Abstract
SHEBO (surrogate optimization of problems with hidden constraints and expensive black-box objectives) is an efficient optimization algorithm that employs surrogate models to solve computationally expensive black-box simulation optimization problems that have hidden constraints. Hidden constraints are encountered when the objective function evaluation does not return a value for a parameter vector. These constraints are often encountered in optimization problems in which the objective function is computed by a black-box simulation code. SHEBO uses a combination of local and global search strategies together with an evaluability prediction function and a dynamically adjusted evaluability threshold to iteratively select new sample points.
- Developers:
-
Mueller, Juliane [1]
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Release Date:
- 2019-10-14
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 31296
- Site Accession Number:
- 2020-002
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Mueller, Juliane.
Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints (SHEBO) v1.0.
Computer Software.
https://bitbucket.org/julianem/shebo-hidden-constraint-optimization/src/master.
USDOE.
14 Oct. 2019.
Web.
doi:10.11578/dc.20191016.1.
Mueller, Juliane.
(2019, October 14).
Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints (SHEBO) v1.0.
[Computer software].
https://bitbucket.org/julianem/shebo-hidden-constraint-optimization/src/master.
https://doi.org/10.11578/dc.20191016.1.
Mueller, Juliane.
"Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints (SHEBO) v1.0." Computer software.
October 14, 2019.
https://bitbucket.org/julianem/shebo-hidden-constraint-optimization/src/master.
https://doi.org/10.11578/dc.20191016.1.
@misc{
doecode_31296,
title = {Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints (SHEBO) v1.0},
author = {Mueller, Juliane},
abstractNote = {SHEBO (surrogate optimization of problems with hidden constraints and expensive black-box objectives) is an efficient optimization algorithm that employs surrogate models to solve computationally expensive black-box simulation optimization problems that have hidden constraints. Hidden constraints are encountered when the objective function evaluation does not return a value for a parameter vector. These constraints are often encountered in optimization problems in which the objective function is computed by a black-box simulation code. SHEBO uses a combination of local and global search strategies together with an evaluability prediction function and a dynamically adjusted evaluability threshold to iteratively select new sample points.},
doi = {10.11578/dc.20191016.1},
url = {https://doi.org/10.11578/dc.20191016.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20191016.1}},
year = {2019},
month = {oct}
}