13 research outputs found

    Optimization Under Uncertainty Using the Generalized Inverse Distribution Function

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    A framework for robust optimization under uncertainty based on the use of the generalized inverse distribution function (GIDF), also called quantile function, is here proposed. Compared to more classical approaches that rely on the usage of statistical moments as deterministic attributes that define the objectives of the optimization process, the inverse cumulative distribution function allows for the use of all the possible information available in the probabilistic domain. Furthermore, the use of a quantile based approach leads naturally to a multi-objective methodology which allows an a-posteriori selection of the candidate design based on risk/opportunity criteria defined by the designer. Finally, the error on the estimation of the objectives due to the resolution of the GIDF will be proven to be quantifiableComment: 20 pages, 25 figure

    Le projet ANR/RNTL/OMD (Optimisation Multi-Disciplinaire)

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    info:eu-repo/semantics/publishe

    Metamodel-assisted optimization based on multiple kernel regression for mixed variables

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    While studies in metamodel-assisted optimization predominantly involve continuous variables, this paper explores the additional presence of categorical data, representing for instance the choice of a material or the type of connection. The common approach consisting in mapping them onto integers might lead to inconsistencies or poor approximation results. Therefore, an investigation of the best coding is necessary; however, to build accurate and flexible metamodels, a special attention should also be devoted to the treatment of the distinct nature of the variables involved. Consequently, a multiple kernel regression methodology is proposed, since it allows for selecting separate kernel functions with respect to the variable type. The validation of the advocated approach is carried out on six analytical benchmark test cases and on the structural responses of a rigid frame. In all cases, better performances are obtained by multiple kernel regression with respect to its single kernel counterpart, thereby demonstrating the potential offered by this approach, especially in combination with dummy coding. Finally, multi-objective surrogate-based optimization is performed on the rigid frame example, firstly to illustrate the benefit of dealing with mixed variables for structural design, then to show the reduction in terms of finite element simulations obtained thanks to the metamodels. © 2014 Springer-Verlag Berlin Heidelberg

    The Similarity Design of Heavy Forging Robot Grippers

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    Multidisciplinary and multiple operating points shape optimization of three-dimensional compressor blades

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    http://dx.doi.org/10.1007/s00158-006-0033-yinfo:eu-repo/semantics/publishe
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