Under a significant level of noise, it is not uncommon that a single model run could significantly overestimate the true Pareto front. The most popular metric in the literature -hypervolume (HV)-is inadequate to estimate how well the identified Pareto front is compared to the true Pareto front. With that in mind, we calculate the volume of the difference between the Pareto front identified by an algorithm and the true Pareto front. This takes into account both under-and over-estimation and combines these into one metric based on HV. A drawback of taking the difference between the true HV and the one identified by the algorithm under high noise is that where both under-and over-estimation are present, they could potentially cancel each other out and not provide a true picture of how far the identified Pareto front is from the true one (see Figure for details).