We present a review that unifies decision-support methods for exploring the
solutions produced by multi-objective optimization (MOO) algorithms. As MOO is
applied to solve diverse problems, approaches for analyzing the trade-offs
offered by MOO algorithms are scattered across fields. We provide an overview
of the advances on this topic, including methods for visualization, mining the
solution set, and uncertainty exploration as well as emerging research
directions, including interactivity, explainability, and ethics. We synthesize
these methods drawing from different fields of research to build a unified
approach, independent of the application. Our goals are to reduce the entry
barrier for researchers and practitioners on using MOO algorithms and to
provide novel research directions.

我们提供了一个综述，统一了多目标优化算法产生的解决方案的决策支持方法。我们提供这个主题的最新进展的概述，包括可视化方法、解决方案集挖掘、不确定性探索以及新兴研究方向，包括交互性、可解释性和伦理。我们综合了来自不同研究领域的这些方法，构建了一个独立于应用的统一方法。我们的目标是降低研究人员和实践者使用多目标优化算法的门槛，并提供新的研究方向。