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。

用于自动分析优化算法的大型语言模型

Large Language Models for the Automated Analysis of Optimization  Algorithms

The performance of multiobjective evolutionary algorithms (MOEAs) varies
across problems, making it hard to develop new algorithms or apply existing
ones to new problems. To simplify the development and application of new
multiobjective algorithms, there has been an increasing interest in their
automatic design from their components. These automatically designed
metaheuristics can outperform their human-developed counterparts. However, it
is still unknown what are the most influential components that lead to
performance improvements. This study specifies a new methodology to investigate
the effects of the final configuration of an automatically designed algorithm.
We apply this methodology to a tuned Multiobjective Evolutionary Algorithm
based on Decomposition (MOEA/D) designed by the iterated racing (irace)
configuration package on constrained problems of 3 groups: (1) analytical
real-world problems, (2) analytical artificial problems and (3) simulated
real-world. We then compare the impact of the algorithm components in terms of
their Search Trajectory Networks (STNs), the diversity of the population, and
the anytime hypervolume values. Looking at the objective space behavior, the
MOEAs studied converged before half of the search to generally good HV values
in the analytical artificial problems and the analytical real-world problems.
For the simulated problems, the HV values are still improving at the end of the
run. In terms of decision space behavior, we see a diverse set of the
trajectories of the STNs in the analytical artificial problems. These
trajectories are more similar and frequently reach optimal solutions in the
other problems.

研究使用自动设计的算法对多目标进化算法进行了效果评估，通过比较搜索轨迹网络、种群的多样性和任意时刻的超体积值，发现算法组件对于不同问题具有不同的影响。

多目标进化算法中组件效应对算法行为的影响

Multiobjective Evolutionary Component Effect on Algorithm behavior

Understanding the search dynamics of multiobjective evolutionary algorithms
(MOEAs) is still an open problem. This paper extends a recent network-based
tool, search trajectory networks (STNs), to model the behavior of MOEAs. Our
approach uses the idea of decomposition, where a multiobjective problem is
transformed into several single-objective problems. We show that STNs can be
used to model and distinguish the search behavior of two popular multiobjective
algorithms, MOEA/D and NSGA-II, using 10 continuous benchmark problems with 2
and 3 objectives. Our findings suggest that we can improve our understanding of
MOEAs using STNs for algorithm analysis.

本文研究多目标进化算法（MOEAs）的搜索动态，并将 STN 模型应用于两种常用算法（MOEA/D 和 NSGA-II）的分析，结果表明我们可以通过 STNs 来提高 MOEA 的算法分析。