ParetoLens帮助研究人员交互式探索多目标优化解集的分布特征。
ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms
- 采用模块化设计，支持决策空间与目标空间的动态可视化分析。
- 通过交互式视图揭示解集在高维空间中的分布模式与权衡关系。
- 适合从事多目标进化算法研究或优化问题分析的科研人员使用。
在多目标优化领域，进化算法因其能够生成多样化解集以应对多个目标间的权衡而备受关注，推动了进化多目标优化（EMO）成为主流方法。然而，所得解集的分析面临巨大挑战，主要源于数据的高维特性以及静态可视化手段导致的视觉混乱，阻碍了交互式探索。为此，本文提出ParetoLens，一个专为提升多目标进化算法解集可视化分析效率而设计的交互式框架。该框架采用模块化、算法无关的设计，通过一系列交互式可视化手段，在决策空间与目标空间中对解集分布进行深入检视。不仅缓解了静态可视化带来的问题，还支持更精细灵活的分析流程。通过案例研究与专家访谈验证了其有效性，证明其能发现复杂模式并深化对解集结构的理解。框架演示网站已开放：https://dva-lab.org/paretolens/。
原文摘要 · Abstract (English)
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-offs inherent among competing objectives. This has catalyzed the ascension of evolutionary multi-objective optimization (EMO) as a prevalent approach. Despite the effectiveness of the EMO paradigm, the analysis of resultant solution sets presents considerable challenges. This is primarily attributed to the high-dimensional nature of the data and the constraints imposed by static visualization methods, which frequently culminate in visual clutter and impede interactive exploratory analysis. To address these challenges, this paper introduces ParetoLens, a visual analytics framework specifically tailored to enhance the inspection and exploration of solution sets derived from the multi-objective evolutionary algorithms. Utilizing a modularized, algorithm-agnostic design, ParetoLens enables a detailed inspection of solution distributions in both decision and objective spaces through a suite of interactive visual representations. This approach not only mitigates the issues associated with static visualizations but also supports a more nuanced and flexible analysis process. The usability of the framework is evaluated through case studies and expert interviews, demonstrating its potential to uncover complex patterns and facilitate a deeper understanding of multi-objective optimization solution sets. A demo website of ParetoLens is available at https://dva-lab.org/paretolens/.
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