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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/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.

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.moo.nsga2 import NSGA2
from pymoo.problems 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:

Citation

If you have used our framework for research purposes, you can cite our publication by:

@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 questions: