Evolutionary multi-objective optimization (EMO) algorithms have been demonstrated to be effective in solving multi-criteria decision-making problems. In real-world applications, analysts often employ several algorithms concurrently and compare their solution sets to gain insight into the characteristics of different algorithms and explore a broader range of feasible solutions. However, EMO algorithms are typically treated as black boxes, leading to difficulties in performing detailed analysis and comparisons between the internal evolutionary processes. Inspired by the successful application of visual analytics tools in explainable AI, we argue that interactive visualization can significantly enhance the comparative analysis between multiple EMO algorithms. In this paper, we present a visual analytics framework that enables the exploration and comparison of evolutionary processes in EMO algorithms. Guided by a literature review and expert interviews, the proposed framework addresses various analytical tasks and establishes a multi-faceted visualization design to support the comparative analysis of intermediate generations in the evolution as well as solution sets. We demonstrate the effectiveness of our framework through case studies on benchmarking and real-world multi-objective optimization problems to elucidate how analysts can leverage our framework to inspect and compare diverse algorithms.

翻译：进化多目标优化算法已被证明能有效解决多准则决策问题。在实际应用中，分析人员通常同时采用多种算法，通过比较其解集来洞察不同算法的特性，并探索更广泛的可行解空间。然而，进化多目标优化算法通常被视为黑箱，导致难以对内部进化过程进行细粒度分析与比较。受可解释人工智能中可视化分析工具成功应用的启发，我们认为交互式可视化能显著增强多种进化多目标优化算法间的比较分析。本文提出一个可视化分析框架，用于探索和比较进化多目标优化算法中的进化过程。该框架基于文献综述与专家访谈，针对多项分析任务构建多层面可视化设计，以支持进化过程中间代与解集的比较分析。我们通过基准测试与实际多目标优化问题的案例研究，展示了框架的有效性，阐明了分析人员如何利用本框架检验与比较不同算法。