Code for ICLR2022 Paper: Pareto Set Learning for Neural Multi-objective Combinatorial Optimization
It contains the training and testing codes for three multi-objective combinatorial optimization (MOCO) problems:
- Multi-Objective Travelling Salesman Problem (MOTSP)
- Multi-Objective Capacitated Vehicle Routing Problem (MOCVRP)
- Multi-Objective Knapsack Problem (MOKP)
This code is heavily based on the POMO repository, and it has been reorganized accroding to the new POMO version. The main changes include:
- Graph embedding has been removed.
- BatchNorm has been replaced by InstanceNorm.
Quick Start
- 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.
Reference
If our work is helpful for your research, please cite our paper:
@inproceedings{lin2022pareto,
title={Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization},
author={Xi Lin, Zhiyuan Yang, Qingfu Zhang},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=QuObT9BTWo}
}
If you find our code useful, please also consider citing the POMO paper:
@inproceedings{Kwon2020pomo,
title = {POMO: Policy Optimization with Multiple Optima for Reinforcement Learning},
author = {Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min},
booktitle = {Advances in Neural Information Processing Systems},
pages = {21188--21198},
volume = {33},
year = {2020}
}