Metadata-Version: 2.2
Name: minionpy
Version: 0.1.5
Summary: MinionPy is the Python implementation of the Minion C++ library, designed for derivative-free optimization.
Author: Khoirul Faiq Muzakka
Author-email: Khoirul Faiq Muzakka <khoirul.muzakka@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Dr. Khoirul Faiq Muzakka (Forschungszentrum Juelich GmbH)
        
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Project-URL: homepage, https://github.com/khoirulmuzakka/Minion
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Requires-Dist: numpy>=1.21.0
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# MinionPy

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


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[![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**  
  Implemented algorithms:
    - **Differential Evolution-based algorithms:**
      - Basic Differential Evolution (DE)
      - JADE  
      - L-SHADE 
      - jSO
      - j2020 
      - NL-SHADE-RSP 
      - LSRTDE 
      - ARRDE (our novel Adaptive Restart-Refine DE algorithm)  
    - **Other population-based algorithms:**
      - Artificial Bee Colony (ABC)
      - Grey Wolf DE Optimization  
    - **Classical optimization algorithms:**
      - Nelder-Mead  
      - Generalized Simulated Annealing (Dual Annealing)  

- **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.14794240](https://doi.org/10.5281/zenodo.14794240)  

