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.

conda --version

Then from scratch create a virtual environment for pymoo:

conda create -n pymoo -y python==3.6 cython numpy
conda activate pymoo

For the current stable release please execute:

pip install pymoo

For the current development version:

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:

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:

from pymoo.optimize import minimize
from pymoo.util import plotting
from pymop.factory import get_problem

# create the optimization problem
problem = get_problem("zdt1")
pf = problem.pareto_front()

res = minimize(problem,
               method='nsga2',
               method_args={'pop_size': 100},
               termination=('n_gen', 200),
               pf=pf,
               save_history=False,
               disp=True)
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