Surrogate based multi-objective optimization tool
Project description
smoot
Installation
pip install smoot
Required 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.
Usage
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
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