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Multi-information source constrained Bayesian optimization

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Abstract

Design decisions for complex systems often can be made or informed by a variety of information sources. When optimizing such a system, the evaluation of a quantity of interest is typically required at many different input configurations. For systems with expensive to evaluate available information sources, the optimization task can potentially be computationally prohibitive using traditional techniques. This paper presents an information-economic approach to the constrained optimization of a system with multiple available information sources. The approach rigorously quantifies the correlation between the discrepancies of different information sources, which enables the overcoming of information source bias. All information is exploited efficiently by fusing newly acquired information with that previously evaluated. Independent decision-makings are achieved by developing a two-step look-ahead utility policy and an information gain policy for objective function and constraints respectively. The approach is demonstrated on a one-dimensional example test problem and an aerodynamic design problem, where it is shown to perform well in comparison to traditional multi-information source techniques.

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Acknowledgments

This work was supported by the AFOSR MURI on multi-information sources of multi-physics systems under Award Number FA9550-15-1-0038, program manager, Dr. Fariba Fahroo and by the National Science Foundation under grant no. CMMI-1663130. Opinions expressed in this paper are of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Texas A&M University, College Station, TX, 77843, USA

    Seyede Fatemeh Ghoreishi & Douglas Allaire

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  1. Seyede Fatemeh Ghoreishi
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  2. Douglas Allaire
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Correspondence to Seyede Fatemeh Ghoreishi.

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Ghoreishi, S.F., Allaire, D. Multi-information source constrained Bayesian optimization. Struct Multidisc Optim 59, 977–991 (2019). https://doi.org/10.1007/s00158-018-2115-z

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