  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.
