Large Language Models (LLMs) have recently emerged as effective surrogate models and candidate generators within global optimization frameworks for expensive blackbox functions. Despite promising results, LLM-based methods often struggle in high-dimensional search spaces or when lacking domain-specific priors, leading to sparse or uninformative suggestions. To overcome these limitations, we propose HOLLM, a novel global optimization algorithm that enhances LLM-driven sampling by partitioning the search space into promising subregions. Each subregion acts as a ``meta-arm'' selected via a bandit-inspired scoring mechanism that effectively balances exploration and exploitation. Within each selected subregion, an LLM then proposes high-quality candidate points, without any explicit domain knowledge. Empirical evaluation on standard optimization benchmarks shows that HOLLM consistently matches or surpasses leading global optimization methods, while substantially outperforming global LLM-based sampling strategies.
@article{arxiv.2505.21372,
title = {Improving LLM-based Global Optimization with Search Space Partitioning},
author = {Andrej Schwanke and Lyubomir Ivanov and David Salinas and Fabio Ferreira and Aaron Klein and Frank Hutter and Arber Zela},
journal= {arXiv preprint arXiv:2505.21372},
year = {2026}
}