Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2023
Abstract
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.
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
22
Publisher
Neural information processing systems foundation
City or Country
California
Citation
CHEN, Jinbiao; ZHANG, Zizhen; CAO, Zhiguang; WU, Yaoxin; MA, Yining; YE, Te; and WANG, Jiahai.
Neural multi-objective combinatorial optimization with diversity enhancement. (2023). Proceedings of the 37th Conference on Neural Information Processing, New Orleans, United States, December 12-14. 1-22.
Available at: https://ink.library.smu.edu.sg/sis_research/8402
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.