A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty

• URL: http://arxiv.org/abs/2202.12848v1
• Date: Fri, 25 Feb 2022 17:45:26 GMT
• Title: A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertainty
• Authors: J.Qing, I. Couckuyt, T. Dhaene
• Abstract summary: In real-life applications like engineering design, the designer often wants to take multiple objectives as well as input uncertainty into account. We introduce a novel Bayesian optimization framework to efficiently perform multi-objective optimization considering input uncertainty.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian optimization is a popular tool for data-efficient optimization of expensive objective functions. In real-life applications like engineering design, the designer often wants to take multiple objectives as well as input uncertainty into account to find a set of robust solutions. While this is an active topic in single-objective Bayesian optimization, it is less investigated in the multi-objective case. We introduce a novel Bayesian optimization framework to efficiently perform multi-objective optimization considering input uncertainty. We propose a robust Gaussian Process model to infer the Bayes risk criterion to quantify robustness, and we develop a two-stage Bayesian optimization process to search for a robust Pareto frontier. The complete framework supports various distributions of the input uncertainty and takes full advantage of parallel computing. We demonstrate the effectiveness of the framework through numerical benchmarks.

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