A Python Benchmark Functions Framework for Numerical Optimisation Problems

TL;DR

This paper introduces a user-friendly Python framework that provides a comprehensive collection of benchmark functions for testing and evaluating numerical optimization algorithms, including visualization and testing tools.

Contribution

The framework offers an easy-to-use, expandable Python package with extensive multi-modal functions, meta-information, visualization, and testing features for optimization research.

Findings

Includes widely used multi-modal functions

Supports arbitrary dimensions for test cases

Provides visualization and baseline algorithms

Abstract

This work proposes a framework of benchmark functions designed to facilitate the creation of test cases for numerical optimisation techniques. The framework, written in Python 3, is designed to be easy to install, use, and expand. The collection includes some of the most used multi-modal continuous functions present in literature, which can be instantiated using an arbitrary number of dimensions. Meta-information of each benchmark function, like search boundaries and position of known optima, are included and made easily accessible through class methods. Built-in interactive visualisation capabilities, baseline techniques, and rigorous testing protocols complement the features of the framework. The framework can be found here: \url{https://gitlab.com/luca.baronti/python_benchmark_functions