Bayesian Optimization and Multi-fidelity Approaches

A multi-fidelity constrained Bayesian optimization toolkit

Key Features

The BOMA package offers a collection of surrogate-based optimization frameworks. The following frameworks are available:

Framework	Inequality Constraints	Equality Constraints	Multi-fidelity	As seen in
SEGO	Yes	Yes	No	https://doi.org/10.1080/03052150211751
MFSEGO	Yes	Yes	Yes	https://doi.org/10.2514/6.2019-3236
VF-EI	Yes	No	Yes	https://doi.org/10.1007/s00158-018-1971-x
VF-PI	Yes	No	Yes	https://doi.org/10.1007/s00158-020-02646-9

Getting Started

Prerequisites

BOMA requires the following Python package to be installed:

1. Numpy pip install numpy
2. SciPy pip install scipy
3. SMT pip install smt

Installation

1. Clone the repo

git clone https://github.com/oihanc/boma.git

2. Install BOMA to your Python environment. In the root directory, type:

pip install -e .

Usage

See usage examples in the boma/examples/ directory.

Please cite us when using BOMA

If you are using BOMA in your work, please cite the following paper.

Oihan Cordelier, Youssef Diouane, Nathalie Bartoli and Eric Laurendeau. "Multi-Fidelity Constrained Bayesian Optimization with Application to Aircraft Wing Design," AIAA 2025-3474. AIAA AVIATION FORUM AND ASCEND 2025. July 2025.