Automatic Design of Hybrid Stochastic Local Search Algorithms

  • Marie-Eléonore Marmion
  • Franco Mascia
  • Manuel López-Ibáñez
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7919)

Abstract

Many stochastic local search (SLS) methods rely on the manipulation of single solutions at each of the search steps. Examples are iterative improvement, iterated local search, simulated annealing, variable neighborhood search, and iterated greedy. These SLS methods are the basis of many state-of-the-art algorithms for hard combinatorial optimization problems. Often, several of these SLS methods are combined with each other to improve performance. We propose here a practical, unified structure that encompasses several such SLS methods. The proposed structure is unified because it integrates these metaheuristics into a single structure from which we can not only instantiate each of them, but we also can generate complex combinations and variants. Moreover, the structure is practical since we propose a method to instantiate actual algorithms for practical problems in a semi-automatic fashion. The method presented in this work implements a general local search structure as a grammar; an instantiation of such a grammar is a program that can be compiled into executable form. We propose to find the appropriate grammar instantiation for a particular problem by means of automatic configuration. The result is a semi-automatic system that, with little human effort, is able to generate powerful hybrid SLS algorithms.

Keywords

Stochastic local search generalized local search structure grammar automatic algorithm design 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balaprakash, P., Birattari, M., Stützle, T.: Improvement strategies for the F-race algorithm: Sampling design and iterative refinement. In: Bartz-Beielstein, T., Blesa Aguilera, M.J., Blum, C., Naujoks, B., Roli, A., Rudolph, G., Sampels, M. (eds.) HM 2007. LNCS, vol. 4771, pp. 108–122. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Burke, E.K., Hyde, M.R., Kendall, G.: Grammatical evolution of local search heuristics. IEEE Transactions on Evolutionary Computation 16(7), 406–417 (2012)CrossRefGoogle Scholar
  3. 3.
    Cahon, S., Melab, N., Talbi, E.G.: ParadisEO: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 10(3), 357–380 (2004)CrossRefGoogle Scholar
  4. 4.
    Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. John Wiley & Sons, New York (1999)Google Scholar
  5. 5.
    Du, J., Leung, J.Y.T.: Minimizing total tardiness on one machine is NP-hard. Mathematics of Operations Research 15(3), 483–495 (1990)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Dubois-Lacoste, J.: A study of Pareto and Two-Phase Local Search Algorithms for Biobjective Permutation Flowshop Scheduling. Master’s thesis, IRIDIA, Université Libre de Bruxelles, Belgium (2009)Google Scholar
  7. 7.
    Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. Computers & Operations Research 38(8), 1219–1236 (2011)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109–113 (1995)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Glover, F.: Tabu search – Part I. INFORMS Journal on Computing 1(3), 190–206 (1989)MATHCrossRefGoogle Scholar
  10. 10.
    Hansen, P., Mladenovic, N.: Variable neighborhood search: Principles and applications. European Journal of Operational Research 130(3), 449–467 (2001)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Hoos, H.H., Stützle, T.: Stochastic Local Search—Foundations and Applications. Morgan Kaufmann Publishers, San Francisco (2005)MATHGoogle Scholar
  12. 12.
    Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research 36, 267–306 (2009)MATHGoogle Scholar
  13. 13.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Tech. Rep. TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium (2011)Google Scholar
  15. 15.
    Lourenço, H.R., Martin, O., Stützle, T.: Iterated local search: Framework and applications. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, ch. 9, 2nd edn., pp. 363–397. Springer (2010)Google Scholar
  16. 16.
    Mascia, F., López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T.: From grammars to parameters: Automatic iterated greedy design for the permutation flow-shop problem with weighted tardiness. In: 7th International Conference on Learning and Intelligent Optimization, LION 7. LNCS. Springer (to appear, 2013)Google Scholar
  17. 17.
    Mckay, R.I., Hoai, N.X., Whigham, P.A., Shan, Y., O’Neill, M.: Grammar-based genetic programming: A survey. Genetic Programming and Evolvable Machines 11(3-4), 365–396 (2010)CrossRefGoogle Scholar
  18. 18.
    Minella, G., Ruiz, R., Ciavotta, M.: A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS Journal on Computing 20(3), 451–471 (2008)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Nawaz, M., Enscore Jr., E., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. OMEGA 11(1), 91–95 (1983)CrossRefGoogle Scholar
  20. 20.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization – Algorithms and Complexity. Prentice Hall, Englewood Cliffs (1982)MATHGoogle Scholar
  21. 21.
    Ruiz, R., Stützle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research 177(3), 2033–2049 (2007)MATHCrossRefGoogle Scholar
  22. 22.
    Taillard, É.D.: Benchmarks for basic scheduling problems. European Journal of Operational Research 64(2), 278–285 (1993)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marie-Eléonore Marmion
    • 1
  • Franco Mascia
    • 1
  • Manuel López-Ibáñez
    • 1
  • Thomas Stützle
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de Bruxelles (ULB)BrusselsBelgium

Personalised recommendations