Surrogate-based 0-th Order Global Optimization for black-box problems.
| Optimization algorithm | Description | Tags |
|---|---|---|
surrogate_optimization() |
Minimize a scalar function using a surrogate and an acquisition function based on (Björkman & Holmström; 2000) and (Müller; 2016). | mixed-integer |
multistart_msrs() |
Multistart Local Metric Stochastic Response Surface (LMSRS) (Regis & Shoemaker; 2007). Applies a derivative-free local search algorithm to obtain new samples. Restarts the surrogate model with new design points whenever the local search has converged. | multi-start, RBF |
dycors() |
Dynamic Coordinate Search (DYCORS) (Regis & Shoemaker; 2012). Acquisition cycles between global and local search. Uses the DDS search from (Tolson & Shoemaker; 2007) to generate pools of candidates. | mixed-integer, RBF |
cptv() |
Minimize a scalar function using rounds of coordinate perturbation (CP) and target value (TV) acquisition functions (Müller; 2016). Derivative-free local search is used to improve a prospective global minimum | mixed-integer, RBF |
socemo() |
Surrogate-based optimization of computationally expensive multiobjective problems (SOCEMO) (Müller; 2017a). | multi-objective, mixed-integer, RBF |
gosac() |
Global optimization with surrogate approximation of constraints (GOSAC) (Müller; 2017b). | mixed-integer, black-box-constraint, RBF |
bayesian_optimization() |
Bayesian optimization with dispersion-enhanced expected improvement acquisition (Müller; 2024). | GP, batch |
shebo() |
Surrogate optimization of problems with hidden constraints and expensive black-box objectives (SHEBO) (Müller & Day; 2019). | hidden-constraint, expensive-objective, RBF |
fsapso() |
Fast Surrogate Assisted Particle Swarm Optimization (Li et al.; 2020). | RBF, PSO |
| Acquisition function | Description |
|---|---|
WeightedAcquisition |
Weighted acquisition function based on the predicted value and distance to the nearest sample (Regis & Shoemaker; 2012). Used in multistart_msrs(), dycors(), and in the CP step from cptv(). It uses average values for the multi-objective scenario (Müller; 2017a). |
TargetValueAcquisition |
Target value acquisition based from (Gutmann; 2001). Used in the TV step from cptv(). Cycles through target values as in (Björkman & Holmström; 2000). For batched acquisition, uses the strategy from (Müller; 2016) to avoid duplicates. |
MinimizeSurrogate |
Sample at the local minimum of the surrogate model (Müller; 2016). The original method, Multi-Level Single-Linkage (MLSL), is described in (Rinnooy Kan & Timmer; 1987). |
MaximizeEI |
Maximize the expected improvement acquisition function for Gaussian processes. Use the dispersion-enhanced strategy from (Müller; 2024) for batch sampling. |
ParetoFront |
Sample at the Pareto front of the multi-objective surrogate model to fill gaps in the surface (Müller; 2017a). |
MinimizeMOSurrogate |
Obtain pareto-optimal sample points for the multi-objective surrogate model (Müller; 2017a). |
GosacSample |
Minimize a function with surrogate constraints to obtain a single new sample point (Müller; 2017b). |
TransitionSearch |
Weighted acquisition function that balances local and global search using a weighted score. Filters candidate points using evaluability surrogate. (Müller & Day; 2019). |
MaximizeDistance |
Maximizes the minimum distance to the set of current points. Used in shebo() and as a fallback in EndPointsParetoFront and GosacSample (Müller & Day; 2019). |
Use PyPI to install this package:
pip install soogoSee other installation methods below.
The binaries for the latest version are available at https://github.com/NREL/soogo/releases/latest. They can be installed through standard installation, e.g.,
using pip (https://pip.pypa.io/en/stable/cli/pip_install/):
pip install git+https://github.com/NREL/soogo.git#egg=soogoThis package contains a pyproject.toml with the list of requirements and dependencies (More about pyproject.toml at https://packaging.python.org/en/latest/specifications/pyproject-toml/). With the source downloaded to your local machine, use pip install [soogo/source/directory].
This project is configured to use the package manager pdm. With pdm installed, run pdm install at the root of this repository to install the dependencies. The file pyproject.toml has the list of dependencies and configurations for the project.
This project uses Sphinx to generate the documentation. The latest documentation is available at https://nrel.github.io/soogo. To generate the documentation locally, run make html in the docs directory. The homepage of the documentation will then be found at docs/_build/html/index.html.
This project uses pytest to run the tests. To run the tests, run pytest at the root of this repository. Run pytest --help to see the available options.
Please, read the contributing guidelines before contributing to this project.
This project is licensed under the GPL-3.0 License. See the LICENSE file for details.
NREL Software Record number: SWR-24-57