GLOBe: A Modular Global Optimization library

TL;DR

GLOBe is an open-source, modular Python library that unifies various classical and recent global optimization algorithms, facilitating easy comparison and extension within research workflows.

Contribution

It introduces a modular architecture that formalizes common algorithmic patterns, enabling reusable components and plugin development for global optimization methods.

Findings

Includes 14 optimizers and 19 benchmarks.

Provides an integrated toolkit for algorithm comparison.

Features a modular design for easy extension and reuse.

Abstract

Open-source libraries are have a catalytic role in research pipelines, where new methods must be compared against up-to-date baselines. We present the GLobal Optimization Benchmark (GLOBe) modular Python library that unifies classical and recent continuous global optimization algorithms, including decision-based and mathematically founded particle-based methods, in a single framework. A central contribution of GLOBe is its modular architecture, which factors common algorithmic patterns into reusable familyl evel components and enables plugins to be implemented once and makes them available to all algorithms in the corresponding family. This modular design leverages recent advances in mathematical formalization of global optimization, where structural commonalities across algorithms have been identified and used to develop broadly applicable, formally grounded algorithmic features. The C++ backend relies on Eigen 5 for efficient linear algebra. GLOBe presently includes 14 optimizers, 19 analytical benchmarks along with a random function generator, and an integrated toolkit for direct algorithm comparison.