ONERA version :
pip install smoot
This version with last updates :
git clone git@github.com:RobinGRAPIN/smoot.git
Necessary packages : pymoo,smt
This surrogate based multi-objective Bayesian optimizer has been created to see the performance of the WB2S criterion adapted to multi-objective problems.
Given a black box function f : x -> y with bolds characters as vectors, smoot will give an accurate approximation of the optima with few calls of f.
Look at the Jupyter notebook in the tutorial folder.
You will learn how to use implemented the functionnalities and options such as :
- The choice of the infill criterion
- The method to manage the constraints
For additional questions, contact: robingrapin@orange.fr
This repository is a first throw of code to then update SEGO to handle multi-objective problems. This led to an AIAA article accepted, accessible at this adress: https://www.overleaf.com/read/fndwdktdgkkk The presentation of the work I made for this project is available here: https://1drv.ms/p/s!Am_qRb-KdCm2gd8rlKU1_3CD13wKWw?e=KEgokt