Solving hypervolume scalarizations for MOCO problems
本文提出精确方法求解多目标组合优化问题的超体积标量化，通过特殊变换高效计算最优解，在背包问题实验中比现有方法快几个数量级。
Hypervolume scalarizations have emerged as a promising strategy to find efficient solutions to multiobjective combinatorial optimization problems. Despite their potential, the exact optimization of hypervolume scalarizations remains challenging. This paper introduces exact approaches that exploit particular transformations to compute optimal hypervolume-scalarized solutions efficiently. Extensive experiments on multiobjective knapsack problems show that our methods can improve upon the current state-of-the-art exact approaches that are based on straightforward linearizations, achieving improvements of several orders of magnitude in terms of problem size, number of objectives, and number of reference points.