Sequential improvement for robust optimization using an uncertainty measure for radial basis functions

    Research output: Contribution to journalArticleAcademicpeer-review

    20 Citations (Scopus)
    86 Downloads (Pure)

    Abstract

    The performance of the sequential metamodel based optimization procedure depends strongly on the chosen building blocks for the algorithm, such as the used metamodeling method and sequential improvement criterion. In this study, the effect of these choices on the efficiency of the robust optimization procedure is investigated. A novel sequential improvement criterion for robust optimization is proposed, as well as an improved implementation of radial basis function interpolation suitable for sequential optimization. The leave-one-out cross-validation measure is used to estimate the uncertainty of the radial basis function metamodel. The metamodeling methods and sequential improvement criteria are compared, based on a test with Gaussian random fields as well as on the optimization of a strip bending process with five design variables and two noise variables. For this process, better results are obtained in the runs with the novel sequential improvement criterion as well as with the novel radial basis function implementation, compared to the runs with conventional sequential improvement criteria and kriging interpolation.
    Original languageEnglish
    Pages (from-to)1345-1363
    Number of pages19
    JournalStructural and multidisciplinary optimization
    Volume55
    Issue number4
    DOIs
    Publication statusPublished - Apr 2017

    Keywords

    • IR-102280
    • METIS-319101

    Fingerprint Dive into the research topics of 'Sequential improvement for robust optimization using an uncertainty measure for radial basis functions'. Together they form a unique fingerprint.

    Cite this