Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.

翻译：Pareto前沿学习（PFL）是一种近期引入的有效方法，可以从给定的权衡向量到Pareto前沿的解决方案，从而解决多目标优化（MOO）问题。由于冲突目标之间的内在权衡，在许多情况下，PFL提供了一种灵活的方法，在这种情况下，决策者无法规定一个Pareto解决方案比另一个优先，必须根据情况切换到它们之间。但是，现有的PFL方法忽略了优化过程中解决方案之间的关系，这已经妨碍了所获得的前沿质量。为了解决这个问题，我们提出了一种新的PFL框架 PHN-HVI，它采用一个超网络从一组多样化的权衡偏好中生成多个解决方案，并通过最大化这些解决方案定义的超体积指标来增强 Pareto 前沿的质量。在几个MOO机器学习任务上的实验结果显示，所提出的框架在获取权衡Pareto前沿方面显著优于其他基准方案。