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.

翻译：自大型语言模型（LLMs）数年前兴起以来，元启发式算法（MHs）领域的研究者一直在探索如何在其算法中有效利用LLMs的能力。本文提出一种创新方法，将LLMs作为模式识别工具来改进MHs。通过在基于社交网络的组合优化问题中测试，这种混合方法在所得解的质量方面超越了现有结合机器学习与MHs的先进方法。通过精心设计提示，我们证明从LLMs获得的输出可作为问题知识使用，从而提升优化效果。最后，我们认识到LLMs的潜在缺陷与局限，并认为必须对其加以审视以推动此类研究的进一步发展。