.png){kind=icon}
.png){kind=icon}
Return
Solving hypervolume scalarizations for MOCO problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.cor.2026.107578.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Multiobjective combinatorial optimization
Hypervolume scalarization
Representation of the nondominated set
Knapsack problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.3
Papers:
184
Citations:
0