Metadata-Version: 1.2
Name: pymoo
Version: 0.3.0
Summary: Multi-Objective Optimization in Python
Home-page: https://github.com/msu-coinlab/pymoo
Author: Julian Blank
Author-email: blankjul@egr.msu.edu
License: Apache License 2.0
Description: pymoo - Multi-Objective Optimization Framework
        ====================================================================
        
        You can find the detailed documentation here: https://pymoo.org
        
        
        |gitlab| |python| |license|
        
        
        .. |gitlab| image:: https://gitlab.msu.edu/blankjul/pymoo/badges/master/pipeline.svg
           :alt: build status
           :target: https://gitlab.msu.edu/blankjul/pymoo/commits/master
        
        .. |python| image:: https://img.shields.io/badge/python-3.6-blue.svg
           :alt: python 3.6
        
        .. |license| image:: https://img.shields.io/badge/license-apache-orange.svg
           :alt: license apache
           :target: https://www.apache.org/licenses/LICENSE-2.0
        
        
        We are currently working on a paper about *pymoo*.
        Meanwhile, if you have used our framework for research purposes, please cite us with:
        
        ::
        
           @misc{pymoo,
               author = {Julian Blank and Kalyanmoy Deb},
               title = {pymoo - {Multi-objective Optimization in Python}},
               howpublished = {https://pymoo.org}
           }
        
        
        
        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 Cython>=0.29 numpy>=1.15 pymoo
        
        
        For the current developer version:
        
        .. code:: bash
        
            git clone https://github.com/msu-coinlab/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.cython.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())"
        
        
        
        Usage
        ==================================
        
        We refer here to our documentation for all the details.
        However, for instance executing NSGA2:
        
        .. code:: python
        
           from pymoo.optimize import minimize
           from pymoo.algorithms.nsga2 import nsga2
           from pymoo.util import plotting
           from pymop.factory import get_problem
        
           # load a test or define your own problem
           problem = get_problem("zdt1")
        
           # get the optimal solution of the problem for the purpose of comparison
           pf = problem.pareto_front()
        
           # create the algorithm object
           method = nsga2(pop_size=100, elimate_duplicates=True)
        
           # execute the optimization
           res = minimize(problem,
                          method,
                          termination=('n_gen', 200),
                          pf=pf,
                          disp=True)
        
           # plot the results as a scatter plot
           plotting.plot(pf, res.F, labels=["Pareto-Front", "F"])
        
        
        
        Contact
        ====================================================================
        Feel free to contact me if you have any question:
        
        | Julian Blank (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 :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >3.3
