A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method
Abstract
Bayesian optimization (BO) is a low-cost global optimization tool for expensive black-box objective functions, where we learn from prior evaluated designs, update a posterior surrogate Gaussian process model, and select new designs for future evaluation using an acquisition function. This research focuses upon developing a BO model with multiple black-box objective functions. In the standard multi-objective (MO) optimization problem, the...
Paper Details
Title
A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method
Published Date
Aug 11, 2021
Journal
Volume
144
Issue
1
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