Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2021

Abstract

Objective-space decomposition algorithms (ODAs) are widely studied for solvingmulti-objective integer programs. However, they often encounter difficulties inhandling scalarized problems, which could cause infeasibility or repetitive nondominatedpoints and thus induce redundant runtime. To mitigate the issue, we presenta graph neural network (GNN) based method to learn the reduction rule in the ODA.We formulate the algorithmic procedure of generic ODAs as a Markov decisionprocess, and parameterize the policy (reduction rule) with a novel two-stage GNNto fuse information from variables, constraints and especially objectives for betterstate representation. We train our model with imitation learning and deploy it ona state-of-the-art ODA. Results show that our method significantly improves thesolving efficiency of the ODA. The learned policy generalizes fairly well to largerproblems or more objectives, and the proposed GNN outperforms existing ones forinteger programming in terms of test and generalization accuracy.

Discipline

Software Engineering

Publication

Proceedings of the 36th Conference on Neural Information Processing Systems, NeurIPS 2022; New Orleans, USA, Nov 28- Dec 9

Volume

35

ISBN

9781713871088

Publisher

Neural information processing systems foundation

City or Country

California

Copyright Owner and License

Authors

Share

COinS