{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:49:03Z","timestamp":1771026543796,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":63,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819722747","type":"print"},{"value":"9789819722754","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2275-4_1","type":"book-chapter","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T19:02:10Z","timestamp":1713207730000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Review of Traveling Salesman Problem Solution Methods"],"prefix":"10.1007","author":[{"given":"Longrui","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8927-7359","authenticated-orcid":false,"given":"Xiyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4376-5228","authenticated-orcid":false,"given":"Zhaoqi","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8367-9146","authenticated-orcid":false,"given":"Sicong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2516-3692","authenticated-orcid":false,"given":"Jie","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"issue":"3731","key":"1_CR1","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1126\/science.153.3731.34","volume":"153","author":"R Bellman","year":"1966","unstructured":"Bellman, R.: Dynamic programming. Science 153(3731), 34\u201337 (1966)","journal-title":"Science"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Xu, S., Panwar, S.S., Kodialam, M., Lakshman, T.V.: Deep neural network approximated dynamic programming for combinatorial optimization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1684\u20131691 (2020)","DOI":"10.1609\/aaai.v34i02.5531"},{"issue":"4","key":"1_CR3","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1287\/opre.14.4.699","volume":"14","author":"EL Lawler","year":"1966","unstructured":"Lawler, E.L., Wood, D.E.: Branch-and-bound methods: a survey. Oper. Res. 14(4), 699\u2013719 (1966)","journal-title":"Oper. Res."},{"issue":"2","key":"1_CR4","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s10589-023-00474-3","volume":"85","author":"W Zhang","year":"2023","unstructured":"Zhang, W., Sauppe, J.J., Jacobson, S.H.: Results for the close-enough traveling salesman problem with a branch-and-bound algorithm. Comput. Optim. Appl. 85(2), 369\u2013407 (2023)","journal-title":"Comput. Optim. Appl."},{"issue":"4","key":"1_CR5","first-page":"74","volume":"32","author":"C Donog","year":"2019","unstructured":"Donog, C.: A relaxation algorithm for solving the traveling salesman problem. Shandong Sci. 32(4), 74\u201379 (2019)","journal-title":"Shandong Sci."},{"issue":"3","key":"1_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJCINI.2019070101","volume":"13","author":"T Weise","year":"2019","unstructured":"Weise, T., Jiang, Y., Qi, Q., Liu, W.: A branch-and-bound-based crossover operator for the traveling salesman problem. Int. J. Cogn. Inform. Nat. Intell. (IJCINI) 13(3), 1\u201318 (2019)","journal-title":"Int. J. Cogn. Inform. Nat. Intell. (IJCINI)"},{"issue":"1","key":"1_CR7","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66\u201373 (1992)","journal-title":"Sci. Am."},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Toathom, T., Champrasert, P.: The complete subtour order crossover in genetic algorithms for traveling salesman problem solving. In: 2022 37th International Technical Conference on Circuits\/Systems, Computers and Communications (ITC-CSCC), pp. 904\u2013907. IEEE (2022)","DOI":"10.1109\/ITC-CSCC55581.2022.9895081"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"109339","DOI":"10.1016\/j.asoc.2022.109339","volume":"127","author":"P Zhang","year":"2022","unstructured":"Zhang, P., Wang, J., Tian, Z., Sun, S., Li, J., Yang, J.: A genetic algorithm with jumping gene and heuristic operators for traveling salesman problem. Appl. Soft Comput. 127, 109339 (2022)","journal-title":"Appl. Soft Comput."},{"issue":"8","key":"1_CR10","first-page":"1811","volume":"34","author":"J Xu","year":"2022","unstructured":"Xu, J., Han, F., Liu, Q., Xue, X.: Bioinformation heuristic genetic algorithm for solving TSP. J. Syst. Simul. 34(8), 1811\u20131819 (2022)","journal-title":"J. Syst. Simul."},{"key":"1_CR11","unstructured":"Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano (1992)"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"4529","DOI":"10.1007\/s10489-020-01799-w","volume":"50","author":"K Yang","year":"2020","unstructured":"Yang, K., You, X., Liu, S., Pan, H.: A novel ant colony optimization based on game for traveling salesman problem. Appl. Intell. 50, 4529\u20134542 (2020)","journal-title":"Appl. Intell."},{"issue":"8","key":"1_CR13","doi-asserted-by":"publisher","first-page":"884","DOI":"10.3390\/e22080884","volume":"22","author":"P Stodola","year":"2020","unstructured":"Stodola, P., Michenka, K., Nohel, J., Rybansk\u00fd, M.: Hybrid algorithm based on ant colony optimization and simulated annealing applied to the dynamic traveling salesman problem. Entropy 22(8), 884 (2020)","journal-title":"Entropy"},{"key":"1_CR14","doi-asserted-by":"publisher","first-page":"108653","DOI":"10.1016\/j.asoc.2022.108653","volume":"120","author":"R Skinderowicz","year":"2022","unstructured":"Skinderowicz, R.: Improving ant colony optimization efficiency for solving large tsp instances. Appl. Soft Comput. 120, 108653 (2022)","journal-title":"Appl. Soft Comput."},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"109943","DOI":"10.1016\/j.asoc.2022.109943","volume":"133","author":"W Li","year":"2023","unstructured":"Li, W., Wang, C., Huang, Y., Cheung, Y.M.: Heuristic smoothing ant colony optimization with differential information for the traveling salesman problem. Appl. Soft Comput. 133, 109943 (2023)","journal-title":"Appl. Soft Comput."},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Soh, M., Tsofack, B.N., Djamegni, C.T.: A hybrid algorithm based on multi-colony ant optimization and lin-kernighan for solving the traveling salesman problem. Rev. Afr. Recherche Inform. Math. Appl. 35 (2022)","DOI":"10.46298\/arima.8660"},{"key":"1_CR17","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849\u2013872 (2019)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"8","key":"1_CR18","first-page":"2265","volume":"41","author":"A Tang","year":"2021","unstructured":"Tang, A., Han, T., Xu, D., Xie, L.: Chaotic elite Harris\u2019 hawk optimization algorithm. Computer Applications 41(8), 2265\u20132272 (2021)","journal-title":"Computer Applications"},{"issue":"3","key":"1_CR19","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1007\/s10586-021-03304-5","volume":"25","author":"FS Gharehchopogh","year":"2022","unstructured":"Gharehchopogh, F.S., Abdollahzadeh, B.: An efficient Harris hawk optimization algorithm for solving the travelling salesman problem. Clust. Comput. 25(3), 1981\u20132005 (2022)","journal-title":"Clust. Comput."},{"key":"1_CR20","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.future.2020.04.008","volume":"111","author":"H Chen","year":"2020","unstructured":"Chen, H., Heidari, A.A., Chen, H., Wang, M., Pan, Z., Gandomi, A.H.: Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies. Futur. Gener. Comput. Syst. 111, 175\u2013198 (2020)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"1_CR21","unstructured":"Hussien, A.G., Amin, M.: A self-adaptive harris hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. Int. J. Mach. Learn. Cybern. 1\u201328 (2022)"},{"key":"1_CR22","unstructured":"Basturk, B.: An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE Swarm Intelligence Symposium, Indianapolis, USA, vol. 2006, p. 12 (2006)"},{"issue":"2","key":"1_CR23","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","volume":"2","author":"XS Yang","year":"2010","unstructured":"Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspir. Comput. 2(2), 78\u201384 (2010)","journal-title":"Int. J. Bio-Inspir. Comput."},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Yang, X. S., Deb, S.: Cuckoo search via L\u00e9vy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210\u2013214. IEEE (2009)","DOI":"10.1109\/NABIC.2009.5393690"},{"issue":"3","key":"1_CR25","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching\u2013learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303\u2013315 (2011)","journal-title":"Comput. Aided Des."},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Tang, R., Fong, S., Yang, X. S., Deb, S.: Wolf search algorithm with ephemeral memory. In: Seventh International Conference on Digital Information Management (ICDIM 2012), pp. 165\u2013172. IEEE (2012)","DOI":"10.1109\/ICDIM.2012.6360147"},{"issue":"3","key":"1_CR27","doi-asserted-by":"publisher","first-page":"591","DOI":"10.3233\/AIC-140652","volume":"28","author":"H Emami","year":"2015","unstructured":"Emami, H., Derakhshan, F.: Election algorithm: a new socio-politically inspired strategy. AI Commun. 28(3), 591\u2013603 (2015)","journal-title":"AI Commun."},{"key":"1_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46\u201361 (2014)","journal-title":"Adv. Eng. Softw."},{"key":"1_CR29","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51\u201367 (2016)","journal-title":"Adv. Eng. Softw."},{"key":"1_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","volume":"169","author":"A Askarzadeh","year":"2016","unstructured":"Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1\u201312 (2016)","journal-title":"Comput. Struct."},{"key":"1_CR31","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163\u2013239 (2017)","journal-title":"Adv. Eng. Softw."},{"key":"1_CR32","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.future.2017.10.052","volume":"81","author":"M Kumar","year":"2018","unstructured":"Kumar, M., Kulkarni, A.J., Satapathy, S.C.: Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Futur. Gener. Comput. Syst. 81, 252\u2013272 (2018)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"1_CR33","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.advengsoft.2017.01.004","volume":"105","author":"S Saremi","year":"2017","unstructured":"Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimization algorithm: theory and application. Adv. Eng. Softw. 105, 30\u201347 (2017)","journal-title":"Adv. Eng. Softw."},{"key":"1_CR34","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/s00500-018-3102-4","volume":"23","author":"S Arora","year":"2019","unstructured":"Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23, 715\u2013734 (2019)","journal-title":"Soft. Comput."},{"key":"1_CR35","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.swevo.2018.02.013","volume":"44","author":"M Jain","year":"2019","unstructured":"Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol. Comput. 44, 148\u2013175 (2019)","journal-title":"Swarm Evol. Comput."},{"key":"1_CR36","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/s00500-019-03949-w","volume":"24","author":"AF Nematollahi","year":"2020","unstructured":"Nematollahi, A.F., Rahiminejad, A., Vahidi, B.: A novel meta-heuristic optimization method based on golden ratio in nature. Soft. Comput. 24, 1117\u20131151 (2020)","journal-title":"Soft. Comput."},{"issue":"1","key":"1_CR37","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","volume":"8","author":"J Xue","year":"2020","unstructured":"Xue, J., Shen, B.: A novel swarm intelligence optimization approach: sparrow search algorithm. Syst. Sci. Control Eng. 8(1), 22\u201334 (2020)","journal-title":"Syst. Sci. Control Eng."},{"issue":"3","key":"1_CR38","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/BF00339943","volume":"52","author":"JJ Hopfield","year":"1985","unstructured":"Hopfield, J.J., Tank, D.W.: \u201cNeural\u201d computation of decisions in optimization problems. Biol. Cybern. 52(3), 141\u2013152 (1985)","journal-title":"Biol. Cybern."},{"issue":"2","key":"1_CR39","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1016\/j.ejor.2020.07.063","volume":"290","author":"B Yoshua","year":"2021","unstructured":"Yoshua, B., Andrea, L., Antoine, P.: Machine learning for combinatorial optimization: a methodological tour d\u2019horizon. Eur. J. Oper. Res. 290(2), 405\u2013421 (2021)","journal-title":"Eur. J. Oper. Res."},{"key":"1_CR40","unstructured":"Kim, M., Park, J., Park, J.: Sym-nco: leveraging symmetricity for neural combinatorial optimization. arXiv preprint arXiv:2205.13209 (2022)"},{"key":"1_CR41","unstructured":"Ouyang, W., Wang, Y., Weng, P., Han, S.: Generalization in deep RL for TSP problems via equivariance and local search. arXiv preprint arXiv:2110.03595 (2021)"},{"key":"1_CR42","doi-asserted-by":"publisher","first-page":"102005","DOI":"10.1016\/j.aei.2023.102005","volume":"56","author":"Y Xu","year":"2023","unstructured":"Xu, Y., Fang, M., Chen, L., Du, Y., Xu, G., Zhang, C.: Shared dynamics learning for large-scale traveling salesman problem. Adv. Eng. Inform. 56, 102005 (2023)","journal-title":"Adv. Eng. Inform."},{"issue":"8","key":"1_CR43","doi-asserted-by":"publisher","first-page":"8152","DOI":"10.3934\/mbe.2022381","volume":"19","author":"T Fei","year":"2022","unstructured":"Fei, T., Wu, X., Zhang, L., Zhang, Y., Chen, L.: Research on improved ant colony optimization for the traveling salesman problem. Math. Biosci. Eng. 19(8), 8152\u20138186 (2022)","journal-title":"Math. Biosci. Eng."},{"key":"1_CR44","unstructured":"Joshi, C.K., Laurent, T., Bresson, X.: On learning paradigms for the traveling salesman problem. arXiv preprint arXiv:1910.07210 (2019)"},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Prates, M., Avelar, P.H., Lemos, H., Lamb, L.C., Vardi, M.Y.: Learning to solve NP-complete problems: a graph neural network for decision TSP. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 4731\u20134738 (2019)","DOI":"10.1609\/aaai.v33i01.33014731"},{"key":"1_CR46","unstructured":"Kim, M., Jiwoo, S.O.N., Kim, H., Park, J.: Scale-conditioned adaptation for large scale combinatorial optimization. In: NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications (2022)"},{"issue":"4","key":"1_CR47","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s42256-022-00468-6","volume":"4","author":"MJ Schuetz","year":"2022","unstructured":"Schuetz, M.J., Brubaker, J.K., Katzgraber, H.G.: Combinatorial optimization with physics-inspired graph neural networks. Nat. Mach. Intell. 4(4), 367\u2013377 (2022)","journal-title":"Nat. Mach. Intell."},{"key":"1_CR48","unstructured":"Kool, W., Van Hoof, H., Welling, M.: Attention, learn to solve routing problems!. arXiv preprint arXiv:1803.08475 (2018)"},{"key":"1_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-319-93031-2_12","volume-title":"Integration of Constraint Programming, Artificial Intelligence, and Operations Research","author":"M Deudon","year":"2018","unstructured":"Deudon, M., Cournut, P., Lacoste, A., Adulyasak, Y., Rousseau, L.-M.: Learning heuristics for the TSP by policy gradient. In: van Hoeve, W.-J. (ed.) CPAIOR 2018. LNCS, vol. 10848, pp. 170\u2013181. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93031-2_12"},{"key":"1_CR50","unstructured":"Cappart, Q., Moisan, T., Rousseau, L.M., et al.: Combining reinforcement learning and constraint programming for combinatorial optimization. arXiv:2006.01610 (2018)"},{"key":"1_CR51","unstructured":"Bresson, X., Laurent, T.: The transformer network for the traveling salesman problem. arXiv preprint arXiv:2103.03012 (2021)"},{"key":"1_CR52","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/978-3-030-77004-4_15","volume-title":"Pattern Recognition","author":"O Guti\u00e9rrez","year":"2021","unstructured":"Guti\u00e9rrez, O., Zamora, E., Menchaca, R.: Graph representation for learning the traveling salesman problem. In: Roman-Rangel, E., Kuri-Morales, \u00c1.F., Mart\u00ednez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-L\u00f3pez, Jos\u00e9 Arturo. (eds.) MCPR 2021. LNCS, vol. 12725, pp. 153\u2013162. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77004-4_15"},{"key":"1_CR53","doi-asserted-by":"crossref","unstructured":"Zheng, J., He, K., Zhou, J., Jin, Y., Li, C.M.: Combining reinforcement learning with Lin-Kernighan-Helsgaun algorithm for the traveling salesman problem. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, vol. 14, pp. 12445\u201312452 (2021)","DOI":"10.1609\/aaai.v35i14.17476"},{"key":"1_CR54","doi-asserted-by":"crossref","unstructured":"Fu, Z. H., Qiu, K. B., Zha, H.: Generalize a small pre-trained model to arbitrarily large TSP instances. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, pp. 7474\u20137482 (2021)","DOI":"10.1609\/aaai.v35i8.16916"},{"key":"1_CR55","doi-asserted-by":"publisher","first-page":"108397","DOI":"10.1016\/j.compeleceng.2022.108397","volume":"104","author":"C Fu","year":"2022","unstructured":"Fu, C., et al.: A learning approach for multi-agent travelling problem with dynamic service requirement in mobile IoT. Comput. Electr. Eng. 104, 108397 (2022)","journal-title":"Comput. Electr. Eng."},{"key":"1_CR56","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-031-30105-6_44","volume-title":"ICONIP 2022, Part I","author":"H Ma","year":"2023","unstructured":"Ma, H., Tu, S., Xu, L.: IA-CL: a deep bidirectional competitive learning method for traveling salesman problem. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) ICONIP 2022, Part I. LNCS, vol. 13623, pp. 525\u2013536. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-30105-6_44"},{"key":"1_CR57","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/978-3-031-24866-5_25","volume-title":"LION 2022","author":"E Gaile","year":"2023","unstructured":"Gaile, E., Draguns, A., Ozoli\u0146\u0161, E., Freivalds, K.: Unsupervised training for neural TSP solver. In: Simos, D.E., Rasskazova, V.A., Archetti, F., Kotsireas, I.S., Pardalos, P.M. (eds.) LION 2022. LNCS, vol. 13621, pp. 334\u2013346. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-24866-5_25"},{"issue":"8","key":"1_CR58","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1007\/s13042-022-01516-8","volume":"13","author":"N Sultana","year":"2022","unstructured":"Sultana, N., Chan, J., Sarwar, T., Qin, A.K.: Learning to optimise general TSP instances. Int. J. Mach. Learn. Cybern. 13(8), 2213\u20132228 (2022)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"1_CR59","doi-asserted-by":"crossref","unstructured":"Jin, Y., et al.: PointerFormer: deep reinforced multi-pointer transformer for the traveling salesman problem. arXiv preprint arXiv:2304.09407 (2023)","DOI":"10.1609\/aaai.v37i7.25982"},{"issue":"3","key":"1_CR60","first-page":"420","volume":"58","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Chen, Z., Yang, X., Wu, Z.: Deep reinforcement learning combined with graph attention model to solve TSP. J. Nanjing Univ. (Nat. Sci.) 58(3), 420\u2013429 (2022)","journal-title":"J. Nanjing Univ. (Nat. Sci.)"},{"issue":"7","key":"1_CR61","first-page":"1516","volume":"16","author":"S Zhang","year":"2022","unstructured":"Zhang, S., Guo, G.: A review of the multi-traveling salesman model and its applications. Comput. Sci. Explor. 16(7), 1516 (2022)","journal-title":"Comput. Sci. Explor."},{"key":"1_CR62","doi-asserted-by":"publisher","first-page":"100379","DOI":"10.1016\/j.cosrev.2021.100379","volume":"40","author":"S Dong","year":"2021","unstructured":"Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 100379 (2021)","journal-title":"Comput. Sci. Rev."},{"issue":"2","key":"1_CR63","first-page":"261","volume":"16","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Chen, Z., Wu, Z., Gao, Y.: Review of reinforcement learning for combinatorial optimization problem. J. Front. Comput. Sci. Technol. 16(2), 261\u2013279 (2022)","journal-title":"J. Front. Comput. Sci. Technol."}],"container-title":["Communications in Computer and Information Science","Bio-Inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2275-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T19:13:45Z","timestamp":1713208425000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2275-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819722747","9789819722754"],"references-count":63,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2275-4_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"16 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2023.bicta.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"168","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}