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    srMO-BO-3GP: A Sequential Regularized Multi-Objective Bayesian Optimization for Constrained Design Applications Using an Uncertain Pareto Classifier

    Source: Journal of Mechanical Design:;2021:;volume( 144 ):;issue: 003::page 31705-1
    Author:
    Tran, Anh
    ,
    Eldred, Mike
    ,
    McCann, Scott
    ,
    Wang, Yan
    DOI: 10.1115/1.4052445
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective BO formalism, called srMO-BO-3GP, to solve multi-objective optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GPs is assigned with a different task. The first GP is used to approximate a single-objective computed from the multi-objective definition, the second GP is used to learn the unknown constraints, and the third one is used to learn the uncertain Pareto frontier. At each iteration, a multi-objective augmented Tchebycheff function is adopted to convert multi-objective to single-objective, where the regularization with a regularized ridge term is also introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the convergence and diversity of the Pareto frontier by the acquisition function for exploitation and exploration. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.
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      srMO-BO-3GP: A Sequential Regularized Multi-Objective Bayesian Optimization for Constrained Design Applications Using an Uncertain Pareto Classifier

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    contributor authorTran, Anh
    contributor authorEldred, Mike
    contributor authorMcCann, Scott
    contributor authorWang, Yan
    date accessioned2022-05-08T08:25:48Z
    date available2022-05-08T08:25:48Z
    date copyright10/21/2021 12:00:00 AM
    date issued2021
    identifier issn1050-0472
    identifier othermd_144_3_031705.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4283916
    description abstractBayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective BO formalism, called srMO-BO-3GP, to solve multi-objective optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GPs is assigned with a different task. The first GP is used to approximate a single-objective computed from the multi-objective definition, the second GP is used to learn the unknown constraints, and the third one is used to learn the uncertain Pareto frontier. At each iteration, a multi-objective augmented Tchebycheff function is adopted to convert multi-objective to single-objective, where the regularization with a regularized ridge term is also introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the convergence and diversity of the Pareto frontier by the acquisition function for exploitation and exploration. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.
    publisherThe American Society of Mechanical Engineers (ASME)
    titlesrMO-BO-3GP: A Sequential Regularized Multi-Objective Bayesian Optimization for Constrained Design Applications Using an Uncertain Pareto Classifier
    typeJournal Paper
    journal volume144
    journal issue3
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4052445
    journal fristpage31705-1
    journal lastpage31705-11
    page11
    treeJournal of Mechanical Design:;2021:;volume( 144 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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