Bayesian vs Evolutionary Optimisation in Exploring Pareto Fronts for
Materials Discovery
Abstract
With advancements in automated experimental setups, material
optimisation and discovery can scale to higher throughput with larger
evaluation budgets. Two state-of-the-art algorithms with conceptually
different multi-objective optimisation strategies (Bayesian and
Evolutionary) are compared on synthetic and real-world datasets. Our
results show that the Bayesian optimisation strategy, q-Noisy Expected
Hypervolume Improvement (qNEHVI) is superior in finding solutions at the
Pareto Front rapidly, and when considering hypervolume improvement as a
performance indicator. On the other hand, the Evolutionary optimisation
strategy, Unified Non-dominated Sorting Genetic Algorithm III
(U-NSGA-III), can exploit the Pareto Front and propose a larger pool of
optimal solutions, given sufficient evaluation budget, and thus may be a
better choice for materials discovery problems where knowing the
complete Pareto Front provides greater scientific value to understanding
materials space. We discuss the limitations of using hypervolume as a
performance indicator for optimisation strategies, alongside
hypervolume-based strategies such as qNEHVI, which do not adequately
explain the number of solutions at or near the Pareto Front. We also
performed a comparison of both optimisation strategies at different
batch sizes to consider throughput capabilities.