Documentation / Paper / Installation / Usage / Citation / Contact
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.
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.functions import is_compiled;print('Compiled Extensions: ', is_compiled())"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:
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},
}
Feel free to contact me if you have any questions: