Results are shown in Figure . MO-E-EQI shows a better coverage (around 50% of the Pareto front) than other algorithms for both linear and loglinear noise structures with various noise magnitudes. qNParEGO shows the worst performance in terms of coverage as the solutions tend to aggregate into the right-bottom corner. When comparing qNParEGO and MO-E-EQI with Loglinear-1 noise, the coverage metric was 0.3 for qNParEGO and 0.6 for MO-E-EQI. It is also notable that there is no clear trend of how noise structure and magnitude affect the coverage, for MO-E-EQI, the coverage dropped by 10% from Linear-1 to Linear 4 and 22% from Loglinear-1 to Loglinear-2. One thing to note is that this coverage metric only describes how distributed the solutions are, no matter how close they are to the Pareto front. LHS Space Filling strategy shows quite high scores of coverage metric, although its performance of hypervolume distance is very poor from Figure . This demonstrates that the evaluation of noisy MOBO algorithms cannot just depend on a single aspect.