of points in N -dimensions, and, in this case, we take the hypervolume of the total optimal points found by a strategy after a given number of iterations. Since a larger hypervolume will always correspond with a better set of optimal values, we can see if the the optimisation strategy is finding conditions that result in better values for all objectives. As shown in Figure , the hypervolume starts at zero indicating no optimal points are found. Subsequently, hypervolume rapidly increases with more iterations, indicating better values of STY and E-factor are achieved.