Open Access
| Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 16, 2025
|
|
|---|---|---|
| Article Number | 11 | |
| Number of page(s) | 16 | |
| DOI | https://doi.org/10.1051/smdo/2025011 | |
| Published online | 25 August 2025 | |
- M.A.A. Hossain, Z.Y. Acar, Comparison of new and old optimization algorithms for traveling salesman problem on small, medium, and large-scale benchmark instances, Bitlis Eren Univ. J. Sci. 13, 216–231 (2024) [Google Scholar]
- D. Mecler, V. Abu-Marrul, R. Martinelli, A. Hoff, Iterated greedy algorithms for a complex parallel machine scheduling problem, Eur. J. Operat. Res. 300, 545–560 (2022) [Google Scholar]
- W. Li, L. Xia, Y. Huang, S. Mahmoodi, An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems, J. Ambient Intell. Humanized Comput. 13, 1557–1571 (2021) [Google Scholar]
- J. Zhang, The logic and application of greedy algorithms, Appl. Comput. Eng. 82, 154–160 (2024) [Google Scholar]
- R. Zhang, Research on Enterprise Management Based on Greedy Algorithm, Atlantis Highlights in Engineering, 1134–1140 (2023) [Google Scholar]
- N.O.V. Eche, N.A.E. Okeyinka, N.I. Abdullah, N.A. Abdulrahman, Review of algorithms to solve travelling salesman problem, J. Sci. Innov. Technol. Res. 05 (2025) https://doi.org/10.70382/ajsitr.v7i9.019 [Google Scholar]
- S. Violina, Analysis of Brute Force and Branch & Bound Algorithms to solve the Traveling Salesperson Problem (TSP) (2021). https://turcomat.org/index.php/turkbilmat/article/view/3031 [Google Scholar]
- M.A. Fahroni, D. Pratiwi, A. Romadhoni, N.R. Syambas, Brute force modification algorithm for ring topology network optimization (2021). https://doi.org/10.1109/tssa52866.2021.9768247 [Google Scholar]
- S. Wahyuningsih, D.R. Sari, Study of the brand and bound algorithm performance on traveling salesman problem variants, Adv. Social Sci. Educ. Humanities Res. (2021) https://doi.org/10.2991/assehr.k.210508.066 [Google Scholar]
- E. Khalil, H. Dai, Y. Zhang, B. Dilkina, L. Song, Learning combinatorial optimization algorithms over graphs, Adv. Neural Inform. Process. Syst. 30 (2017) [Google Scholar]
- M. Dell'Amico, R. Montemanni, S. Novellani, Algorithms based on branch and bound for the flying sidekick traveling salesman problem, Omega 104, 102493 (2021) [CrossRef] [Google Scholar]
- D.R. Morrison, S.H. Jacobson, J.J. Sauppe, E.C. Sewell, Branch-and-bound algorithms: a survey of recent advances in searching, branching, and pruning, Discrete Optim. 19, 79–102 (2016) [Google Scholar]
- M. Gendreau, G. Laporte, F. Semet, Branch and price for the stochastic traveling salesman problem with time windows, Transport. Sci. 57, 656–672 (2023) [Google Scholar]
- S. Poikonen, B.L. Golden, E. Wasil, A branch-and-bound approach to the traveling salesman problem with a drone, Informs J. Comput. 31, 335–346 (2019) [Google Scholar]
- S. Dhanasekar, S. Dash, N. Uthaman, A branch and bound algorithm to solve travelling salesman problem (TSP) with uncertain parameters, Math. Stat. 10, 358–365 (2022) [Google Scholar]
- B. Dimitrijević, R. Jovanović, T. Burić, D. Teodorović, G. Ćirović, A comprehensive survey on the generalized traveling salesman problem, Eur. J. Operat. Res. 305, 409–428 (2023) [Google Scholar]
- N.S.M. Mussafi, Distribution route optimization of Zakat Al-Fitr based on the branch-and-bound algorithm, Int. J. Modern Trends Technol. Sci. 66, 528–533 (2023) [Google Scholar]
- S.-W. Lin, S. Guo, W.-J. Wu, Applying the simulated annealing algorithm to the set orienteering problem with mandatory visits, Mathematics 12, 3089 (2024) [Google Scholar]
- E. Baidoo, S.O. Oppong, Solving the TSP using traditional computing approach, Int. J. Comput. Appl. 152, 13–19 (2016) [Google Scholar]
- I. Ariyanti, M.A. Ganiardi, U. Oktari, Mobile application searching of the shortest route on delivery order of CV. Alfa Fresh with brute force algorithm, Logic/Logic: Jurnal Rancang Bangun Dan Teknologi 19, 120 (2019) [Google Scholar]
- G. Lera-Romero, J.J. Miranda-Bront, F.J. Soulignac, Dynamic programming for the time-dependent traveling salesman problem with time windows, INFORMS J. Comput. 34, 3292–3308 (2022) [Google Scholar]
- P. Bouman, N. Agatz, M. Schmidt, Dynamic programming approaches for the traveling salesman problem with drone, Networks 72, 528–542 (2018) [CrossRef] [MathSciNet] [Google Scholar]
- C. Malandraki, R.B. Dial, A restricted dynamic programming heuristic algorithm for the time dependent traveling salesman problem, Eur. J. Operat. Res. 90, 45–55 (1996) [Google Scholar]
- M.Z. Rahman, S. Sheikh, A.Z.M.T. Islam, M. Rahman, Improvement of the nearest neighbor heuristic search algorithm for traveling salesman problem, J. Eng. Adv. 19–26 (2024) [Google Scholar]
- E.O. Asani, A.E. Okeyinka, A.A. Adebiyi, A Construction Tour Technique For Solving The Travelling Salesman Problem Based On Convex Hull And Nearest Neighbour Heuristics. 0 International Conference in Mathematics, Computer Engineering and Computer Science (2020). https://doi.org/10.1109/icmcecs47690.2020.240847 [Google Scholar]
- T. George, T. Amudha, Genetic algorithm based multi-objective optimization framework to solve traveling salesman problem. In Algorithms for intelligent systems (2020) pp. 141–151 [Google Scholar]
- M. Dorigo, V. Maniezzo, A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 26, 29–41 (1996) [Google Scholar]
- Y. Deng, Y. Liu, D. Zhou, An improved genetic algorithm with initial population strategy for symmetric TSP, Math. Probl. Eng. 2015, 1–6 (2015) [Google Scholar]
- S. Prayudani, A. Hizriadi, E.B. Nababan, S. Suwilo, Analysis effect of tournament selection on genetic algorithm performance in Traveling Salesman Problem (TSP), J. Phys. Conf. Ser. 1566, 012131 (2020) [Google Scholar]
- T. Kowalski, A review of the applications of genetic algorithms to forecasting commodity prices, Economies 9, 6 (2021) [Google Scholar]
- D. Yang, Z. Yu, H. Yuan, Y. Cui, An improved genetic algorithm and its application in neural network adversarial attack, PLOS ONE 16, e0251234 (2021) [Google Scholar]
- J. Zheng, J. Zhong, M. Chen, K. He, A reinforced hybrid genetic algorithm for the traveling salesman problem, Comput. Operat. Res. 157, 106249 (2023b) [Google Scholar]
- D. Shan, S. Zhang, X. Wang, P. Zhang, Path-planning strategy: adaptive ant colony optimization combined with an enhanced dynamic window approach, Electronics 13, 825 (2024) [Google Scholar]
- G. Dantzig, R. Fulkerson, S. Johnson, Solution of a large-scale traveling-salesman problem, J. Oper. Res. Soc. Am. 2, 393–410 (1954) [Google Scholar]
- K. Tang, C. Meng, Particle swarm optimization algorithm using velocity pausing and adaptive strategy, Symmetry 16, 661 (2024) [Google Scholar]
- W. Kool, H. Van Hoof, J. Gromicho, M. Welling, Deep policy dynamic programming for vehicle routing problems, In Lecture notes in computer science (2022) (pp. 190–213) [Google Scholar]
- W. Li, L. Xia, Y. Huang, S. Mahmoodi, An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems, J. Ambient Intell. Humanized Comput. 13, 1557–1571 (2021) [Google Scholar]
- A. Mingozzi, L. Bianco, S. Ricciardelli, Dynamic programming strategies for the traveling salesman problem with time window and precedence constraints, Operat. Res. 45, 365–377 (1997) [Google Scholar]
- C.S. Chauhan, R.K. Gupta, K. Pathak, Survey of methods of solving TSP along with its implementation using dynamic programming approach, Int. J. Computer Appl. 52, 12–19 (2012) [Google Scholar]
- J. Zhang, Comparison of various algorithms based on TSP solving, J. Phys. Conf. Ser. 2083, 032007 (2021) [Google Scholar]
- E. Balas, New Classes of Efficiently Solvable Generalized Traveling Salesman Problems,” MSRR No. 615, GSIA, Carnegie Mellon University, 1996 [Google Scholar]
- R.K. Halder, M.N. Uddin, M.A. Uddin, S. Aryal, A. Khraisat, Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications, J. Big Data 11, (2024) Article 113 [Google Scholar]
- T. Xie, L. Chen, B. Yi, S. Li, Z. Leng, X. Gan, Z. Mei, Application of the improved K-nearest neighbor-based multi-model ensemble method for runoff prediction, Water 16, 69 (2024) [Google Scholar]
- A. Nayyar, R. Singh, Performance analysis of ACO based routing Protocols- EMCBR, ANTChain, IACR, ACO-EAMRA for Wireless Sensor Networks (WSNs), Br. J. Math. Comput. Sci. 20, 1–18 (2017) [Google Scholar]
- N. Bilandi, H.K. Verma, R. Dhir, hPSO-SA: hybrid particle swarm optimization-simulated annealing algorithm for relay node selection in wireless body area networks, Appl. Intell. 51, 1410–1438 (2020) [Google Scholar]
- A. Kuznetsov, L. Wieclaw, N. Poluyanenko, L. Hamera, S. Kandiy, Y. Lohachova, Optimization of a simulated annealing algorithm for S-Boxes generating, Sensors 22, 6073 (2022) [Google Scholar]
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