Skip to main content
SpringerLink
Log in

A PSO-Based Memetic Algorithm for the Team Orienteering Problem

  • Conference paper
Applications of Evolutionary Computation (EvoApplications 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6625))

Included in the following conference series:

Abstract

This paper proposes an effective Particle Swarm Optimization (PSO)-based Memetic Algorithm (PSOMA) for the Team Orienteering Problem (TOP). TOP is a particular vehicle routing problem whose aim is to maximize the profit gained from visiting clients while not exceeding a travel cost/time limit. Our PSOMA features optimal splitting techniques and genetic crossover operators. Furthermore, the memetic characteristic of our PSOMA is strengthened by an intensive use of local search techniques and also by a low value of 0.07 for inertia. In our experiments with the standard benchmark for TOP, PSOMA attained a gap of only 0.016%, as compared to 0.041%, the best known gap in the literature.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Archetti, C., Hertz, A., Speranza, M.: Metaheuristics for the team orienteering problem. Journal of Heuristics 13(1) (February 2006)

    Google Scholar 

  2. Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. part I: background and development. Natural Computing 6(4), 467–484 (2007)

    MathSciNet  MATH  Google Scholar 

  3. Banks, A., Vincent, J., Anyakoha, C.: A review of particle swarm optimization. part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing 7(1), 109–124 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bouly, H., Dang, D.C., Moukrim, A.: A memetic algorithm for the team orienteering problem. 4OR 8(1), 49–70 (2010)

    Article  MATH  Google Scholar 

  5. Bouly, H., Moukrim, A., Chanteur, D., Simon, L.: Un algorithme de destruction/construction itératif pour la résolution d’un problème de tournées de véhicules spécifique. In: MOSIM 2008 (2008)

    Google Scholar 

  6. Boussier, S., Feillet, D., Gendreau, M.: An exact algorithm for team orienteering problems. 4OR 5(3), 211–230 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Butt, S., Cavalier, T.: A heuristic for the multiple tour maximum collection problem. Computers and Operations Research 21, 101–111 (1994)

    Article  MATH  Google Scholar 

  8. Butt, S.E., Ryan, D.M.: An optimal solution procedure for the multiple tour maximum collection problem using column generation. Computers and Operations Research 26, 427–441 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chao, I.M., Golden, B., Wasil, E.: The team orienteering problem. European Journal of Operational Research 88 (1996)

    Google Scholar 

  10. Golden, B., Levy, L., Vohra, R.: The orienteering problem. Naval Research Logistics 34, 307–318 (1987)

    Article  MATH  Google Scholar 

  11. Kameyama, K.: Particle swarm optimization - a survey. IEICE Transactions 92-D(7), 1354–1361 (2009)

    Article  Google Scholar 

  12. Ke, L., Archetti, C., Feng, Z.: Ants can solve the team orienteering problem. Computers and Industrial Engineering 54(3), 648–665 (2008)

    Article  Google Scholar 

  13. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  14. Montemanni, R., Gambardella, L.: Ant colony system for team orienteering problems with time windows. Foundations of Computing and Decision Sciences 34, 287–306 (2009)

    Google Scholar 

  15. Moscato, P.: Memetic Algorithms: a short introduction. In: New Ideas in Optimization, pp. 219–234. McGraw-Hill, New York (1999)

    Google Scholar 

  16. Schilde, M., Doerner, K.F., Hartl, R.F., Kiechle, G.: Metaheuristics for the bi-objective orienteering problem. Swarm Intelligence 3(3), 179–201 (2009)

    Article  Google Scholar 

  17. Sha, D.Y., Hsu, C.Y.: A hybird particle swarm optimization for job shop scheduling problem. Computers and Industrial Engineering 51(4), 791–808 (2006)

    Article  Google Scholar 

  18. Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Evolutionary Programming, pp. 591–600 (1998)

    Google Scholar 

  19. Souffriau, W., Vansteenwegen, P., Van den Berghe, G., Van Oudheusden, D.: A path relinking approach for the team orienteering problem. Computers & Operations Research 37(11), 1853–1859 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  20. Tang, H., Miller-Hooks, E.: A tabu search heuristic for the team orienteering problem. Computer & Operations Research 32, 1379–1407 (2005)

    Article  MATH  Google Scholar 

  21. Tricoire, F., Romauch, M., Doerner, K.F., Hartl, R.F.: Heuristics for the multi-period orienteering problem with multiple time windows. Computers & Operations Research 37, 351–367 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  22. Vansteenwegen, P., Souffriau, W., Van den Berghe, G., Van Oudheusden, D.: Metaheuristics for tourist trip planning. In: Metaheuristics in the Service Industry. LNEMS, vol. 624, pp. 15–31. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  23. Vansteenwegen, P., Souffriau, W., Van Oudheusden, D.: The orienteering problem: A survey. European Journal of Operational Research 209(1), 1–10 (2011)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Heudiasyc, CNRS UMR 6599, Université de Technologie de Compiègne, BP 20529, 60205, Compiègne, France

    Duc-Cuong Dang, Rym Nesrine Guibadj & Aziz Moukrim

  2. MERCUR subsidiary, VEOLIA Transport, 15, rue du Louvre, 75001, Paris, France

    Rym Nesrine Guibadj

Authors
  1. Duc-Cuong Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rym Nesrine Guibadj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aziz Moukrim
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. School of Business, University College Dublin, 4, Belfield, Dublin, UK

    Anthony Brabazon

  3. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  5. Next Generation Intelligent Networks Research Center (nexGIN RC), National University of Computer & Emerging Sciences (FAST-NU), Islamabad, Pakistan

    Muddassar Farooq

  6. Department of Information Systems, Johannes Gutenberg-Universität, 55099, Mainz, Germany

    Jörn Grahl

  7. Mathematics and Computer Science Depatment, University of Richmond, 23173, Richmond, VA, USA

    Gary Greenfield

  8. ICD - OSI, University of Technology of Troyes, 12, Rue Marie Curie, BP 2060, 10010, Troyes, France

    Christian Prins

  9. Facultad de Informatica, Universidade de A Coruña, 15071, Coruña, CP, Spain

    Juan Romero

  10. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  11. Consiglio Nazionale delle Ricerche (CNR), Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR), Via P. Castellino 111, 80131, Naples, Italy

    Ernesto Tarantino

  12. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Via Bramante 65, 26013, Crema (CR), Italy

    Andrea G. B. Tettamanzi

  13. School of Computing, Edinburgh Napier University, 10 Clinton Road, EH10 5DT, Edinburgh, UK

    Neil Urquhart

  14. Computer Engineering Department, Istanbul Technical University, Room Nr. 3302, Maslak, 34469, Istanbul, Turkey

    A. Şima Uyar

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dang, DC., Guibadj, R.N., Moukrim, A. (2011). A PSO-Based Memetic Algorithm for the Team Orienteering Problem. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20520-0_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20520-0_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20519-4

  • Online ISBN: 978-3-642-20520-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation