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<p>This review systematically summarizes the research progress of Large Language Models (LLMs) as meta-optimizers in the field of automated intelligent optimization algorithm design, aiming to establish a unified framework for this emerging research direction. The study first defines the core paradigm and research scope of LLM-driven meta-optimization, explaining how it overcomes the limitations of traditional optimization algorithms in terms of parameter sensitivity and cross-domain generalization. It then systematically summarizes representative methodological frameworks and typical methods for key stages such as algorithm auto-generation, dynamic selection, parameter configuration, and mutation control using LLMs, revealing the potential for synergies between generative AI and gradient-free optimization heuristics. Additionally, this paper integrates relevant performance evaluation metrics and benchmarking problems, providing practical references for researchers. Finally, the paper discusses the main challenges in this field and outlines future research directions, emphasizing the core role of LLMs as meta-optimizers in driving paradigm shifts toward algorithm design automation and intelligence.</p>

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A survey on large language models driven meta-optimizers for automated intelligent optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月6日

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  • Yan Zheng,
  • Lida Zhang,
  • Kaiwen Li,
  • Rui Wang,
  • Wenhua Li,
  • Tao Zhang,
  • Qingfu Zhang,
  • Yaochu Jin

摘要

This review systematically summarizes the research progress of Large Language Models (LLMs) as meta-optimizers in the field of automated intelligent optimization algorithm design, aiming to establish a unified framework for this emerging research direction. The study first defines the core paradigm and research scope of LLM-driven meta-optimization, explaining how it overcomes the limitations of traditional optimization algorithms in terms of parameter sensitivity and cross-domain generalization. It then systematically summarizes representative methodological frameworks and typical methods for key stages such as algorithm auto-generation, dynamic selection, parameter configuration, and mutation control using LLMs, revealing the potential for synergies between generative AI and gradient-free optimization heuristics. Additionally, this paper integrates relevant performance evaluation metrics and benchmarking problems, providing practical references for researchers. Finally, the paper discusses the main challenges in this field and outlines future research directions, emphasizing the core role of LLMs as meta-optimizers in driving paradigm shifts toward algorithm design automation and intelligence.