Metadata-Version: 1.2
Name: pymoo
Version: 0.2.3
Summary: Multi-Objective Optimization Algorithms
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://www.egr.msu.edu/coinlab/blankjul/pymoo/
        
        
        Installation
        ====================================================================
        
        First, make sure you have a python environment installed. We recommend miniconda3 or anaconda3.
        
        .. code:: bash
        
            conda --version
        
        Then from scratch create a virtual environment for pymoo:
        
        .. code:: bash
        
            conda create -n pymoo -y python==3.7.1 cython numpy
            conda activate pymoo
        
        
        For the current stable release please execute:
        
        .. code:: bash
        
            pip install pymoo
        
        For the current development 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:
        
        .. code:: bash
        
            python -c 'from pymoo.cython.function_loader import is_compiled;print("Compiled Extentions: ", 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.util import plotting
            from pymop.factory import get_problem
        
            # create the optimization problem
            problem = get_problem("zdt1")
        
            # solve the given problem using an optimization algorithm (here: nsga2)
            res = minimize(problem,
                           method='nsga2',
                           method_args={'pop_size': 100},
                           termination=('n_gen', 200),
                           pf=problem.pareto_front(100),
                           save_history=False,
                           disp=True)
            plotting.plot(res.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
Requires-Python: >3.3.0
