pymoo 0.6.1.5


pip install pymoo

  Latest version

Released: May 26, 2025

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Author: Julian Blank
Requires Python: >=3.9

Classifiers

Intended Audience
  • Developers
  • Science/Research

Operating System
  • OS Independent

Programming Language
  • Python
  • Python :: 3
  • Python :: 3.9
  • Python :: 3.10
  • Python :: 3.11
  • Python :: 3.12
  • Python :: 3.13

Topic
  • Scientific/Engineering
  • Scientific/Engineering :: Artificial Intelligence
  • Scientific/Engineering :: Mathematics

python 3.10 license apache

pymoo

Documentation / Paper / Installation / Usage / Citation / Contact

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:

pymoo

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:

Julian Blank (blankjul [at] msu.edu)
Michigan State University
Computational Optimization and Innovation Laboratory (COIN)
East Lansing, MI 48824, USA

Wheel compatibility matrix

Platform CPython 3.9 CPython 3.10 CPython 3.11 CPython 3.12 CPython 3.13
macosx_10_13_universal2
macosx_10_9_universal2
macosx_11_0_arm64
manylinux2014_x86_64
manylinux_2_17_x86_64
musllinux_1_2_x86_64
win_amd64

Files in release

pymoo-0.6.1.5-cp310-cp310-macosx_10_9_universal2.whl (1.5MiB)
pymoo-0.6.1.5-cp310-cp310-macosx_11_0_arm64.whl (923.2KiB)
pymoo-0.6.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.0MiB)
pymoo-0.6.1.5-cp310-cp310-musllinux_1_2_x86_64.whl (5.4MiB)
pymoo-0.6.1.5-cp310-cp310-win_amd64.whl (1.9MiB)
pymoo-0.6.1.5-cp311-cp311-macosx_10_9_universal2.whl (1.5MiB)
pymoo-0.6.1.5-cp311-cp311-macosx_11_0_arm64.whl (921.7KiB)
pymoo-0.6.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2MiB)
pymoo-0.6.1.5-cp311-cp311-musllinux_1_2_x86_64.whl (5.6MiB)
pymoo-0.6.1.5-cp311-cp311-win_amd64.whl (1.9MiB)
pymoo-0.6.1.5-cp312-cp312-macosx_10_13_universal2.whl (1.5MiB)
pymoo-0.6.1.5-cp312-cp312-macosx_11_0_arm64.whl (921.9KiB)
pymoo-0.6.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2MiB)
pymoo-0.6.1.5-cp312-cp312-musllinux_1_2_x86_64.whl (5.5MiB)
pymoo-0.6.1.5-cp312-cp312-win_amd64.whl (1.9MiB)
pymoo-0.6.1.5-cp313-cp313-macosx_10_13_universal2.whl (1.5MiB)
pymoo-0.6.1.5-cp313-cp313-macosx_11_0_arm64.whl (915.9KiB)
pymoo-0.6.1.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1MiB)
pymoo-0.6.1.5-cp313-cp313-musllinux_1_2_x86_64.whl (5.5MiB)
pymoo-0.6.1.5-cp313-cp313-win_amd64.whl (1.9MiB)
pymoo-0.6.1.5-cp39-cp39-macosx_10_9_universal2.whl (1.5MiB)
pymoo-0.6.1.5-cp39-cp39-macosx_11_0_arm64.whl (927.3KiB)
pymoo-0.6.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.0MiB)
pymoo-0.6.1.5-cp39-cp39-musllinux_1_2_x86_64.whl (5.4MiB)
pymoo-0.6.1.5-cp39-cp39-win_amd64.whl (1.9MiB)
pymoo-0.6.1.5.tar.gz (252.0KiB)
Extras: None
Dependencies:
numpy (>=1.19.3)
scipy (>=1.1)
matplotlib (>=3)
autograd (>=1.4)
cma (>=3.2.2)
alive-progress
dill
Deprecated