Since the rise of Large Language Models (LLMs) a couple of years ago,
researchers in metaheuristics (MHs) have wondered how to use their power in a
beneficial way within their algorithms. This paper introduces a novel approach
that leverages LLMs as pattern recognition tools to improve MHs. The resulting
hybrid method, tested in the context of a social network-based combinatorial
optimization problem, outperforms existing state-of-the-art approaches that
combine machine learning with MHs regarding the obtained solution quality. By
carefully designing prompts, we demonstrate that the output obtained from LLMs
can be used as problem knowledge, leading to improved results. Lastly, we
acknowledge LLMs' potential drawbacks and limitations and consider it essential
to examine them to advance this type of research further.

该论文介绍了一种创新方法，利用 Large Language Models（LLMs）作为模式识别工具来改善元启发式算法，并通过在基于社交网络的组合优化问题中进行测试，证明了该混合方法在解决方案质量方面优于现有的结合机器学习和元启发式算法的最新方法。通过精心设计提示，我们证明了从 LLMs 获得的输出可以用作问题知识，从而提高了结果。最后，我们承认 LLMs 的潜在缺点和限制，并认为进一步研究这方面的研究需要对其进行检验。