Code for NeurIPS2023 Paper: Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Quick Start For NHDE-P
- To train a model, such as MOTSP with 20 nodes, run train_motsp_n20.py in the corresponding folder.
- To test a model, such as MOTSP with 20 nodes, run test_motsp_n20.py in the corresponding folder.
- Pretrained models for each problem can be found in the result folder.
Quick Start For NHDE-M
- To train a model, such as MOTSP with 20 nodes, set TSP_SIZE=20 and MODE=1 in HYPER_PARAMS.py, and then run run.py in the corresponding folder.
- To fine-tune and test a model, such as MOTSP with 20 nodes, set TSP_SIZE=20 and MODE=2 in HYPER_PARAMS.py, and then run run.py in the corresponding folder.
- Pretrained models for each problem can be found in the result folder.
Reference
If our work is helpful for your research, please cite our paper:
@inproceedings{chen2023neural,
title={Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement},
author={Chen, Jinbiao and Zhang, Zizhen and Cao, Zhiguang and Wu, Yaoxin and Ma, Yining and Ye, Te and Wang, Jiahai},
booktitle={Advances in Neural Information Processing Systems},
year={2023},
}