Awesome

NHDE

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},
}