MF2: A Collection of Multi-Fidelity Benchmark Functions in Python
Sander van Rijn, Sebastian Schmitt · The Journal of Open Source Software · 2020
The field of (evolutionary) optimization algorithms often works with expensive black-box optimization problems.However, for the development of novel algorithms and approaches, real-world problems are not feasible due to their high computational cost.Instead, benchmark functions such as Sphere, Rastrigin, and Ackley are typically used.These functions are not only fast to compute, but also have known properties which are very helpful when examining the performance of new algorithms.