  Optimization algorithms and large language models (LLMs) enhance
decision-making in dynamic environments by integrating artificial intelligence
with traditional techniques. LLMs, with extensive domain knowledge, facilitate
intelligent modeling and strategic decision-making in optimization, while
optimization algorithms refine LLM architectures and output quality. This
synergy offers novel approaches for advancing general AI, addressing both the
computational challenges of complex problems and the application of LLMs in
practical scenarios. This review outlines the progress and potential of
combining LLMs with optimization algorithms, providing insights for future
research directions.
