pymoo 0.2.3

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==3.7.1 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:

python -c 'from pymoo.cython.function_loader import is_compiled;print("Compiled Extentions: ", 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")

# solve the given problem using an optimization algorithm (here: nsga2)
res = minimize(problem,
               method='nsga2',
               method_args={'pop_size': 100},
               termination=('n_gen', 200),
               pf=problem.pareto_front(100),
               save_history=False,
               disp=True)
plotting.plot(res.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