pymoo 0.2.4

Multi-Objective Optimization Algorithms

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. 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.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