BenchmarkFcns¶
BenchmarkFcns is a high-performance library providing a comprehensive suite of over 310 mathematical benchmark functions designed for the rigorous testing and evaluation of optimization algorithms.
By combining a core engine implemented in C++ with Eigen and multi-core parallelization via OpenMP, the library offers extremely fast evaluation speeds, often providing 10x-30x speedups over traditional pure-Python implementations. This makes it an ideal choice for large-scale tasks, Reinforcement Learning training, and high-fidelity surrogate modeling.
Core Benefits¶
High Performance: Optimized C++ core leveraging modern SIMD instruction sets.
AI & RL Ready: Standardized Gymnasium (OpenAI Gym) environments for training agents on complex landscapes.
Massive Batch Support: Native vectorization allows evaluating millions of data points in parallel.
Comprehensive Coverage: Includes Classic (100+), Multi-Objective (50), Multi-Fidelity (49), and official CEC Competition suites (2005-2022).
Cross-Language Consistency: Mathematically verified implementations ensuring consistent results across Python, C++, and MATLAB.