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  • Parallel triangulated partitioning for black box optimization

    Author(s)
    Wu, Yong
    Ozdamar, Linet
    Kumar, Arun
    Griffith University Author(s)
    Wu, Yong
    Year published
    2006
    Metadata
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    Abstract
    We propose a parallel triangulation based partitioning algorithm (TRIOPT) for solving low dimensional bound-constrained black box global optimization problems. Black box optimization problems are important in engineering design where restricted numbers of input-output pairs are provided as data. Optimization is carried out over sparse data in the absence of a formal mathematical relationship among inputs and outputs. In such settings, function evaluations become expensive, because system performance assessment might be conducted via simulation studies or physical experiments. Thus, the optimal solution should be found ...
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    We propose a parallel triangulation based partitioning algorithm (TRIOPT) for solving low dimensional bound-constrained black box global optimization problems. Black box optimization problems are important in engineering design where restricted numbers of input-output pairs are provided as data. Optimization is carried out over sparse data in the absence of a formal mathematical relationship among inputs and outputs. In such settings, function evaluations become expensive, because system performance assessment might be conducted via simulation studies or physical experiments. Thus, the optimal solution should be found in a minimal number of function evaluations. In TRIOPT, input-output pairs are treated as samples located in the search domain and search space coverage is obtained over these samples by triangulation. This produces an initial partition of the domain. Thereafter, each simplex is assessed for re-partitioning in parallel. In this assessment, performance values at the vertices are transformed and mapped to [0,1] interval using a non-linear transformation function with dynamic parameters. Transformed values are then aggregated into a group measure
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    Book Title
    Global Optimization
    DOI
    https://doi.org/10.1007/0-387-30927-6_20
    Subject
    Optimisation
    Publication URI
    http://hdl.handle.net/10072/36196
    Collection
    • Book chapters

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