Metadata-Version: 2.1
Name: pymoo
Version: 0.5.0
Summary: Multi-Objective Optimization in Python
Home-page: https://pymoo.org
Author: Julian Blank
Author-email: blankjul@egr.msu.edu
License: Apache License 2.0
Description: 
        .. |python| image:: https://img.shields.io/badge/python-3.9-blue.svg
           :alt: python 3.9
        
        .. |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://raw.githubusercontent.com/anyoptimization/pymoo/master/data/logo.png
          :target: https://pymoo.org
          :alt: pymoo
        
        
        .. |animation| image:: https://raw.githubusercontent.com/anyoptimization/pymoo/master/data/animation.gif
          :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.factory 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] egr.msu.edu)
        | Michigan State University
        | Computational Optimization and Innovation Laboratory (COIN)
        | East Lansing, MI 48824, USA
        
        
        
        
Keywords: optimization
Platform: any
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
