Probability density maps in objective space for 10 runs of 24 iterations x 8 points per batch. a-b) ZDT1. c-d) ZDT2. e-f) ZDT3. g-h) MW7. The evaluated data points are plotted with a Gaussian kernel density estimate using SciPy to illustrate the distribution of points across objective space. The colour bar represents the numerical value of probability density. Results are averaged over the 10 runs and highlight the lower diversity of points and consistency in optimisation trajectory for qNEHVI compared to U-NSGA-III.