Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima, and the failure of the analysis to return a value to the optimizer. One possible remedy to alleviate these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem (SP) considered at each search step of the mesh adaptive direct search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on 20 analytical benchmark problems and two simulation-based multidisciplinary design optimization (MDO) problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm. [DOI: 10.1115/1.4028756]


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Statistical surrogate formulations for simulation-based design optimization



    Erschienen in:

    Erscheinungsdatum :

    2015




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch



    Klassifikation :

    BKL:    50.15 Konstruktionslehre / 52.15 Maschinenelemente, Maschinenbaugruppen / 52.20 Antriebstechnik, Getriebelehre
    Lokalklassifikation TIB:    770/5315/5330



    Formulations for Surrogate-Based Optimization Under Uncertainty

    Eldred, Michael / Giunta, Anthony / Wojtkiewicz, Steven et al. | AIAA | 2002


    AIAA-2002-5585 Formulations for Surrogate-Based Optimization Under Uncertainty

    Eldred, M. / Giunta, A. / Wojtkiewicz, S. et al. | British Library Conference Proceedings | 2002


    Surrogate Based Optimization for Multidisciplinary Design

    Boussouf, Loïc | SAE Technical Papers | 2011



    Formulations for Surrogate-Based Optimization with Data Fit, Multifidelity, and Reduced-Order Models

    Eldred, M. / Dunlavy, D. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2006