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Recent advances in surrogate-based optimization

By Alexander I.J. Forrester and Andy J. Keane

Abstract

The evaluation of aerospace designs is synonymous with the use of long running computationally intensive simulations. This fuels the desire to harness the efficiency of surrogate-based methods in aerospace design optimization. Recent advances in surrogate-based design methodology bring the promise of efficient global optimization closer to reality. We review the present state of the art of constructing surrogate models and their use in optimization strategies. We make extensive use of pictorial examples and, since no method is truly universal, give guidance as to each method's strengths and weaknesses

Topics: TL, QA76
Year: 2009
OAI identifier: oai:eprints.soton.ac.uk:65935
Provided by: e-Prints Soton

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  1. (1978). A ¯rst course in numerical analysis.
  2. (2006). A bayesian approach to the design and analysis of fractional experiments.
  3. (2000). A Bayesian committee machine.
  4. (1980). A comparison of algorithms for constructing exact D-optimal designs.
  5. (1979). A comparison of three methods for selecting values of input variables in the anslysis of output from a computer code.
  6. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II.
  7. (2003). A knowledge-based approach to response surface modelling in multi¯delity optimization.
  8. (2007). A non-stationary covariance-based kriging method for metamodelling in engineering design.
  9. (2002). A parallel updating scheme for approximating and optimizing high ¯delity computer simulations.
  10. (2004). A parallel updating scheme for approximating and optimizing high ¯delity computer simulations. Structural and multidisciplinary optimization,
  11. (2001). A radial basis function method for global optimization.
  12. (1951). A statistical approach to some basic mine valuation problems on the Witwatersrand.
  13. (2001). A taxonomy of global optimization methods based on response surfaces.
  14. (1998). A trust region framework for managing the use of approximation models in optimization.
  15. (2004). A tutorial on support vector regression.
  16. (2000). An introduction to the adjoint approach to design.
  17. (1995). An overview of evolutionary algorithms in mutliobjective optimization.
  18. (2001). Approximation and model management in aerodynamic optimization with variable-¯delity models.
  19. (2006). Automated multi-stage geometry parameterization of internal °uid °ow applications.
  20. (1997). Bayesian Gaussian Processes for Regression and Classi¯cation. Dphil dissertation,
  21. (2008). Blind kriging: A new method for developing metamodels.
  22. (1991). Combining global and local approsimations.
  23. (2008). Comparing error estimation measures for polynomial and kriging approximation of noise-free functions. Structural and Multidisciplinary Optimization, to appear,
  24. (2005). Computational Approaches to Aerospace Design: the Pursuit of Excellence.
  25. (1997). Computer Experiments and Global Optimization.
  26. (2008). Design and analysis of computer experiments in multidisciplinary design optimization: a review of how we have come { or not.
  27. (2003). Design and Analysis of Computer Experiments.
  28. (2005). Design optimization and stochastic analysis based on the moving least squares method. 6th World congress of structural and multidisciplinary optimization,
  29. (2004). Design search and optimisation using radial basis functions with regression capabilities. In
  30. (2002). Developments of an e±cient global optimal design technique a combined approach of mls and sa algorithm.
  31. (1998). E±cient global optimisation of expensive black-box functions.
  32. (2004). E±cient Global Optimisation Using Expensive CFD Simulations.
  33. (2001). E±cient Pareto frontier exploration using surrogate approximations.
  34. (1987). Empirical Model Building and Response Surfaces.
  35. (2008). Engineering Design via Surrogate Modelling: A Practical Guide.
  36. (2003). Enhancements to Global Optimisation.
  37. (2007). Ensemble of surrogates.
  38. (1983). Estimating the error rate of a prediction rule: improvement on cross-validation.
  39. (2000). Evaluating Derivatives: Principles and Techniques of Algorithmic Di®erentiation. Frontiers in Applied Mathematics.
  40. (2000). Experiments: Planning, Analysis, and Paramter Design Optimization.
  41. (2005). Exploiting hessian matrix and trustregion algorithm in hyperparameters estimation of gaussian process.
  42. (1995). Exploratory designs for computational experiments.
  43. (1991). Factorial sampling plans for preliminary computational experiments.
  44. (2008). Global optimization of deceptive functions with sparse sampling.
  45. (1996). Global optimization using response surfaces.
  46. (2004). Gradient-enhanced response surface building. Structural and Multidisciplinary Optimization,
  47. (2008). Kriging hyperparameter tuning strategies.
  48. (1998). Learning From Data { Concepts, Theory, and Methods.
  49. (2002). Learning with Kernals. MIT,
  50. (1990). Minimax and maximin distance designs.
  51. (2007). Multi-¯delity optimization via surrogate modelling.
  52. Multi-objective optimization using surrogates.
  53. (1995). Multicriteria Optimization and Engineering: Theory and Practice. Chapman and Hall,
  54. (2005). Multiobjective optimization on a budget of 250 evaluations.
  55. (1988). Multivariate functional interpolation and adaptive networks.
  56. (1983). Numerical methods for unconstrained optimization and nonlinear equations.
  57. (1935). On least squares and linear combinations of observations.
  58. (2005). On the design of optimization strategies based on global response surface approximation models.
  59. (1979). On the systematic search in a hypercube.
  60. (1982). Optimal Engineering Design: Principles and Applications.
  61. (2006). Optimization using surrogate models and partially converged computational °uid dynamics simulations.
  62. (2006). Optimization with missing data.
  63. (1989). Physically based sensitivity derivatives for structural analysis programs.
  64. (1992). Precipitation estimation in mountainous terrai using multivariate geostatistics. part II: Isohyetal maps.
  65. (2000). Predicting the output from copmlex computer code when fast approximations are available.
  66. (1963). Principles of geostatistics.
  67. (1995). Regularisation in the selection of RBF centres.
  68. (1990). Regularization algorithms for learning that are equivalent to multilayer networks.
  69. (1995). Response Surface Methodology: Process and Product Optimization Using Designed Experiments.
  70. (2004). Second-order corrections for surrogate-based optimization with model hierachies.
  71. (2004). Space mapping: the state of the art.
  72. (2006). Statistical improvement criteria for use in multiobjective design optimization.
  73. (1998). Statistical Learning Theory.
  74. (1993). Statistics for Spatial Data. Probability and mathematical statistics. Wiley, revised edition,
  75. (1981). Surfaces generated by moving least squares methods.
  76. (1998). The approximation power of moving least-squares.
  77. (2006). The correct kriging variance estimated by bootstrapping.
  78. (2001). The Elements of Statistical Learning.
  79. (1995). The Nature of Statistical Learning Theory.
  80. (2003). Wing optimization using design of experiment, response surface, and data fusion methods.

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