Efficient Global Optimization of Expensive Black-Box Functions View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1998-12

AUTHORS

Donald R. Jones, Matthias Schonlau, William J. Welch

ABSTRACT

In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome. More... »

PAGES

455-492

References to SciGraph publications

  • 1990-04. The origins of kriging in MATHEMATICAL GEOSCIENCES
  • 1992-06. A review of statistical models for global optimization in JOURNAL OF GLOBAL OPTIMIZATION
  • 1997-01. Bayesian Algorithms for One-Dimensional Global Optimization in JOURNAL OF GLOBAL OPTIMIZATION
  • 1994-06. Application of Bayesian approach to numerical methods of global and stochastic optimization in JOURNAL OF GLOBAL OPTIMIZATION
  • 1998. Optimization Using Surrogate Objectives on a Helicopter Test Example in COMPUTATIONAL METHODS FOR OPTIMAL DESIGN AND CONTROL
  • 1991-03. Bayesian methods in global optimization in JOURNAL OF GLOBAL OPTIMIZATION
  • 1998-02. A trust-region framework for managing the use of approximation models in optimization in STRUCTURAL OPTIMIZATION
  • 1996-12. Predicting Urban Ozone Levels and Trends with Semiparametric Modeling in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 1993-02. Multivariable spatial prediction in MATHEMATICAL GEOSCIENCES
  • 1995-12. αBB: A global optimization method for general constrained nonconvex problems in JOURNAL OF GLOBAL OPTIMIZATION
  • 1995. Stochastic Methods in HANDBOOK OF GLOBAL OPTIMIZATION
  • 1998. Evaluation of Injection Island GA Performance on Flywheel Design Optimisation in ADAPTIVE COMPUTING IN DESIGN AND MANUFACTURE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1023/a:1008306431147

    DOI

    http://dx.doi.org/10.1023/a:1008306431147

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1009040383


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