Search Trajectory Networks

Beschreibung

Visual representations of complex concepts enhance our ability to understand digital information more effectively, particularly in fields like Computer Science and Artificial Intelligence. Despite this, the research community in optimization has been relatively unproductive in developing visual tools, even though there is a growing need for such tools to aid in comparing optimization algorithms. Traditionally, the comparison of optimization algorithms has relied on collecting numerical data from optimization runs and presenting them through tables and conventional data visualizations (e.g., line plots, bar plots, scatter plots). This has typically been supplemented by statistical analyses of the data. However, in recent years, more researchers have recognized that to gain a deeper understanding of the behavior of optimization algorithms, such as metaheuristics, additional graphical tools are necessary. Furthermore, these tools must be user-friendly in order to facilitate their use.

In this book, the authors present one of the latest and most advanced efforts along these lines. Search Trajectory Networks (STNs) are representations of multiple runs of multiple optimization algorithms applied to the same instance of the tackled optimization problem. In mathematical terms they are directed graph objects. STNWeb is a tool for the generation of STNs on the basis of data logs obtained from running optimization algorithms. After the generation of an STN, it produces a visualization of the STN that can then be examined and analyzed by the user. The first version of STNWeb produces 2D visualizations, while the second version of STNWeb generates interactive 3D visualizations. Users can store the generated visualizations in order to be used, for example, in their scientific articles. In this book we provide a detailed description of the functionalities of both versions of STNWeb and we show how to analyze the visualizations. In fact, both STNWeb versions are equipped with Large Language Model (LLM) support for the analysis of visualizations. We also describe interesting use cases that show how the use of STN visualizations can help in algorithmic research on metaheuristics.

Visual representations of complex concepts enhance our ability to understand digital information more effectively, particularly in fields like Computer Science and Artificial Intelligence. Despite this, the research community in optimization has been relatively unproductive in developing visual tools, even though there is a growing need for such tools to aid in comparing optimization algorithms. Traditionally, the comparison of optimization algorithms has relied on collecting numerical data from optimization runs and presenting them through tables and conventional data visualizations (e.g., line plots, bar plots, scatter plots). This has typically been supplemented by statistical analyses of the data. However, in recent years, more researchers have recognized that to gain a deeper understanding of the behavior of optimization algorithms, such as metaheuristics, additional graphical tools are necessary. Furthermore, these tools must be user-friendly to facilitate their use.

In this book, the authors present one of the latest and most advanced efforts in this direction. Search Trajectory Networks (STNs) provide a means of representing multiple runs of multiple optimization algorithms applied to the same instance of an optimization problem. Mathematically, STNs are directed graphs. The authors first provide a formal description of how STNs can be constructed from data logs generated by runs of optimization algorithms. The resulting STNs can then be visualized using graph-layout techniques, enabling users to visually inspect and analyze the search behavior of the algorithms. Such visualizations can potentially be generated in different ways. The authors introduce STNWeb, currently the principal tool for this purpose. They provide a detailed account of its functionality and explain how the resulting visualizations can be analyzed and interpreted. Notably, STNWeb incorporates Large Language Model (LLM) support to assist users in analyzing these visualizations. Finally, the authors present a range of compelling use cases illustrating how STN visualizations can contribute to algorithmic research on metaheuristics.

Autor*in

Gabriela Ochoa

Themen in »Search Trajectory Networks«

Search Trajectory Networks (STNs) STNWeb Optimization Convex Optimization Stochastic Optimization Multi-objective Optimization 2D Visualization 3D Visualization Large Language Models Metaheuristics Automated Explainability

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