IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v297y2022i2p442-450.html
   My bibliography  Save this article

A linearithmic heuristic for the travelling salesman problem

Author

Listed:
  • Taillard, Éric D.

Abstract

A linearithmic (nlogn) randomized method based on POPMUSIC (Partial Optimization Metaheuristic Under Special Intensification Conditions) is proposed for generating reasonably good solutions to the travelling salesman problem. The method improves a previous work with empirical algorithmic complexity in n1.6. The method has been tested on instances with billions of cities. For a lot of problem instances of the literature, a few dozens of runs are able to generate a very high proportion of the edges of the best solutions known. This characteristic is exploited in a new release of the Helsgaun’s implementation of the Lin-Kernighan heuristic (LKH) that is also able to produce rapidly extremely good solutions for non-Euclidean instances. The practical limits of the proposed method are discussed on a new type of problem instances arising in a manufacturing process, especially in 3D extrusion printing.

Suggested Citation

  • Taillard, Éric D., 2022. "A linearithmic heuristic for the travelling salesman problem," European Journal of Operational Research, Elsevier, vol. 297(2), pages 442-450.
  • Handle: RePEc:eee:ejores:v:297:y:2022:i:2:p:442-450
    DOI: 10.1016/j.ejor.2021.05.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221721004628
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2021.05.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rego, César & Gamboa, Dorabela & Glover, Fred & Osterman, Colin, 2011. "Traveling salesman problem heuristics: Leading methods, implementations and latest advances," European Journal of Operational Research, Elsevier, vol. 211(3), pages 427-441, June.
    2. Quang Minh Ha & Yves Deville & Quang Dung Pham & Minh Hoàng Hà, 2020. "A hybrid genetic algorithm for the traveling salesman problem with drone," Journal of Heuristics, Springer, vol. 26(2), pages 219-247, April.
    3. David Applegate & William Cook & André Rohe, 2003. "Chained Lin-Kernighan for Large Traveling Salesman Problems," INFORMS Journal on Computing, INFORMS, vol. 15(1), pages 82-92, February.
    4. Neri Volpato & Lauro Cesar Galvão & Luiz Fernando Nunes & Rômulo Ianuch Souza & Karina Oguido, 2020. "Combining heuristics for tool-path optimisation in material extrusion additive manufacturing," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(6), pages 867-877, June.
    5. Cacchiani, Valentina & Contreras-Bolton, Carlos & Toth, Paolo, 2020. "Models and algorithms for the Traveling Salesman Problem with Time-dependent Service times," European Journal of Operational Research, Elsevier, vol. 283(3), pages 825-843.
    6. Taillard, Éric D. & Helsgaun, Keld, 2019. "POPMUSIC for the travelling salesman problem," European Journal of Operational Research, Elsevier, vol. 272(2), pages 420-429.
    7. S. Lin & B. W. Kernighan, 1973. "An Effective Heuristic Algorithm for the Traveling-Salesman Problem," Operations Research, INFORMS, vol. 21(2), pages 498-516, April.
    8. Campbell, James F. & Corberán, Ángel & Plana, Isaac & Sanchis, José M. & Segura, Paula, 2021. "Solving the length constrained K-drones rural postman problem," European Journal of Operational Research, Elsevier, vol. 292(1), pages 60-72.
    9. G Laporte & U Palekar, 2002. "Some applications of the clustered travelling salesman problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 972-976, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gary R. Waissi & Pragya Kaushal, 2020. "A polynomial matrix processing heuristic algorithm for finding high quality feasible solutions for the TSP," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 73-87, March.
    2. Taillard, Éric D. & Helsgaun, Keld, 2019. "POPMUSIC for the travelling salesman problem," European Journal of Operational Research, Elsevier, vol. 272(2), pages 420-429.
    3. Pan-Li Zhang & Xiao-Bo Sun & Ji-Quan Wang & Hao-Hao Song & Jin-Ling Bei & Hong-Yu Zhang, 2022. "The Discrete Carnivorous Plant Algorithm with Similarity Elimination Applied to the Traveling Salesman Problem," Mathematics, MDPI, vol. 10(18), pages 1-34, September.
    4. Andrzej Jaszkiewicz & Thibaut Lust, 2017. "Proper balance between search towards and along Pareto front: biobjective TSP case study," Annals of Operations Research, Springer, vol. 254(1), pages 111-130, July.
    5. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    6. Dontas, Michael & Sideris, Georgios & Manousakis, Eleftherios G. & Zachariadis, Emmanouil E., 2023. "An adaptive memory matheuristic for the set orienteering problem," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1010-1023.
    7. Sebastian Herrmann & Gabriela Ochoa & Franz Rothlauf, 2016. "Communities of Local Optima as Funnels in Fitness Landscapes," Working Papers 1609, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    8. Elena Nechita & Gloria Cerasela Crişan & Laszlo Barna Iantovics & Yitong Huang, 2020. "On the Resilience of Ant Algorithms. Experiment with Adapted MMAS on TSP," Mathematics, MDPI, vol. 8(5), pages 1-20, May.
    9. Sleegers, Joeri & Olij, Richard & van Horn, Gijs & van den Berg, Daan, 2020. "Where the really hard problems aren’t," Operations Research Perspectives, Elsevier, vol. 7(C).
    10. repec:jss:jstsof:23:i02 is not listed on IDEAS
    11. William Cook & Paul Seymour, 2003. "Tour Merging via Branch-Decomposition," INFORMS Journal on Computing, INFORMS, vol. 15(3), pages 233-248, August.
    12. Sebastian Herrmann & Gabriela Ochoa & Franz Rothlauf, 2018. "PageRank centrality for performance prediction: the impact of the local optima network model," Journal of Heuristics, Springer, vol. 24(3), pages 243-264, June.
    13. Emde, Simon & Tahirov, Nail & Gendreau, Michel & Glock, Christoph H., 2021. "Routing automated lane-guided transport vehicles in a warehouse handling returns," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1085-1098.
    14. Baniasadi, Pouya & Foumani, Mehdi & Smith-Miles, Kate & Ejov, Vladimir, 2020. "A transformation technique for the clustered generalized traveling salesman problem with applications to logistics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 444-457.
    15. César Rego & Fred Glover, 2010. "Ejection chain and filter-and-fan methods in combinatorial optimization," Annals of Operations Research, Springer, vol. 175(1), pages 77-105, March.
    16. Çavdar, Bahar & Sokol, Joel, 2015. "TSP Race: Minimizing completion time in time-sensitive applications," European Journal of Operational Research, Elsevier, vol. 244(1), pages 47-54.
    17. Thomas Weise & Yuezhong Wu & Raymond Chiong & Ke Tang & Jörg Lässig, 2016. "Global versus local search: the impact of population sizes on evolutionary algorithm performance," Journal of Global Optimization, Springer, vol. 66(3), pages 511-534, November.
    18. Yuichi Nagata & Shigenobu Kobayashi, 2013. "A Powerful Genetic Algorithm Using Edge Assembly Crossover for the Traveling Salesman Problem," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 346-363, May.
    19. Mutsunori Yagiura & Toshihide Ibaraki & Fred Glover, 2004. "An Ejection Chain Approach for the Generalized Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 16(2), pages 133-151, May.
    20. Zi-bin Jiang & Qiong Yang, 2016. "A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-15, November.
    21. Stefan Poikonen & Bruce Golden, 2020. "The Mothership and Drone Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 249-262, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:297:y:2022:i:2:p:442-450. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.