EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 59 / No 2 (March-April 2025) RAIRO-Oper. Res., 59 2 (2025) 1199-1213 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 2, March-April 2025
Page(s) 1199 - 1213
DOI https://doi.org/10.1051/ro/2025031
Published online 25 April 2025
  • Y. Hu, Z. Zhang, Y. Yao, X. Huyan, X. Zhou and W.S. Lee, A bidirectional graph neural network for traveling salesman problems on arbitrary symmetric graphs. Eng. App. Artif. Intell. 97 (2021) 104061. [CrossRef] [Google Scholar]
  • J. Li, M. Liu and P. Liu, Route optimization of multi-vehicle cold chain logistics for fresh agricultural products. J. China Agr. Univ. 26 (2021) 115. [Google Scholar]
  • H. Katagiri, G. Qingqiang, W. Bin, T. Muranaka, H. Hamori and K. Kato, Path optimization for electrical pcb inspections with alignment operations using multiple cameras. Proc. Comput. Sci. 60 (2015) 1051–1060. [CrossRef] [Google Scholar]
  • Z. Liu, H. Lou, K. Xie, H. Wang, N. Chen, O.M. Aparicio, M.Q. Zhang, R. Jiang and T. Chen, Reconstructing cell cycle pseudo time-series via single-cell transcriptome data. Nat. Commun. 8 (2017) 22. [CrossRef] [Google Scholar]
  • M. Dell’Amico, R. Montemanni and S. Novellani, Algorithms based on branch and bound for the flying sidekick traveling salesman problem. Omega 104 (2021) 102493. [CrossRef] [Google Scholar]
  • P. Bouman, N. Agatz and M. Schmidt, Dynamic programming approaches for the traveling salesman problem with drone. Networks 72 (2018) 528–542. [CrossRef] [MathSciNet] [Google Scholar]
  • C.C. Murray and R. Raj, The multiple flying sidekicks traveling salesman problem: parcel delivery with multiple drones. Transp. Res. Part C Emerg. Technol. 110 (2020) 368–398. [CrossRef] [Google Scholar]
  • A. Bandyopadhyay and F. Sajadi, On the nearest-neighbor algorithm for the mean-field traveling salesman problem. J. Appl. Probab. 51 (2014) 106–117. [CrossRef] [MathSciNet] [Google Scholar]
  • A. Paul, D. Freund, A. Ferber, D.B. Shmoys and D.P. Williamson, Budgeted prize-collecting traveling salesman and minimum spanning tree problems. Math. Oper. Res. 45 (2020) 576–590. [CrossRef] [MathSciNet] [Google Scholar]
  • Y. Dong, Q. Wu and J. Wen, An improved shuffled frog-leaping algorithm for the minmax multiple traveling salesman problem. Neural Comput. App. 33 (2021) 17057–17069. [CrossRef] [Google Scholar]
  • G.K. Jati and Suyanto, Evolutionary discrete firefly algorithm for travelling salesman problem, in International Conference on Adaptive and Intelligent Systems. Springer (2011) 393–403. [Google Scholar]
  • K. Panwar and K. Deep, Discrete grey wolf optimizer for symmetric travelling salesman problem. Appl. Soft Comput. 105 (2021) 107298. [CrossRef] [Google Scholar]
  • A. Ouaarab, B. Ahiod and X.-S. Yang, Random-key cuckoo search for the travelling salesman problem. Soft Comput. 19 (2015) 1099–1106. [CrossRef] [Google Scholar]
  • Y. Saji and M.E. Riffi, A novel discrete bat algorithm for solving the travelling salesman problem. Neural Comput. App. 27 (2016) 1853–1866. [CrossRef] [Google Scholar]
  • M. Akram and A. Habib, Hybridizing simulated annealing and genetic algorithms with pythagorean fuzzy uncertainty for traveling salesman problem optimization. J. Appl. Math. Comput. 69 (2023) 4451–4497. [CrossRef] [MathSciNet] [Google Scholar]
  • T. Stutzle and H.H. Hoos, Max-min ant system. Future Gener. Comput. Syst. 16 (2000) 889–914. [CrossRef] [Google Scholar]
  • T.A.O. Li-hua, M.A. Zhen-nan, S.H.I. Peng-tao and W.A.N.G. Rui-feng, Dynamic ant colony genetic algorithm based on TSP. Mach. Des. Manuf. 12 (2019) 147–154. [Google Scholar]
  • J. Amara, T.M. Hamdani and A.M. Alimi, A new hybrid discrete bat algorithm for traveling salesman problem using ordered crossover and 3-opt operators for bat’s local search, in 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE (2015) 154–159. [Google Scholar]
  • Q. He, Y. Wu and T. Xu, Application of improved genetic simulated annealing algorithm in TSP optimization. Control Decis. 33 (2018) 219. [Google Scholar]
  • X. Chen, Research on network optimization based on simulated annealing genetic algorithm, in 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017). Atlantis Press (2017) 1349–1354. [Google Scholar]
  • Y. Peng, X. Xiao and W. Wei, New fuzzy adaptive simulated annealing genetic algorithm. Control Decis. 24 (2009) 843–848+853. [MathSciNet] [Google Scholar]
  • C. Wang, M. Lin, Y. Zhong and H. Zhang, Solving travelling salesman problem using multiagent simulated annealing algorithm with instance-based sampling. Int. J. Comput. Sci. Math. 6 (2015) 336–353. [CrossRef] [MathSciNet] [Google Scholar]
  • W.H. Yuan, X.M. You, S. Liu and Y. Zhu, Adaptive simulated annealing ant colony algorithm for solving TSP problem. Comput. App. Softw. 35 (2018) 261–266. [Google Scholar]
  • A. Colorni, M. Dorigo and V. Maniezzo, Distributed optimization by ant colonies, in Proceedings of the First European Conference on Artificial Life. Vol. 142. Paris, France (1991) 134–142. [Google Scholar]
  • N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E. Teller, Equation of state calculations by fast computing machines. J. Chem. Phys. 21 (1953) 1087–1092. [Google Scholar]
  • S. Kirkpatrick, C.D. Gelatt Jr and M.P. Vecchi, Optimization by simulated annealing a preliminary computational study for the TSP. Science 220 (1983) 671–680. [CrossRef] [MathSciNet] [Google Scholar]
  • F.-T. Lin, C.-Y. Kao and C.-C. Hsu, Applying the genetic approach to simulated annealing in solving some np-hard problems. IEEE Trans. Syst. Man Cybern. 23 (1993) 1752–1767. [CrossRef] [Google Scholar]
  • T. Hao, W. Yingnian, Z. Jiaxing and Z. Jing, Study on a hybrid algorithm combining enhanced ant colony optimization and double improved simulated annealing via clustering in the traveling salesman problem (TSP). PeerJ Comput. Sci. 9 (2023) e1609. [CrossRef] [Google Scholar]
  • H. Qian and T. Su, Hybrid algorithm based on max and min ant system and particle swarm optimization for solving TSP problem, in 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE (2018) 683–687. [Google Scholar]
  • M. Dorigo and L.M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1 (1997) 53–66. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.