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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

First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3.

The official release is always available at PyPi:

pip install -U pymoo

For the current developer 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. 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.

python -c "from pymoo.util.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.algorithms.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=False)

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:

Citation

We are currently working on a journal publication for pymoo. Meanwhile, if you have used our framework for research purposes, please cite us with:

@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

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

mdjabc/pymoo

pymoo: Multi-objective Optimization in Python

Installation

Usage

Citation

Contact