Minion is a high-performance derivative-free optimization library designed for solving complex optimization problems where gradients are unavailable or unreliable. It implements state-of-the-art evolutionary algorithms, including top-performing methods from IEEE CEC competitions, which are often missing in standard optimization libraries such as SciPy, NLopt, OptimLib, pyGMO, and pagmo2. Minion is not only a solver but also a research platform for developing and testing new optimization algorithms. It includes benchmark functions from CEC competitions (2011, 2014, 2017, 2019, 2020, and 2022), providing a robust framework for algorithm evaluation and comparison. Features: State-of-the-art optimization algorithms: Implements JADE, L-SHADE, jSO, j2020, NL-SHADE-RSP, LSRTDE, and ARRDE (our novel Adaptive Restart-Refine DE algorithm). Parallelization-ready: Supports vectorized function evaluations, allowing seamless integration with multithreading and multiprocessing for high-performance optimization. Optimized C++ backend with a Python wrapper: Provides high efficiency with an easy-to-use Python API. CEC Benchmark Suite: Includes benchmark problems from 2011, 2014, 2017, 2019, 2020, and 2022 for rigorous testing and comparison. MinionPy: Python Package Minion is available as a Python package called MinionPy, which provides an easy-to-use interface for Python users while benefitting from the optimized C++ backend. It can be installed via pip: pip install minionpy This package allows users to access Minion’s optimization algorithms directly in Python, making it suitable for both research and practical applications. Documentation For detailed usage instructions, examples, and API references, visit the official documentation:🔗 Minion Documentation