  Many real world scientific and industrial applications require optimizing
multiple competing black-box objectives. When the objectives are
expensive-to-evaluate, multi-objective Bayesian optimization (BO) is a popular
approach because of its high sample efficiency. However, even with recent
methodological advances, most existing multi-objective BO methods perform
poorly on search spaces with more than a few dozen parameters and rely on
global surrogate models that scale cubically with the number of observations.
In this work we propose MORBO, a scalable method for multi-objective BO over
high-dimensional search spaces. MORBO identifies diverse globally optimal
solutions by performing BO in multiple local regions of the design space in
parallel using a coordinated strategy. We show that MORBO significantly
advances the state-of-the-art in sample efficiency for several high-dimensional
synthetic problems and real world applications, including an optical display
design problem and a vehicle design problem with 146 and 222 parameters,
respectively. On these problems, where existing BO algorithms fail to scale and
perform well, MORBO provides practitioners with order-of-magnitude improvements
in sample efficiency over the current approach.
