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：帕累托前沿学习（PFL）近期被引入作为一种有效方法，用于从给定的权衡向量映射到帕累托前沿上的解，从而解决多目标优化（MOO）问题。由于冲突目标之间存在固有权衡，PFL 在决策者无法指定某一帕累托解优于另一解、且需根据情境在不同解之间切换的众多场景中提供了一种灵活的方法。然而，现有 PFL 方法在优化过程中忽略了各解之间的关系，这阻碍了所获前沿的质量。为解决这一问题，我们提出一种新颖的 PFL 框架，即 PHN-HVI，该框架采用超网络从一组多样化的权衡偏好中生成多个解，并通过最大化这些解定义的超体积指标来提升帕累托前沿的质量。在多个 MOO 机器学习任务上的实验结果表明，所提框架在生成权衡帕累托前沿方面显著优于基线方法。