  Techniques to reduce the energy burden of an industrial ecosystem often
require solving a multiobjective optimization problem. However, collecting
experimental data can often be either expensive or time-consuming. In such
cases, statistical methods can be helpful. This article proposes Pareto Front
(PF) and Pareto Set (PS) estimation methods using Bayesian Additive Regression
Trees (BART), which is a non-parametric model whose assumptions are typically
less restrictive than popular alternatives, such as Gaussian Processes (GPs).
These less restrictive assumptions allow BART to handle scenarios (e.g.
high-dimensional input spaces, nonsmooth responses, large datasets) that GPs
find difficult. The performance of our BART-based method is compared to a
GP-based method using analytic test functions, demonstrating convincing
advantages. Finally, our BART-based methodology is applied to a motivating
engineering problem. Supplementary materials, which include a theorem proof,
algorithms, and R code, for this article are available online.
