Multiobjective optimization is often a difficult task owing to the need to balance competing objectives. A typical approach to handling this is to estimate a Pareto frontier in objective space by identifying nondominated points. This task is typically computationally demanding owing to the need to incorporate information of high enough fidelity to be trusted in design and decision-making processes. In this work, we present a multi-information source framework for enabling efficient multiobjective optimization. The framework allows for the exploitation of all available information and considers both potential improvement and cost. The framework includes ingredients of model fusion, expected hypervolume improvement, and intermediate Gaussian process surrogates. The approach is demonstrated on a test problem and an aerostructural wing design problem.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bayesian Optimization of Multiobjective Functions Using Multiple Information Sources


    Beteiligte:

    Erschienen in:

    AIAA Journal ; 59 , 6 ; 1964-1974


    Erscheinungsdatum :

    2021-03-25


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    EFFICIENT MULTI-INFORMATION SOURCE MULTIOBJECTIVE BAYESIAN OPTIMIZATION

    Khatamsaz, Danial / Peddareddygari, Lalith / Friedman, Sam et al. | TIBKAT | 2020


    Efficient Multi-Information Source Multiobjective Bayesian Optimization

    Khatamsaz, Danial / Peddareddygari, Lalith / Friedman, Sam et al. | AIAA | 2020


    Bayesian Preference Elicitation for Multiobjective Engineering Design Optimization

    Lepird, John R. / Owen, Michael P. / Kochenderfer, Mykel J. | AIAA | 2015


    Multiobjective Crashworthiness Optimization with Radial Basis Functions

    Fang, Hongbing / Rais-Rohani, Masoud / Horstemeyer, Mark | AIAA | 2004