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Saturday, May 16, 2020 | History

2 edition of A rigorous framework for optimization of expensive functions by surrogates found in the catalog.

A rigorous framework for optimization of expensive functions by surrogates

A rigorous framework for optimization of expensive functions by surrogates

  • 138 Want to read
  • 7 Currently reading

Published by Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, National Technical Information Service, distributor in Hampton, VA, Springfield, VA .
Written in English

    Subjects:
  • Design analysis.,
  • Rotary wings.,
  • Optimization.,
  • Computer design.

  • Edition Notes

    StatementAndrew J. Booker ... [et al.].
    SeriesICASE report -- no. 98-47., [NASA contractor report] -- NASA/CR-1998-208735., NASA contractor report -- NASA CR-208735.
    ContributionsBooker, Andrew J., Institute for Computer Applications in Science and Engineering.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL15542626M

    Downloadable (with restrictions)! We present the AQUARS (A QUAsi-multistart Response Surface) framework for finding the global minimum of a computationally expensive black-box function subject to bound constraints. In a traditional multistart approach, the local search method is blind to the trajectories of the previous local searches. Hence, the algorithm might find the same local minima even. In engineering design, space mapping aligns a very fast coarse model with the expensive-to-compute fine model so as to avoid direct expensive optimization of the fine model. The alignment can be done either off-line (model enhancement) or on-the-fly with surrogate updates (e.g., .

    Dennis JE, Jr., Frank PD, Serafini DB, Torczon V, Trosset MW. A rigorous framework for optimization of expensive functions by surrogates. Structural optimization. /02/01 ;17(1) 7. Davis E, Ierapetritou M. A kriging method for the solution of nonlinear programs with black-box functions. AIChE Journal. ;53(8) 8. Huang.   This paper addresses the solution of bound-constrained optimization problems using algorithms that require only the availability of objective function values but no derivative information. We refer to these algorithms as derivative-free algorithms. Fueled by a growing number of applications in science and engineering, the development of derivative-free optimization algorithms has long been Cited by:

      These metamodels are initially developed as “surrogates” of the expensive simulation process in order to improve the overall computation efficiency. They are then found to be a valuable tool to support a wide scope of activities in modern engineering design, especially design by: Chapter 7 Optimization. In this chapter, the goal is to demonstrate how Gaussian process (GP) surrogate modeling can assist in optimizing a blackbox objective function. That is, a function about which one knows little – one opaque to the optimizer – and that can only be probed through expensive .


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A rigorous framework for optimization of expensive functions by surrogates Download PDF EPUB FB2

To the objective function and managing the use of these approximations as surrogates for optimization. The result is to obtain convergence to a minimizer of an expensive objective function subject to simple constraints.

The approach is widely applicable because it does not require, or even explicitly approximate, derivatives of the by: 2 A Rigorous Framework for Optimization Using Sur-rogates In this section we describe SMF, our framework for managing surrogate objective functions to facilitate the optimization of expensive computer simulations.

The framework is sufficiently general to accommodate surrogates that are (1) simplified physical models of the expensive. The framework is su ciently general to accommodate surrogates that are (1) simpli ed physical models of the expensive simulation; (2) approximations of the expensive simulation, constructed by interpolating or smoothing known values of the objective; or (3) model-approximation hybrids.

This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for optimization.

The result is to obtain convergence to a minimizer of an expensive objective function. Multi-fidelity surrogate (MFS) method is very promising for the optimization of complex problems.

The optimization capability of MFS can be improved by infilling samples in the optimization process. A rigorous framework for optimization of expensive functions by surrogates Author: Andrew J Booker ; Institute for Computer Applications in Science and Engineering.

The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which direct application of traditional optimization approaches is not practical.

This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for by: Thus, hypothesis testing is converted into optimization of a response surface. First, an objective function is evaluated at a few points.

Then, the hypothetical (surrogate) surface landscape is created from an ensemble of approximations of the objective : Jiří Pospíchal. The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which design application of traditional optimization approaches is not practical.

This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for.

A Surrogate Management Framework Using Rigorous Trust-Regions Steps A rigorous framework for optimization of expensive functions.

is a major challenge for optimization. This book explains. A rigorous framework for optimization of expensive functions by surrogates, Structural Optimization 17 (1): 1– Büche, D., Schraudolph, N.N. and Koumoutsakos, P. Accelerating evolutionary algorithms with Gaussian process fitness function models, IEEE Transactions on Systems, Man, and Cybernetics C 35 (2): –Author: Yoel Tenne.

We introduce MISO, the mixed-integer surrogate optimization framework. MISO aims at solving computationally expensive black-box optimization problems with mixed-integer variables. This type of optimization problem is encountered in many applications for which time consuming simulation codes must be run in order to obtain an objective function by:   Simulation-Based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions.

A rigorous framework for optimization of expensive functions by surrogates. Struct. by: 5. An enhanced surrogate assisted framework, based on Probability of Improvement (PI) method, is proposed in this paper. We made some modifications to the original PI approach to enhance the performance of the modeling and optimization framework, leading to fewer rigorous simulations to find the optimal solution without loss of accuracy.

A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions.

A rigorous framework for optimization of expensive functions by surrogates. Structural Optimization, 17(1), Cited by: We introduce MISO, the mixed-integer surrogate optimization framework. MISO aims at solving computationally expensive black-box optimization problems with mixed-integer : Juliane Mueller.

This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for optimization. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.

() Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection. Journal of Global Optimization() A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization by:   Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates “ A Rigorous Framework for Optimization of Expensive Functions by Surrogates,” Structural Optimization, Vol of the European Union’s Seventh Framework Programme FP7// under grant agreement no.

PIEF-GA Received Cited by:. A common approach to tackling such problems is the implementation of Bayesian global optimization techniques. However, these techniques often rely on surrogate modeling strategies that endow the approximation of the underlying expensive function with nonexistent : Benson Isaac, Douglas Allaire.The great computational burden caused by complicated and unknown analysis restricts the use of simulation-based optimization.

In order to mitigate this challenge, surrogate-based global optimization methods have gained popularity for their capability in handling computationally expensive functions.

This paper surveys the fundamental issues that arise in Surrogate-based Global Optimization Author: Pengcheng Ye.We present different formulations for the surrogate problem (SP) considered at each search step of the mesh adaptive direct search (MADS) algorithm using a surrogate management framework.

The proposed formulations are tested on 20 analytical benchmark problems and two simulation-based multidisciplinary design optimization (MDO) by: