Dakota Reference Manual
Version 6.2
LargeScale Engineering Optimization and Uncertainty Analysis

Global Surrogate Based Optimization
This keyword is related to the topics:
Alias: none
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Required (Choose One)  Group 1  method_pointer  Pointer to submethod to apply to surrogate  
method_name  Specify submethod by name  
Required  model_pointer  Identifier for model block to be used by a method  
Optional  replace_points  (Recommended) Replace points in the surrogate training set, instead of appending 
The surrogate_based_global
specification must identify:
surrogate_based_global
works in an iterative scheme where optimization is performed on a global surrogate using the same bounds during each iteration.
In this way, the optimization acts on a more accurate surrogate during each iteration, presumably driving to optimality quickly.
Method Independent Controls
Notes
We have some cautionary notes before using the surrogatebased global method:
max_iterations
to 1 and will allow one to get a sense of what surrogate types are the most accurate to use for the problem. In surrogatebased optimization (SBO) and surrogatebased nonlinear least squares (SBNLS), minimization occurs using a set of one or more approximations, defined from a surrogate model, that are built and periodically updated using data from a "truth" model. The surrogate model can be a global data fit (e.g., regression or interpolation of data generated from a design of computer experiments), a multipoint approximation, a local Taylor Series expansion, or a model hierarchy approximation (e.g., a lowfidelity simulation model), whereas the truth model involves a highfidelity simulation model. The goals of surrogatebased methods are to reduce the total number of truth model simulations and, in the case of global data fit surrogates, to smooth noisy data with an easily navigated analytic function.
It was originally designed for MOGA (a multiobjective genetic algorithm). Since genetic algorithms often need thousands or tens of thousands of points to produce optimal or nearoptimal solutions, the use of surrogates can be helpful for reducing the truth model evaluations. Instead of creating one set of surrogates for the individual objectives and running the optimization algorithm on the surrogate once, the idea is to select points along the (surrogate) Pareto frontier, which can be used to supplement the existing points.
In this way, one does not need to use many points initially to get a very accurate surrogate. The surrogate becomes more accurate as the iterations progress.
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