https://github.com/anyoptimization/pymoo

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
https://github.com/anyoptimization/pymoo

cmaes differential-evolution genetic-algorithm multi-objective-optimization nsga2 nsga3 optimization pso

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NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

• Host: GitHub
• URL: https://github.com/anyoptimization/pymoo
• Owner: anyoptimization
• License: apache-2.0
• Created: 2017-09-19T21:52:39.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2024-12-16T01:06:37.000Z (16 days ago)
• Last Synced: 2024-12-23T19:07:00.594Z (8 days ago)
• Topics: cmaes, differential-evolution, genetic-algorithm, multi-objective-optimization, nsga2, nsga3, optimization, pso
• Language: Python
• Homepage: https://pymoo.org
• Size: 17.7 MB
• Stars: 2,352
• Watchers: 29
• Forks: 402
• Open Issues: 36
• Metadata Files:
  • Readme: README.rst
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        


.. |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 `_

|

| 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 `_  (blankjul [at] msu.edu)

| Michigan State University

| Computational Optimization and Innovation Laboratory (COIN)

| East Lansing, MI 48824, USA