Opytimark: Python Optimization Benchmarking Functions

Opytimark provides ready-to-use benchmark functions for evaluating optimization algorithms.

Opytimark supports Python 3.11 or newer. Read the full API reference at opytimark.readthedocs.io.

Installation

Opytimark is published on PyPI. Add it to a project managed by uv with:

uv add opytimark

For a consumer installation in an existing Python environment, pip is also supported:

pip install opytimark

Usage

import numpy as np

from opytimark.markers.n_dimensional import Sphere

value = Sphere()(np.array([1.0, 2.0, 3.0]))
print(value)

More examples are available in examples/.

Reproducibility

Imports do not reset NumPy's random state. For reproducible noisy or randomized benchmarks, call np.random.seed(your_seed) explicitly before the experiment.

The 3.0.1 CEC conditioning, group-rotation, and composition corrections change some fitness values relative to 3.0.0. See numerical behavior before comparing old and new optimization results.

Development

Install uv, clone the repository, then run:

uv sync --locked
uv run pytest
uv run pre-commit run --all-files
uv run --locked --group docs sphinx-build -W --keep-going -b html docs docs/_build/html
uv build

Citation

If you use Opytimark, please cite:

@misc{rosa2019opytimizer,
    title={Opytimizer: A Nature-Inspired Python Optimizer},
    author={Gustavo H. de Rosa and João P. Papa},
    year={2019},
    eprint={1912.13002},
    archivePrefix={arXiv},
    primaryClass={cs.NE}
}