Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2024
Abstract
The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS relies on the proper configuration of its selection and acceptance parameters, which is known to be a complex and resource-intensive task. To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search. The proposed method aims to learn, based on the state of the search, to configure ALNS for the next iteration to yield more effective solutions for the given optimization problem. We evaluate the proposed method on an orienteering problem with stochastic weights and time windows, as presented in an IJCAI competition. The results show that our approach outperforms vanilla ALNS, ALNS tuned with Bayesian optimization, and two state-of-the-art DRL approaches that were the winning methods of the competition, achieving this with significantly fewer training observations. Furthermore, we demonstrate several good properties of the proposed DR-ALNS method: it is easily adapted to solve different routing problems, its learned policies perform consistently well across various instance sizes, and these policies can be directly applied to different problem variants.
Keywords
Deep reinforcement learning, Adaptive large neighborhood search, Algorithm configuration
Discipline
Artificial Intelligence and Robotics | Computer Sciences
Publication
Proceedings of the 34th International Conference on Automated Planning and Scheduling (ICAPS-2024) : Banff, Alberta, Canada, Jun 1-6
Volume
34
First Page
475
Last Page
483
Identifier
10.1609/icaps.v34i1.31507
Publisher
PKP Publishing Services Network
City or Country
Banff, Canada
Citation
REIJNEN, Reijnen; ZHANG, Yingqian; LAU, Hoong Chuin; and BUKHSH, Zaharah.
Online control of adaptive large neighborhood search using deep reinforcement learning. (2024). Proceedings of the 34th International Conference on Automated Planning and Scheduling (ICAPS-2024) : Banff, Alberta, Canada, Jun 1-6. 34, 475-483.
Available at: https://ink.library.smu.edu.sg/sis_research/9893
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1609/icaps.v34i1.31507
