Fig. 3: Comparison of hyper-volume improvements in batch and sequential Bayesian optimization.
From: Bayesian optimization with active learning of design constraints using an entropy-based approach
Only 13 iteration is needed to reach the same Pareto front estimation quality in comparison to the sequential approach. 48 cores are accessible in our supercomputing system. Thus, it is possible to run 48 experiments in parallel without additional wall-time in batch Bayesian optimization case.
