This Python code is for multi/single-objective Bayesian optimization (MBO/SBO) with/without constraint handling.
This code is released under the MIT License, see LICENSE.txt.
You can start with "tutorial_multi-objective_Bayesian_optimization.ipynb", "tutorial_single-objective_Bayesian_optimization.ipynb", or "main.py".
Detailed usage is written in "tutorial_*.ipynb".
MBO part is based on MBO-EPBII-SRVA and MBO-EPBII published in the following articles:
- N. Namura, "Surrogate-Assisted Reference Vector Adaptation to Various Pareto Front Shapes for Many-Objective Bayesian Optimization," IEEE Congress on Evolutionary Computation, Krakow, Poland, pp.901-908, 2021.
- N. Namura, K. Shimoyama, and S. Obayashi, "Expected Improvement of Penalty-based Boundary Intersection for Expensive Multiobjective Optimization," IEEE Transactions on Evolutionary Computation, vol. 21, no. 6, pp. 898-913, 2017.
Please cite the article(s) if you use the MBO code.
- numpy
- pandas
- scipy
- matplotlib
- scikit-learn
- pymoo
- pyDOE2
- optproblems
- diversipy
Nobuo Namura (nobuo.namura.gp@gmail.com)