  We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch
acquisition function that enables multi-objective Bayesian optimisation methods
to efficiently exploit parallel processing resources. Multi-Objective Bayesian
Optimisation (MOBO) is a very efficient tool for tackling expensive black-box
problems. However, most MOBO algorithms are designed as purely sequential
strategies, and existing batch approaches are prohibitively expensive for all
but the smallest of batch sizes. We show that by encouraging batch diversity
through penalising evaluations with similar predicted objective values, HIPPO
is able to cheaply build large batches of informative points. Our extensive
experimental validation demonstrates that HIPPO is at least as efficient as
existing alternatives whilst incurring an order of magnitude lower
computational overhead and scaling easily to batch sizes considerably higher
than currently supported in the literature. Additionally, we demonstrate the
application of HIPPO to a challenging heat exchanger design problem, stressing
the real-world utility of our highly parallelisable approach to MOBO.

Machine Learning