The ability of Large Language Models (LLMs) to generate high-quality text and
code has fuelled their rise in popularity. In this paper, we aim to demonstrate
the potential of LLMs within the realm of optimization algorithms by
integrating them into STNWeb. This is a web-based tool for the generation of
Search Trajectory Networks (STNs), which are visualizations of optimization
algorithm behavior. Although visualizations produced by STNWeb can be very
informative for algorithm designers, they often require a certain level of
prior knowledge to be interpreted. In an attempt to bridge this knowledge gap,
we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive
written reports, complemented by automatically generated plots, thereby
enhancing the user experience and reducing the barriers to the adoption of this
tool by the research community. Moreover, our approach can be expanded to other
tools from the optimization community, showcasing the versatility and potential
of LLMs in this field.

利用大型语言模型（LLMs）在优化算法领域中的潜力，我们将它们集成到 STNWeb 中，通过生成详细的书面报告和自动生成的图表，提升用户体验，减少研究社区对该工具的采用障碍。同时，我们的方法还可以扩展到优化领域的其他工具，展示了 LLMs 在这一领域的多样性和潜力。