This repository contains the code implementation of the paper Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver. In this paper, we propose a powerful RL-based constructive method called ICAM. When facing diverse geometric structures and patterns of instances across different scales, ICAM can effectively capture the instance-specific features (i.e., distance and scale) via the proposed instance-conditioned adaptation function. To make the model better aware of instance-specific information, we incorporate these features into the whole solution construction process (i.e., embedding, attention, and compatibility) via a powerful yet low-complexity instance-conditioned adaptation attention mechanism. Therefore, ICAM can directly generate promising solutions for instances across quite different scales, which improves the large-scale generalization performance for RL-based NCOs.

Python>=3.8
torch==2.0.1
numpy==1.24.4
tqdm

We don't use any hard-to-install packages. If any package is missing, just install it following the prompts.

We recommend using PyTorch 2.x for better GPU memory utilization and training (testing) acceleration. This code is trained and tested with PyTorch 2.0.1 (CUDA version 11.7). If you are using PyTorch 1.x, you may encounter OOM (i.e., Out of Memory) issues even with the same training configuration (unchanged batch size). For more characteristics of PyTorch 2.x, please refer to the PyTorch 2.x.

Please refer to the official instructions Previous PyTorch Versions to install the correct version of PyTorch, which is compatible with your CUDA version.

We provide the official instructions to install Torch 2.0.1 with CUDA 11.7:

# CUDA 11.7
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2

Note: The project's structure is clear, with code based on .py files that should be easy to read, understand, and run.

The code for each CO problem defaults to retaining the parameters used for training the pre-trained model and those used for testing. Please refer to our paper for training and testing settings.

To run the code and further verify the efficiency of our method, please follow these steps:

git clone https://github.com/CIAM-Group/ICAM.git
cd ICAM

We recommend creating a virtual environment first (e.g., using conda).

# Example using conda
conda create -n icam python=3.8
conda activate icam

# Install PyTorch (example for CUDA 11.7)
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2

# Install other dependencies
pip install numpy==1.24.4
pip install tqdm

ICAM is trained using reinforcement learning, and the random data generation method is demonstrated in three files, such as ICAM_TSP/TSProblemDef.py and ICAM_CVRP/CVRProblemDef.py.

# Example: Train the TSP model
cd ICAM_TSP
python tsp_train_vst_n100_to_n500.py

# Example: Train the CVRP model
cd ICAM_CVRP
python cvrp_train_vst_n100_to_n500.py

You can use our provided pre-trained models or your own trained models to run the evaluation script and verify the method's performance.

Models: All pre-trained models are placed in ./pretrained.

Data: Please download test sets from Google Drive https://drive.google.com/drive/folders/1B2qBj8rD5apvxaWuBsjOeBStOa_bMQu9?usp=sharing, and place them in ./data.

# Example: Evaluate on TSPs
cd ICAM_TSP
python tsp_test_main.py  # Testing model performance on synthetic data
python tsp_test_lib.py # Testing model performance on TSPLib

# Example: Evaluate on CVRPs
cd ICAM_CVRP
python cvrp_test_main.py  # Testing model performance on synthetic data
python cvrp_test_lib.py # Testing model performance on CVRPLib

We have verified the legality of the corresponding solutions for each problem. We will continue to strive to improve its clarity and welcome any minor errors in the code implementation.

🐛 If there are any issues in running or re-implementing the code, please contact the author Changliang Zhou via email ([email protected]) in a timely manner.

🤩🤩🤩 If this repository is helpful for your research, please cite our paper:

@article{zhou2024instance,
  title={Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver},
  author={Zhou, Changliang and Lin, Xi and Wang, Zhenkun and Tong, Xialiang and Yuan, Mingxuan and Zhang, Qingfu},
  journal={arXiv preprint arXiv:2405.01906},
  year={2024}
}

The code implementation of ICAM is based on the code of POMO and MatNet. Thank them for their implementations.

The code can only be used for non-commercial purposes. Please contact the authors if you want to use this code for business matters.