Simultaneously visualizing the decision and objective space of continuous multi-objective optimization problems (MOPs) recently provided key contributions in understanding the structure of their landscapes. For the sake of advancing these recent findings, we compiled all state-of-the-art visualization methods in a single R-package (moPLOT). Moreover, we extended these techniques to handle three-dimensional decision spaces and propose two solutions for visualizing the resulting volume of data points. This enables - for the first time - to illustrate the landscape structures of three-dimensional MOPs. However, creating these visualizations using the aforementioned framework still lays behind a high barrier of entry for many people as it requires basic skills in R. To enable any user to create and explore MOP landscapes using moPLOT, we additionally provide a dashboard that allows to compute the state-of-the-art visualizations for a wide variety of common benchmark functions through an interactive (web-based) user interface.
翻译:在连续多目标优化问题的决定和客观空间同时,最近为了解其景观结构作出了重要贡献。为了推进这些最新发现,我们汇编了所有最先进的视觉化方法,用单一的R包(MOLIPT)进行汇编。此外,我们扩展了这些技术,以处理三维决策空间,并为由此得出的数据点量的视觉化提出了两种解决办法。这第一次使得能够展示三维模型的景观结构。然而,利用上述框架创建这些视觉化仍然为许多人设置了很高的进入障碍,因为它需要R的基本技能。为了使任何用户能够利用MOLOT创造和探索MOP景观,我们还提供了一个仪表板,以便通过互动(网基)用户界面,为广泛的通用基准功能计算出最先进的视觉化。