pysamoo - Surrogate-Assisted Multi-objective Optimization

The software documentation is available here: https://anyoptimization.com/projects/pysamoo/

Developer guides (in this repository):

• docs/PERFORMANCE.md — why surrogate-assisted runs can be slow and how to speed them up.
• docs/BENCHMARKING.md — measuring solution quality and developing better algorithms/models.

Installation

The official release is always available at PyPi:

pip install -U pysamoo

Usage

We refer here to our documentation for all the details. However, for instance, executing NSGA2:

from pymoo.optimize import minimize
from pymoo.problems.multi.zdt import ZDT1
from pymoo.visualization.scatter import Scatter
from pysamoo.algorithms.ssansga2 import SSANSGA2

problem = ZDT1(n_var=10)

algorithm = SSANSGA2(n_initial_doe=50,
                     n_infills=10,
                     surr_pop_size=100,
                     surr_n_gen=50)

res = minimize(
    problem,
    algorithm,
    ('n_evals', 200),
    seed=1,
    verbose=True)

plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, facecolor="none", edgecolor="red")
plot.show()

Citation

If you use this framework, we kindly ask you to cite the following paper:

Julian Blank, & Kalyanmoy Deb. (2022). pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python.

BibTex:

@misc{pysamoo,
  title={pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python},
  author={Julian Blank and Kalyanmoy Deb},
  year={2022},
  eprint={2204.05855},
  archivePrefix={arXiv},
  primaryClass={cs.NE}
}

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