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Dual hybrid frameworks combining graph convolutional network with decoding for covering location problem

文献类型：期刊论文

作者Zhang, Yao6; Wang, Shaohua1,7,8; Liang, Haojian2; Li, Xiao3; Wang, Zhenbo4; Lu, Hao5
刊名ISCIENCE
出版日期2024-05-17
卷号27期号:5页码:20
DOI10.1016/j.isci.2024.109803
英文摘要The Covering Location Problem (CLP) is widely used for the efficient facility distribution. However, existing algorithms for this problem suffer from long computation times or suboptimal solutions. To address this, we propose two methods based on graph convolutional networks (GCN) to solve two types of covering location problems: the location set covering problem and the maximum covering location problem. The first method, GCN-Greedy, is a supervised algorithm that synergized with the Greedy algorithm as decoder. It designs a specialized loss function to train the model, tailored to the characteristics of the two covering location problems. The second method, reinforcement learning based on GCN with autoregressive decoder (GCN-AR-RL), represents a reinforcement learning framework that integrates a GCN encoder with an auto -regressive decoder. The experimental results of these models demonstrate the remarkable accuracy and performance advantages. Additionally, we apply these two models to the realistic dataset and achieve good performance.
WOS关键词SET ; ALGORITHM ; SEARCH ; MODEL ; OPTIMIZATION ; ENUMERATION
资助项目National Key R&D Program of China[2021YFB1407002] ; Talent Introduction Program Youth Project of the Chinese Academy of Sciences[E2Z10501] ; Innovation group project of the Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences[E33D0201-5] ; Henan Zhongmu County Research Project[E3C1050101] ; CBAS project 2023, Remote Sensing Big Data Analystics Project[E3E2051401] ; Beijing Chaoyang District Collaborative Innovation Project[E2DZ050100]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001240607200001
出版者CELL PRESS
资助机构National Key R&D Program of China ; Talent Introduction Program Youth Project of the Chinese Academy of Sciences ; Innovation group project of the Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences ; Henan Zhongmu County Research Project ; CBAS project 2023, Remote Sensing Big Data Analystics Project ; Beijing Chaoyang District Collaborative Innovation Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/206694]  
专题区域可持续发展分析与模拟院重点实验室_外文论文
通讯作者Wang, Shaohua
作者单位1.Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China
2.Jilin Univ, Sch Artificial Intelligence, Changchun 130012, Peoples R China
3.Lanzhou Jiaotong Univ, Lanzhou 730070, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
5.SuperMap Software Co Ltd, Beijing 100015, Peoples R China
6.Beihang Univ, Sch Software, Beijing 100191, Peoples R China
7.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Remote Sensing & Digital Earth, Beijing 100094, Peoples R China
8.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yao,Wang, Shaohua,Liang, Haojian,et al. Dual hybrid frameworks combining graph convolutional network with decoding for covering location problem[J]. ISCIENCE,2024,27(5):20.
APA Zhang, Yao,Wang, Shaohua,Liang, Haojian,Li, Xiao,Wang, Zhenbo,&Lu, Hao.(2024).Dual hybrid frameworks combining graph convolutional network with decoding for covering location problem.ISCIENCE,27(5),20.
MLA Zhang, Yao,et al."Dual hybrid frameworks combining graph convolutional network with decoding for covering location problem".ISCIENCE 27.5(2024):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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