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Surrogate based multi-objective optimization tool

Project description

Tests Code style: black



pip install smoot

Required 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.

modeli1 modeli2


modeli12 modeli22


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:

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smoot-0.1.0.tar.gz (14.2 kB view hashes)

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