Gliding over the Pareto Front with Uniform Designs

Multiobjective optimization (MOO) plays a critical role in various real-world domains. A major challenge therein is generating K uniform Pareto-optimal solutions to represent the entire Pareto front. To address this issue, this paper firstly introduces \emph{fill distance} to evaluate the K design points, which provides a quantitative metric for the representativeness of the design. However, directly specifying the optimal design that minimizes the fill distance is nearly intractable due to the nested \min-\max-\min optimization problem. To address this, we propose a surrogate max-packing'' design for the fill distance design, which is easier to optimize and leads to a rate-optimal design with a fill distance at most 4\times the minimum value.