MinionPy

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MinionPy is the Python interface to the Minion C++ optimization library. It focuses on single-objective, derivative-free optimization. The package includes several population-based and local optimization methods, along with CEC benchmark suites that can be used for testing and comparison.

Features

  • Optimization Algorithms

    • Differential Evolution-based algorithms:
      • Basic Differential Evolution (DE)
      • JADE
      • LSHADE
      • ARRDE
      • and other DE variants
    • Other population-based algorithms:
      • Artificial Bee Colony (ABC)
      • Canonical PSO, SPSO-2011, Dynamic Multi-Swarm PSO (DMS-PSO)
      • CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
      • BIPOP-aCMAES
      • RCMAES
    • Classical optimization algorithms:
      • Nelder-Mead
      • Generalized Simulated Annealing (Dual Annealing)
      • L-BFGS-B (vectorized & noise-robust)
      • L-BFGS (vectorized & noise-robust)

  • Benchmark Support
    The library includes benchmark functions from the CEC competitions (2011, 2014, 2017, 2019, 2020, 2022) and BBOB2009, providing a standardized environment for algorithm development, testing, and comparison.

  • Performance
    Most implemented algorithms are population-based, making them suitable for parallelization. MinionPy is optimized for vectorized functions, enabling efficient use of multithreading and multiprocessing capabilities.

  • Explicit termination status
    Optimization results expose status so callers can distinguish convergence, evaluation budget exhaustion, callback stops, numerical errors, and other termination reasons. The status prints as a readable string; use succeeded() for the C++-matching success predicate.

  • Callback early stopping
    Python callbacks receive a MinionResult. Return True to stop optimization early, or return False/None to continue.

  • Cross-Platform Compatibility
    MinionPy is implemented in C++ with a Python wrapper, supporting usage in both languages. It has been tested on the following platforms:

    • Windows 11
    • Linux Ubuntu 24.04
    • macOS Sequoia 15

Applications

MinionPy is applicable in scenarios where single objective, bound-constrained/unconstrauned optimization is required, including engineering, physics, and machine learning. Its standardized benchmarks and high-performance algorithms make it suitable for developing and evaluating new optimization techniques as well as solving real-world optimization problems.

📖 Documentation

For full usage instructions, API reference, and examples, visit the official documentation:

Citing Minion

If you use MinionPy in your research or projects, we would be grateful if you could cite the following publication:

Muzakka, K. F., Möller, S., & Finsterbusch, M. (2025).
Minion: A high-performance derivative-free optimization library designed for solving complex optimization problems.
Zenodo. https://doi.org/10.5281/zenodo.14794239

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.9.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for muzakka from gravatar.com muzakka

Credits

Author: Khoirul Faiq Muzakka

License expression

MIT
View SPDX License List

Requires

Python >=3.9

Classifiers

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1.9.1

24 files

1.9.0

24 files

This release

1.8.0 This release

24 files

1.7.0

24 files

1.6.1

24 files

1.6.0

24 files

1.4.0

15 files

1.3.0

15 files

1.2.0

15 files

1.1.0

25 files

1.0.1

25 files

0.2.8

25 files

0.2.7

25 files

0.2.6

25 files

0.2.5

25 files

0.2.4

25 files

0.2.3

25 files

0.2.2

25 files

0.1.7

54 files

0.1.6

54 files

0.1.5

54 files

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