MinionPy
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)
- Differential Evolution-based algorithms:
-
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 exposestatusso callers can distinguish convergence, evaluation budget exhaustion, callback stops, numerical errors, and other termination reasons. The status prints as a readable string. -
Callback early stopping
Python callbacks receive aMinionResult. ReturnTrueto stop optimization early, or returnFalse/Noneto 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
1 maintainer
Metadata
1 maintainer
Release files for minionpy 1.9.1
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Built distributions (wheels)
Total release size: 228.5 MB
Release files / minionpy-1.9.1-cp314-cp314-win_amd64.whl
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