[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Optimization of Transport Routes Through a Social Interaction Algorithm-Based Application

  • Conference paper
  • First Online:
International Joint Conferences (ICEUTE 2024, CISIS 2024)

Abstract

In this paper, we present a prototype application designed to solve the Traveling Salesman Problem (TSP). This application allows for obtaining high-quality solutions within reasonable time frames using bio-inspired algorithms, specifically variants of Ant Systems such as Ant Colony System, Max-Min Ant System and Best-Worst Ant System. We will review the operation of each of these algorithms and apply them interactively to solve TSP instances. These algorithms will be compared with the deterministic Lin-Kernighan algorithm to demonstrate their effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 109.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 139.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.math.uwaterloo.ca/tsp/concorde.html.

  2. 2.

    http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/.

  3. 3.

    https://acortar.link/KXlpMd.

  4. 4.

    https://github.com/diva0001/aplicacionTSP.

  5. 5.

    https://github.com/diva0001/TFG.

References

  1. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of ECAL91-European Conference on Artificial Life, pp. 134–142. Elsevier, New York (1991)

    Google Scholar 

  2. Colorni, A., Dorigo, M., Maniezzo, V.: An investigation of some properties of an ant algorithm. In: Proceedings of Parallel Problem Solving from Nature Conference (PPSN 92), pp. 509–520. Elsevier, New York (1992)

    Google Scholar 

  3. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. dissertation, DEI, Politecnico di Milano, Italy (1992). (in Italian)

    Google Scholar 

  4. Dorigo, M.: The any system optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern.-Part B 26(1), 1–13 (1996)

    Google Scholar 

  5. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: The traveling salesman problem: a comprehensive survey. Oper. Res. 34(2), 319–368 (1985)

    Google Scholar 

  6. Lin, H., Gao, L., Wang, X.: Solving the traveling salesman problem using multi-agent simulated annealing algorithm. Appl. Soft Comput. 11(7), 6113–6120 (2011)

    Google Scholar 

  7. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2006)

    Article  MathSciNet  Google Scholar 

  8. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Wiley, Hoboken (1985)

    Google Scholar 

  9. Bitner, J.R., Reingold, E.M.: Backtrack programming techniques. Commun. ACM 18(11), 651–656 (1975)

    Article  Google Scholar 

  10. Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)

    Google Scholar 

  11. Liu, C.-C., Kernighan, B.W.: An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21, 498–516 (1973)

    Article  MathSciNet  Google Scholar 

  12. Gambardella, L.M., Dorigo, M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  13. Stützle, T., Hoos, H.H.: MAX-MIN Ant System. Futur. Gener. Comput. Syst. 16(8), 889–914 (2000)

    Article  Google Scholar 

  14. Cordón, O., Fernández de Viana, I., Herrera, F., Moreno, L.l.: A new ACO model integrating evolutionary computation concepts: the best-worst ant system. In: Actas de A ’2000, pp. 22–29 (2000)

    Google Scholar 

  15. Laporte, G.: The traveling salesman problem: an overview of exact and approximate algorithms. Eur. J. Oper. Res. 59(2), 231–247 (1992)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Diego Ismael Valdivia Alcalá .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Valdivia Alcalá, D.I., García Vico, Á.M., Carmona del Jesús, C.J. (2024). Optimization of Transport Routes Through a Social Interaction Algorithm-Based Application. In: Quintián, H., et al. International Joint Conferences. ICEUTE CISIS 2024 2024. Lecture Notes in Networks and Systems, vol 957. Springer, Cham. https://doi.org/10.1007/978-3-031-75016-8_28

Download citation

Publish with us

Policies and ethics