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Tests Code style: black

smoot

Installation

ONERA version : pip install smoot

This version with last updates : git clone git@github.com:RobinGRAPIN/smoot.git

Necessary packages : pymoo,smt

Description

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.

modeli1 modeli2

activ

modeli12 modeli22

Utilisation

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

Article and presentation

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