Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2023
Abstract
Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta model, so as to speed up the training; meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 37th Conference on Neural Information Processing, New Orleans, United States, December 12-14
First Page
1
Last Page
25
Publisher
Neural information processing systems foundation
City or Country
California
Citation
CHEN, Jinbiao; ZHANG, Zizhen; YE, Te; CAO, Zhiguang; CHEN, Siyuan; and WANG, Jiahai.
Efficient meta neural heuristic for multi-objective combinatorial optimization. (2023). Proceedings of the 37th Conference on Neural Information Processing, New Orleans, United States, December 12-14. 1-25.
Available at: https://ink.library.smu.edu.sg/sis_research/8403
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.