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Hierarchical Iterated Local Search for the Quadratic Assignment Problem

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Hybrid Metaheuristics (HM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5818))

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Abstract

Iterated local search is a stochastic local search (SLS) method that combines a perturbation step with an embedded local search algorithm. In this article, we propose a new way of hybridizing iterated local search. It consists in using an iterated local search as the embedded local search algorithm inside another iterated local search. This nesting of local searches and iterated local searches can be further iterated, leading to a hierarchy of iterated local searches. In this paper, we experimentally examine this idea applying it to the quadratic assignment problem. Experimental results on large, structured instances show that the hierarchical iterated local search can offer advantages over using a “flat” iterated local search and make it a promising technique to be further considered for other applications.

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References

  1. Hoos, H.H., Stützle, T.: Stochastic Local Search—Foundations and Applications. Morgan Kaufmann Publishers, San Francisco (2005)

    MATH  Google Scholar 

  2. Lourenço, H.R., Martin, O., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 321–353. Kluwer Academic Publishers, Norwell (2002)

    Google Scholar 

  3. Call for Papers: HM2009: 6th International Workshop on Hybrid Metaheuristics (2009), http://www.diegm.uniud.it/hm2009/

  4. Smyth, K., Hoos, H.H., Stützle, T.: Iterated robust tabu search for MAX-SAT. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS (LNAI), vol. 2671, pp. 129–144. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Cordeau, J.-F., Laporte, G., Pasin, F.: Iterated tabu search for the car sequencing problem. European Journal of Operational Research 191(3), 945–956 (2008)

    Article  MATH  Google Scholar 

  6. Misevicius, A.: Using iterated tabu search for the traveling salesman problem. Information Technology and Control 32(3), 29–40 (2004)

    Google Scholar 

  7. Misevicius, A., Lenkevicius, A., Rubliauskas, D.: Iterated tabu search: an improvement to standard tabu search. Information Technology and Control 35(3), 187–197 (2006)

    Google Scholar 

  8. Lozano, M., García-Martínez, C.: An evolutionary ILS-perturbation technique. In: Blesa, M.J., Blum, C., Cotta, C., Fernández, A.J., Gallardo, J.E., Roli, A., Sampels, M. (eds.) HM 2008. LNCS, vol. 5296, pp. 1–15. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Essafi, I., Mati, Y., Dauzère-Pèréz, S.: A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Computers & Operations Research 35(8), 2599–2616 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  10. Sahni, S., Gonzalez, T.: P-complete approximation problems. Journal of the ACM 23(3), 555–565 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  11. Burkard, R.E., Çela, E., Pardalos, P.M., Pitsoulis, L.S.: The quadratic assignment problem. In: Pardalos, P.M., Du, D.Z. (eds.) Handbook of Combinatorial Optimization, vol. 2, pp. 241–338. Kluwer Academic Publishers, Dordrecht (1998)

    Google Scholar 

  12. Çela, E.: The Quadratic Assignment Problem: Theory and Algorithms. Kluwer Academic Publishers, Dordrecht (1998)

    Book  MATH  Google Scholar 

  13. Stützle, T.: Iterated local search for the quadratic assignment problem. European Journal of Operational Research 174(1), 1519–1539 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Hansen, P., Mladenović, N.: Variable neighborhood search: Principles and applications. European Journal of Operational Research 130(3), 449–467 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  15. Stützle, T., Fernandes, S.: New benchmark instances for the QAP and the experimental analysis of algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 199–209. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  18. Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Langdon, W.B., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 11–18. Morgan Kaufmann Publishers, San Francisco (2002)

    Google Scholar 

  19. Drezner, Z., Hahn, P., Taillard, É.D.: A study of quadratic assignment problem instances that are difficult for meta-heuristic methods. Annals of Operations Research 174, 65–94 (2005)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Mohamed Saifullah Hussin & Thomas Stützle

Authors
  1. Mohamed Saifullah Hussin
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  2. Thomas Stützle
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Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Omega 213 Campus Nord, Jordi Girona 1-3, 08034, Barcelona, Spain

    María J. Blesa

  2. Universitat Politècnica de Catalunya, Omega 112 Campus Nord, Jordi Girona 1-3, 08034, Barcelona, Spain

    Christian Blum

  3. DIEGM, Università di Udine, via delle Scienze 208, 33100, Udine, Italy

    Luca Di Gaspero

  4. DEIS, Università di Bologna, Sede di Cesena, Via Venezia 52, 47521, Cesena, Italy

    Andrea Roli

  5. IRIDIA CP 194/6, Université Libre de Bruxelles, Avenue Franklin D. Roosevelt 50, 1050, Bruxelles, Belgium

    Michael Sampels

  6. DIEGM, University of Udine, via delle Scienze 208, 33100, Udine, Italy

    Andrea Schaerf

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© 2009 Springer-Verlag Berlin Heidelberg

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Hussin, M.S., Stützle, T. (2009). Hierarchical Iterated Local Search for the Quadratic Assignment Problem. In: Blesa, M.J., Blum, C., Di Gaspero, L., Roli, A., Sampels, M., Schaerf, A. (eds) Hybrid Metaheuristics. HM 2009. Lecture Notes in Computer Science, vol 5818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04918-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-04918-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04917-0

  • Online ISBN: 978-3-642-04918-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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