Metadata-Version: 2.4
Name: minionpy
Version: 1.4.0
Summary: MinionPy is the Python implementation of the Minion (C++) ibrary.
Author: Khoirul Faiq Muzakka
Author-email: Khoirul Faiq Muzakka <khoirul.muzakka@gmail.com>
License: MIT License
        -----------
        
        Copyright (c) 2024 Dr. Khoirul Faiq Muzakka (Forschungszentrum Jülich GmbH)
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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        LBFGSpp Library
        ---------------
        
        Minion uses the `LBFGSpp` library (https://github.com/yixuan/LBFGSpp), 
        which is also licensed under the MIT License. The use of this library in 
        Minion follows the same licensing terms, which allow for free usage, modification, 
        and distribution, provided that the copyright notice and license are included.
        
        For more information about the `LBFGSpp` library, please refer to its official repository.
        
        
        CEC Benchmark Problems Code (from Suganthan GitHub Repository)
        --------------------------------------------------------------
        
        This project includes implementations of the CEC benchmark problems that are based on 
        the original code released by [P. N. Suganthan](https://github.com/P-N-Suganthan). The original
        code was distributed prior to the CEC competitions for research and benchmarking purposes and, 
        to the best of our knowledge, is free to use and redistribute for non-commercial, 
        academic, and research applications.  
        
        
Project-URL: homepage, https://github.com/khoirulmuzakka/Minion
Project-URL: documentation, https://minion-py.readthedocs.io/
Project-URL: source, https://github.com/khoirulmuzakka/Minion
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.21.0
Dynamic: author
Dynamic: requires-python

# MinionPy

<div align="center">
  <img src="https://github.com/khoirulmuzakka/Minion/raw/main/docs/minion_logo.png" alt="Logo" width="200" />
</div>


![PyPI Python Version](https://img.shields.io/pypi/pyversions/minionpy)
![PyPI version](https://img.shields.io/pypi/v/minionpy.svg)
![PyPI downloads](https://img.shields.io/pypi/dm/minionpy.svg)
![PyPI License](https://img.shields.io/pypi/l/minionpy.svg)
[![Documentation Status](https://readthedocs.org/projects/minion-py/badge/?version=latest)](https://minion-py.readthedocs.io/en/latest/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14794240.svg)](https://doi.org/10.5281/zenodo.14794240)


MinionPy is the Python implementation of the Minion C++ library, designed for derivative-free optimization. It provides tools for solving optimization problems where gradients are unavailable or unreliable, incorporating state-of-the-art algorithms recognized in IEEE Congress on Evolutionary Computation (CEC) competitions. The library offers researchers and practitioners access to advanced optimization techniques and benchmarks for testing and evaluation.

## Features

- **Optimization Algorithms**  
    - **Differential Evolution-based algorithms:**
      - Basic Differential Evolution (DE)
      - JADE  
      - L-SHADE  
      - LSHADE-cnEpSin
      - jSO
      - j2020 
      - NL-SHADE-RSP 
      - LSRTDE 
      - ARRDE *(Adaptive Restart-Refine DE)*  
      - AGSK 
      . IMODE
    - **Other population-based algorithms:**
      - Artificial Bee Colony (ABC)
      - Grey Wolf DE Optimization  
      - 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), 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.

- **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 derivative-free 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:

- **[Minion Documentation](https://minion-py.readthedocs.io/)**

## 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](https://doi.org/10.5281/zenodo.14794239)  
