Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
 All Pages
sbo


Use the surrogate based optimization method

Specification

Alias: none

Argument(s): none

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional gaussian_process

Gaussian Process surrogate model

Optional use_derivatives

Use derivative data to construct surrogate models

Optional import_build_points_file

File containing points you wish to use to build a surrogate

Optional export_approx_points_file

Output file for evaluations of a surrogate model

Description

A surrogate-based optimization method will be used. The surrogate employed in sbo is a Gaussian process surrogate.

The main difference between ego and the sbo approach is the objective function being optimized. ego relies on an expected improvement function, while in sbo, the optimization proceeds using an evolutionary algorithm (coliny_ea) on the Gaussian process surrogate: it is a standard surrogate-based optimization. Also note that the sbo option can support optimization over discrete variables (the discrete variables are relaxed) while ego cannot.

This is not the same as surrogate_based_global.