Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. (arXiv:2109.10964v1 [cs.LG])

The ability to optimize multiple competing objective functions with high
sample efficiency is imperative in many applied problems across science and
industry. Multi-objective Bayesian optimization (BO) achieves strong empirical
performance on such problems, but even with recent methodological advances, it
has been restricted to simple, low-dimensional domains. Most existing BO
methods exhibit poor performance on search spaces with more than a few dozen
parameters. In this work we propose MORBO, a method for multi-objective
Bayesian optimization over high-dimensional search spaces. MORBO performs local
Bayesian optimization within multiple trust regions simultaneously, allowing it
to explore and identify diverse solutions even when the objective functions are
difficult to model globally. We show that MORBO significantly advances the
state-of-the-art in sample-efficiency for several high-dimensional synthetic
and real-world multi-objective problems, including a vehicle design problem
with 222 parameters, demonstrating that MORBO is a practical approach for
challenging and important problems that were previously out of reach for BO



Related post