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Pareto Front Learning (PFL) was recently introduced as an effective approachto obtain a mapping function from a given trade-off vector to a solution on thePareto front, which solves the multi-objective optimization (MOO) problem. Dueto the inherent trade-off between conflicting objectives, PFL offers a flexibleapproach in many scenarios in which the decision makers can not specify thepreference of one Pareto solution over another, and must switch between themdepending on the situation. However, existing PFL methods ignore therelationship between the solutions during the optimization process, whichhinders the quality of the obtained front. To overcome this issue, we propose anovel PFL framework namely PHN-HVI, which employs a hypernetwork to generatemultiple solutions from a set of diverse trade-off preferences and enhance thequality of the Pareto front by maximizing the Hypervolume indicator defined bythese solutions. The experimental results on several MOO machine learning tasksshow that the proposed framework significantly outperforms the baselines inproducing the trade-off Pareto front.

Improving Pareto Front Learning via Multi-Sample Hypernetworks