Probability density maps in objective space for 10 optimisation runs of 24 iterations x 8 points per batch. a-b) Thin Film. c-d) Concrete Slump. The evaluated data points are plotted with a Gaussian kernel density estimate using SciPy to illustrate the distribution of points across objective space, with a colour bar to represent the numerical value of probability density. Results are averaged over 10 runs, taking a smaller evaluation budget of 24 iterations x 8 points = 192. The results here reinforce the finding that qNEHVI has a more random distribution of points, but still outperforms U-NSGA-III for a low evaluation budget.