Multi-Objective Optimization Algorithms
Project description
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
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