Online control of adaptive large neighborhood search using deep reinforcement learning
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 resourc...
Saved in:
Main Authors: | , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9893 https://ink.library.smu.edu.sg/context/sis_research/article/10893/viewcontent/2211.00759v3.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-10893 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-108932025-01-02T09:05:37Z Online control of adaptive large neighborhood search using deep reinforcement learning REIJNEN, Reijnen ZHANG, Yingqian LAU, Hoong Chuin BUKHSH, Zaharah 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. 2024-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9893 info:doi/10.1609/icaps.v34i1.31507 https://ink.library.smu.edu.sg/context/sis_research/article/10893/viewcontent/2211.00759v3.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Deep reinforcement learning Adaptive large neighborhood search Algorithm configuration Artificial Intelligence and Robotics Computer Sciences |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Deep reinforcement learning Adaptive large neighborhood search Algorithm configuration Artificial Intelligence and Robotics Computer Sciences |
spellingShingle |
Deep reinforcement learning Adaptive large neighborhood search Algorithm configuration Artificial Intelligence and Robotics Computer Sciences REIJNEN, Reijnen ZHANG, Yingqian LAU, Hoong Chuin BUKHSH, Zaharah Online control of adaptive large neighborhood search using deep reinforcement learning |
description |
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. |
format |
text |
author |
REIJNEN, Reijnen ZHANG, Yingqian LAU, Hoong Chuin BUKHSH, Zaharah |
author_facet |
REIJNEN, Reijnen ZHANG, Yingqian LAU, Hoong Chuin BUKHSH, Zaharah |
author_sort |
REIJNEN, Reijnen |
title |
Online control of adaptive large neighborhood search using deep reinforcement learning |
title_short |
Online control of adaptive large neighborhood search using deep reinforcement learning |
title_full |
Online control of adaptive large neighborhood search using deep reinforcement learning |
title_fullStr |
Online control of adaptive large neighborhood search using deep reinforcement learning |
title_full_unstemmed |
Online control of adaptive large neighborhood search using deep reinforcement learning |
title_sort |
online control of adaptive large neighborhood search using deep reinforcement learning |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2024 |
url |
https://ink.library.smu.edu.sg/sis_research/9893 https://ink.library.smu.edu.sg/context/sis_research/article/10893/viewcontent/2211.00759v3.pdf |
_version_ |
1821237276898754560 |