{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T08:31:39Z","timestamp":1773477099074,"version":"3.50.1"},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"2022 Youth Project of Guangdong Basic and Applied Basic Research Fund","award":["2022A1515110437"],"award-info":[{"award-number":["2022A1515110437"]}]},{"name":"2018 Higher Education of Guangdong Key Platforms and Scientific Research Projects","award":["2018KQNCX251"],"award-info":[{"award-number":["2018KQNCX251"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3387851","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T19:05:26Z","timestamp":1712862326000},"page":"52902-52910","source":"Crossref","is-referenced-by-count":1,"title":["ASPDD: An Adaptive Knowledge Distillation Framework for TSP Generalization Problems"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4016-5646","authenticated-orcid":false,"given":"Sisi","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Huizhou University, Huizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5528-965X","authenticated-orcid":false,"given":"Rongye","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Huizhou University, Huizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403356"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s12351-020-00600-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1287\/inte.2020.1047"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/03611981221117157"},{"key":"ref5","volume-title":"Python Wrapper Around the Concorde TSP Solver","author":"Kwon","year":"2022"},{"key":"ref6","volume-title":"Gurobi Optimizer Reference Manual","year":"2021"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2018.06.039"},{"key":"ref8","article-title":"Learning permutations with sinkhorn policy gradient","author":"Emami","year":"2018","journal-title":"arXiv:1805.07010"},{"key":"ref9","article-title":"Learning the multiple traveling salesmen problem with permutation invariant pooling networks","author":"Kaempfer","year":"2018","journal-title":"arXiv:1803.09621"},{"key":"ref10","article-title":"An efficient graph convolutional network technique for the travelling salesman problem","author":"Joshi","year":"2019","journal-title":"arXiv:1906.01227"},{"key":"ref11","first-page":"1","article-title":"Learning to perform local rewriting for combinatorial optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Chen"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI50451.2021.9659970"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref14","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00309"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20862-1_23"},{"key":"ref17","article-title":"Attention, learn to solve routing problems!","author":"Kool","year":"2018","journal-title":"arXiv:1803.08475"},{"key":"ref18","first-page":"11096","article-title":"Learning to iteratively solve routing problems with dual-aspect collaborative transformer","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Ma"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref20","first-page":"31226","article-title":"Learning generalizable models for vehicle routing problems via knowledge distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Bi"},{"key":"ref21","first-page":"10418","article-title":"Learning collaborative policies to solve NP-hard routing problems","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kim"},{"key":"ref22","first-page":"1","article-title":"Pointer networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Vinyals"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3000236"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16916"},{"key":"ref25","article-title":"Neural combinatorial optimization with reinforcement learning","author":"Bello","year":"2016","journal-title":"arXiv:1611.09940"},{"key":"ref26","first-page":"1","article-title":"Learning combinatorial optimization algorithms over graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Khalil"},{"key":"ref27","first-page":"21188","article-title":"POMO: Policy optimization with multiple optima for reinforcement learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Kwon"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_17"},{"key":"ref29","first-page":"1","article-title":"Stochastic neighbor embedding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"15","author":"Hinton"},{"issue":"11","key":"ref30","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00280"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1002\/SERIES1345"},{"key":"ref33","volume-title":"Tensors and Dynamic Neural Networks in Python With Strong GPU Acceleration","year":"2023"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10496677.pdf?arnumber=10496677","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T04:32:18Z","timestamp":1713414738000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10496677\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3387851","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}