Skip to main content
SpringerLink
Log in

Hybridization as Cooperative Parallelism for the Quadratic Assignment Problem

  • Conference paper
  • First Online:
Hybrid Metaheuristics (HM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9668))

Included in the following conference series:

Abstract

The Quadratic Assignment Problem is at the core of several real-life applications. Finding an optimal assignment is computationally very difficult, for many useful instances. The best results are obtained with hybrid heuristics, which result in complex solvers. We propose an alternate solution where hybridization is obtain by means of parallelism and cooperation between simple single-heuristic solvers. We present experimental evidence that this approach is very efficient and can effectively solve a wide variety of hard problems, often surpassing state-of-the-art systems.

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 EPUB and 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

References

  1. Koopmans, T.C., Beckmann, M.: Assignment problems and the location of economic activities. Econometrica 25(1), 53–76 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  2. Commander, C.W.: A survey of the quadratic assignment problem, with applications. Morehead Electron. J. Appl. Math. 4, 1–15 (2005). MATH-2005-01

    Google Scholar 

  3. Bhati, R.K., Rasool, A.: Quadratic assignment problem and its relevance to the real world: a survey. Int. J. Comput. Appl. 96(9), 42–47 (2014)

    Google Scholar 

  4. James, T., Rego, C., Glover, F.: Multistart tabu search and diversification strategies for the quadratic assignment problem. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 39(3), 579–596 (2009)

    Article  Google Scholar 

  5. Benlic, U., Hao, J.K.: Breakout local search for the quadratic assignment problem. Appl. Math. Comput. 219(9), 4800–4815 (2013)

    MathSciNet  MATH  Google Scholar 

  6. Drezner, Z.: The extended concentric tabu for the quadratic assignment problem. Eur. J. Oper. Res. 160(2), 416–422 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Drezner, Z.: Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem. Comput. Oper. Res. 35(3), 717–736 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. James, T., Rego, C., Glover, F.: A cooperative parallel tabu search algorithm for the quadratic assignment problem. Eur. J. Oper. Res. 195, 810–826 (2009)

    Article  MATH  Google Scholar 

  9. Tosun, U.: On the performance of parallel hybrid algorithms for the solution of the quadratic assignment problem. Eng. Appl. Artif. Intell. 39, 267–278 (2015)

    Article  Google Scholar 

  10. Munera, D., Diaz, D., Abreu, S.: Solving the quadratic assignment problem with cooperative parallel extremal optimization. In: Chicano, F., et al. (eds.) EvoCOP 2016. LNCS, vol. 9595, pp. 251–266. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30698-8_17

    Chapter  Google Scholar 

  11. Munera, D., Diaz, D., Abreu, S., Codognet, P.: A parametric framework for cooperative parallel local search. In: Blum, C., Ochoa, G. (eds.) EvoCOP 2014. LNCS, vol. 8600, pp. 13–24. Springer, Heidelberg (2014)

    Google Scholar 

  12. Munera, D., Diaz, D., Abreu, S., Codognet, P.: Flexible cooperation in parallel local search. In: Symposium on Applied Computing (SAC), pp. 1360–1361. ACM Press, New York (2014)

    Google Scholar 

  13. Charles, P., Grothoff, C., Saraswat, V., Donawa, C., Kielstra, A., Ebcioglu, K., Von Praun, C., Sarkar, V.: X10: an object-oriented approach to non-uniform cluster computing. In: SIGPLAN Conference on Object-oriented Programming, Systems, Languages, and Applications, pp. 519–538. ACM, San Diego (2005)

    Google Scholar 

  14. Saraswat, V., Tardieu, O., Grove, D., Cunningham, D., Takeuchi, M., Herta, B.: A Brief Introduction to X10 (for the High Performance Programmer). Technical report (2012)

    Google Scholar 

  15. Taillard, É.D.: Robust taboo search for the quadratic assignment problem. Parallel Comput. 17(4–5), 443–455 (1991)

    Article  MathSciNet  Google Scholar 

  16. Burkard, R.E.: Quadratic assignment problems. In: Pardalos, P.M., Du, D.Z., Graham, R.L. (eds.) Handbook of Combinatorial Optimization, 2nd edn, pp. 2741–2814. Springer, New York (2013)

    Chapter  Google Scholar 

  17. Loiola, E.M., de Abreu, N.M.M., Netto, P.O.B., Hahn, P., Querido, T.M.: A survey for the quadratic assignment problem. Eur. J. Oper. Res. 176(2), 657–690 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. Zaied, A.N.H., Shawky, L.A.E.F.: A survey of quadratic assignment problems. Int. J. Comput. Appl. 101(6), 28–36 (2014)

    Google Scholar 

  19. Sahni, S., Gonzalez, T.: P-complete approximation problems. J. ACM 23(3), 555–565 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  20. Said, G., Mahmoud, A.M., El-Horbaty, E.S.M.: A comparative study of meta-heuristic algorithms for solving quadratic assignment problem. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 5(1), 1–6 (2014)

    Google Scholar 

  21. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)

    Book  MATH  Google Scholar 

  22. Taillard, É.D.: Comparison of iterative searches for the quadratic assignment problem. Location Sci. 3(2), 87–105 (1995)

    Article  MATH  Google Scholar 

  23. Misevicius, A.: A tabu search algorithm for the quadratic assignment problem. Comput. Optim. Appl. 30(1), 95–111 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. Boettcher, S., Percus, A.: Nature’s way of optimizing. Artif. Intell. 119(1–2), 275–286 (2000)

    Article  MATH  Google Scholar 

  25. Boettcher, S., Percus, A.G.: Extremal optimization: an evolutionary local-search algorithm. In: Bhargava, H.K., Ye, N. (eds.) Computational Modeling and Problem Solving in the Networked World, vol. 21. Springer, New York (2003)

    Chapter  Google Scholar 

  26. Boettcher, S.: Extremal Optimization. In: Hartmann, A.K., Rieger, H. (eds.) New Optimization Algorithms to Physics, pp. 227–251. Wiley-VCH Verlag, Berlin (2004)

    Google Scholar 

  27. Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Phy. Rev. Lett. 71(24), 4083–4086 (1993)

    Article  Google Scholar 

  28. Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley-Interscience, New York (2005)

    Book  MATH  Google Scholar 

  29. Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res. 20(1), 1–48 (2013)

    Article  MATH  Google Scholar 

  30. Diaz, D., Abreu, S., Codognet, P.: Parallel constraint-based local search on the cell/be multicore architecture. In: Essaaidi, M., Malgeri, M., Badica, C. (eds.) Intelligent Distributed Computing IV. SCI, vol. 315, pp. 265–274. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  31. Verhoeven, M., Aarts, E.: Parallel local search. J. Heuristics 1(1), 43–65 (1995)

    Article  MATH  Google Scholar 

  32. Caniou, Y., Codognet, P., Richoux, F., Diaz, D., Abreu, S.: Large-scale parallelism for constraint-based local search: the costas array case study. Constraints 20(1), 1–27 (2014)

    MATH  Google Scholar 

  33. Toulouse, M., Crainic, T., Sansó, B.: Systemic behavior of cooperative search algorithms. Parallel Comput. 30, 57–79 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  34. Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. J. Heuristics 4(4), 295–321

    Google Scholar 

  35. Gomes, C.P., Selman, B.: Algorithm portfolios. Artif. Intell. 126(1–2), 43–62 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  36. Palubeckis, G.: An algorithm for construction of test cases for the quadratic assignment problem. Informatica Lith. Acad. Sci. 11(3), 281–296 (2000)

    MathSciNet  MATH  Google Scholar 

  37. Wu, K.C., Ting, C.J., Gonzalez, L.C.: An ant colony optimization algorithm for quadratic assignment problem. In: Asia-Pacific Conference on Industrial Engineering and Management Systems (2011)

    Google Scholar 

  38. Drezner, Z., Hahn, P., Taillard, É.: Recent advances for the quadratic assignment problem with special emphasis on instances that are difficult for meta-heuristic methods. Ann. Oper. Res. 139(1), 65–94 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  39. Acan, A., Ünveren, A.: A great deluge and tabu search hybrid with two-stage memory support for quadratic assignment problem. Appl. Soft Comput. 36(C), 185–203 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to thank Prof. E. Taillard for providing the RoTS source code and explanations. The experimentation used the cluster of the University of Évora, which was partly funded by grants ALENT-07-0262-FEDER-001872 and ALENT-07-0262-FEDER-001876.

Author information

Authors and Affiliations

  1. University of Paris 1-Sorbonne/CRI, Paris, France

    Danny Munera, Daniel Diaz & Salvador Abreu

  2. Universidade de Évora/LISP, Évora, Portugal

    Salvador Abreu

Authors
  1. Danny Munera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Diaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salvador Abreu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Diaz .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  2. University of the Basque Country, San Sebastian, Spain

    Christian Blum

  3. Universiy of Plymouth, Plymouth, United Kingdom

    Angelo Cangelosi

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

  5. Computing, Sheffield Hallam University, SHEFFIELD, United Kingdom

    ALESSANDRO DI NUOVO

  6. University of Catania, Catania, Italy

    Mario Pavone

  7. University of Lille 1, Villeneuve d'Ascq, France

    El-Ghazali Talbi

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Munera, D., Diaz, D., Abreu, S. (2016). Hybridization as Cooperative Parallelism for the Quadratic Assignment Problem. In: Blesa, M., et al. Hybrid Metaheuristics. HM 2016. Lecture Notes in Computer Science(), vol 9668. Springer, Cham. https://doi.org/10.1007/978-3-319-39636-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39636-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39635-4

  • Online ISBN: 978-3-319-39636-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation