Additionally, Chimera and the custom multi-objective transform were tested, and the best combination by terminal hypervolume for each strategy is shown. Figure plots the hypervolume after 50 iterations and the average computation time per suggestion for each strategy. On average, the Bayesian optimisation strategies (GRYFFIN, SOBO, and TSEMO) perform best, finding the most optimal points in the allotted number of experiments. However, this performance comes at the price of three orders of magnitude greater computation cost. Furthermore, these tests were run on a high performance computing cluster with up to 32 threads dedicated for each strategy, and our experience is that the runtime increases by a factor of ten on consumer hardware. The longer computation time is likely acceptable in the case of TSEMO given the improvement in performance, but it might not be justified in the case of GRYFFIN or the SOBO strategies. Interestingly, Nelder-Mead Simplex, which has been used in several studies, failed to find any optimal points, even with ten random restarts on each run; in fact, random sampling performed better. This is because Nelder-Mead is a local search strategy, and we found that the multi-objective scalarisation functions (e.g., Chimera) have many local optima (see Figure ).