.. |python| image:: https://img.shields.io/badge/python-3.10-blue.svg
:alt: python 3.10
.. |license| image:: https://img.shields.io/badge/license-apache-orange.svg
:alt: license apache
:target: https://www.apache.org/licenses/LICENSE-2.0
.. |logo| image:: https://github.com/anyoptimization/pymoo-data/blob/main/logo.png?raw=true
:target: https://pymoo.org
:alt: pymoo
.. |animation| image:: https://github.com/anyoptimization/pymoo-data/blob/main/animation.gif?raw=true
:target: https://pymoo.org
:alt: pymoo
.. _Github: https://github.com/anyoptimization/pymoo
.. _Documentation: https://www.pymoo.org/
.. _Paper: https://ieeexplore.ieee.org/document/9078759
|python| |license|
|logo|
Documentation_ / Paper_ / Installation_ / Usage_ / Citation_ / Contact_
pymoo: Multi-objective Optimization in Python
====================================================================
Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features
related to multi-objective optimization such as visualization and decision making.
.. _Installation:
Installation
********************************************************************************
First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3.
The official release is always available at PyPi:
.. code:: bash
pip install -U pymoo
For the current developer version:
.. code:: bash
git clone https://github.com/anyoptimization/pymoo
cd pymoo
pip install .
Since for speedup, some of the modules are also available compiled, you can double-check
if the compilation worked. When executing the command, be sure not already being in the local pymoo
directory because otherwise not the in site-packages installed version will be used.
.. code:: bash
python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())"
.. _Usage:
Usage
********************************************************************************
We refer here to our documentation for all the details.
However, for instance, executing NSGA2:
.. code:: python
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.visualization.scatter import Scatter
problem = get_problem("zdt1")
algorithm = NSGA2(pop_size=100)
res = minimize(problem,
algorithm,
('n_gen', 200),
seed=1,
verbose=True)
plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, color="red")
plot.show()
A representative run of NSGA2 looks as follows:
|animation|
.. _Citation:
Citation
********************************************************************************
If you have used our framework for research purposes, you can cite our publication by:
| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 <https://ieeexplore.ieee.org/document/9078759>`_
|
| BibTex:
::
@ARTICLE{pymoo,
author={J. {Blank} and K. {Deb}},
journal={IEEE Access},
title={pymoo: Multi-Objective Optimization in Python},
year={2020},
volume={8},
number={},
pages={89497-89509},
}
.. _Contact:
Contact
********************************************************************************
Feel free to contact me if you have any questions:
| `Julian Blank <http://julianblank.com>`_ (blankjul [at] msu.edu)
| Michigan State University
| Computational Optimization and Innovation Laboratory (COIN)
| East Lansing, MI 48824, USA
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