A Variable Strategy Reinforced Lin-Kernighan-Helsgaun Algorithm (VSR-LKH)
This repository contains the code to the VSR-LKH algorithm for the TSP proposed in our paper:
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem (AAAI 2021)
Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-Min Li
Installation
On a Unix/Linux machine execute the following commands:
unzip VSR-LKH-main.zip
cd VSR-LKH-main
make
An executable file called LKH will now be available in the directory VSR-LKH-main.
Then enter the .par file name corresponding to the TSP instance, such as u574.par, to run the program.
File Description
VSR-LKH was achieved on top of the famous TSP heuristic, Lin-Kernighan-Helsgaun (LKH) algorithm. You can learn LKH from its open source website, http://akira.ruc.dk/~keld/research/LKH/, and understand the effect of each file and the meaning of the parameters from the PDF files in the directory DOC. The description of some files that differ between VSR-LKH and LKH is as follows:
- The statement of reinforcement learning parameters and Q-value: LKH.h
- The reinforcement learning process in k-opt: BestKOptMove.c (for parameters PATHING_A = 2, PATHING_C = 3), Best5OptMove.c (for parameters PATHING_A = 1, PATHING_C = 0).
- The initialization of parameters: ReadParameters.c
- The reinforcement learning parameters adaptation process: FindTour.c
Data
We give three TSP instances (u574, u1060, rl11849) as examples. All the TSPLIB Format instance (.tsp) can be calculated by VSR-LKH. The all 111 TSPLIB instances can be found in the directory TSPLIB_DATA or http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/.
Contact
Questions and suggestions can be sent to jzzheng@hust.edu.cn.
Citation
If you find this code and data useful, please consider citing the original work by authors:
@inproceedings{zheng2021RL-TSP,
title={Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem},
author={Jiongzhi Zheng and Kun He and Jianrong Zhou and Yan Jin and Chu-Min Li},
booktitle={The Thirty-Fifth AAAI Conference on Artificial Intelligence},
pages={12445--12452},
year={2021}
}