Improving local-search metaheuristics through look-ahead policies

  • David Meignan
  • Silvia SchwarzeEmail author
  • Stefan Voß


As a basic principle, look-ahead approaches investigate the outcomes of potential future steps to evaluate the quality of alternative search directions. Different policies exist to set up look-ahead methods differing in the object of inspection and in the extensiveness of the search. In this work, two original look-ahead strategies are developed and tested through numerical experiments. The first method introduces a look-ahead mechanism that acts as a hyper-heuristic for comparing and selecting local-search operators. The second method uses a look-ahead strategy on a lower level in order to guide a local-search metaheuristic. The proposed approaches are implemented using a hyper-heuristic framework. They are tested against alternative methods using two different competition benchmarks, including a comparison with results given in literature. Furthermore, in a second set of experiments, a detailed investigation regarding the influence of particular parameter values is executed for one method. The experiments reveal that the inclusion of a simple look-ahead principle into an iterated local-search procedure significantly improves the outcome regarding the considered benchmarks.


Metaheuristic Hyper-heuristic Look-ahead Iterated local-search 

Mathematics Subject Classifications (2010)

68T20 90B40 


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  1. 1.
    Awadallah, M.A., Khader, A.T., Al-Betar, M.A., Bolaji, A.L.: Nurse rostering using modified harmony search algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C (eds.) SEMCCO 2011. Vol. 7077 of Lecture Notes in Computer Science, pp 27–37. Springer, Berlin Heidelberg (2011)Google Scholar
  2. 2.
    Bertsekas, D.P., Tsitsiklis, J.N., Wu, C.: Rollout algorithms for combinatorial optimization. J. Heuristics 3, 245–262 (1997)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bilgin, B., Demeester, P., Misir, M., Vancroonenburg, W., Berghe, G.V.: One hyper-heuristic approach to two timetabling problems in health care. J. Heuristics 18, 401–434 (2012)CrossRefGoogle Scholar
  4. 4.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)CrossRefGoogle Scholar
  5. 5.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.R.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, Vol. 146 of International Series in Operations Research & Management Science, pp 449–468. Springer, New York (2010)Google Scholar
  6. 6.
    Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., McCollum, B., Ochoa, G., Parkes, A.J., Petrovic, S.: The cross-domain heuristic search challenge - an international research competition. In: Coello Coello, C.A (ed.) Learning and Intelligent Optimization, Vol. 6683 of Lecture Notes in Computer Science, pp 631–634. Springer, Berlin Heidelberg (2011)Google Scholar
  7. 7.
    Burke, E.K., Curtois, T., Qu, R., Berghe, G.V.: A time predefined variable depth search for nurse rostering. INFORMS J. Comp. 25(3), 411–419 (2013)CrossRefGoogle Scholar
  8. 8.
    Caserta, M., Schwarze, S., Voß, S.: Container rehandling at maritime container terminals. In: Böse, J.W. (ed.) Handbook of Terminal Planning, Operations Research/Computer Science Interfaces Series, vol. 49, pp 247–269. Springer, New York (2011)Google Scholar
  9. 9.
    Cotta, C.: Effective patient prioritization in mass casualty incidents using hyperheuristics and the pilot method. OR Spectr. 33, 699–720 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Duin, C., Voß, S.: Steiner tree heuristics - a survey. In: Dyckhoff, H., Derigs, U., Salomon, M., Tijms, H. (eds.) Operations Research Proceedings 1993, pp 485–496. Springer, Berlin (1994)CrossRefGoogle Scholar
  11. 11.
    Duin, C., Voß, S.: The pilot method: a strategy for heuristic repetition with application to the Steiner problem in graphs. Networks 34, 181–191 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Frost, D., Dechter, R.: Look-ahead value ordering for constraint satisfaction problems In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp 572–578 (1995)Google Scholar
  13. 13.
    Geiger, M., Sevaux, M., Voß, S.: Neigborhood selection in variable neighborhood search In: Proceedings of the Metaheuristics International Conference. Udine (2011)Google Scholar
  14. 14.
    Hansen, P., Mladenović, N., Brimberg, J., Moreno Pérez, J.A.: Variable neighborhood search. In: Gendreau, M., Potvin, J.-Y (eds.) Handbook of Metaheuristics, Vol. 146 of International Series in Operations Research & Management Science, pp 61–86. Springer, New York (2010)Google Scholar
  15. 15.
    Haspeslagh, S., De Causmaecker, P., Schaerf, A., Stølevik, M.: The first international nurse rostering competition 2010. Ann. Oper. Res. 218, 221–236 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Haul, C., Voß, S.: Using surrogate constraints in genetic algorithms for solving multidimensional knapsack problems. In: Woodruff, D.L (ed.) Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search, pp 235–251. Kluwer, Boston (1998)CrossRefGoogle Scholar
  17. 17.
    Höller, H., Melian, B., Voß, S.: Applying the pilot method to improve VNS and GRASP metaheuristics for the design of SDH/WDM networks. Eur. J. Oper. Res. 191, 691–704 (2008)CrossRefzbMATHGoogle Scholar
  18. 18.
    Jin, B., Lim, A., Zhu, W.: A greedy look-ahead heuristic for the container relocation problem. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) Recent Trends in Applied Artificial Intelligence. Vol 7906 of Lecture Notes in Computer Science, pp. 181–190. Springer, Berlin, Heidelberg (2013)Google Scholar
  19. 19.
    Jovanovic, R., Voß, S.: A chain heuristic for the blocks relocation problem. Comput. Ind. Eng. 75, 79–86 (2014)CrossRefGoogle Scholar
  20. 20.
    Kim, K.H., Bae, J.W.: A look-ahead dispatching method for automated guided vehicles in automated port container terminals. Transp. Sci. 38(2), 224–234 (2004)CrossRefGoogle Scholar
  21. 21.
    Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G.A (eds.) Handbook of Metaheuristics, Vol. 57 of International Series in Operations Research & Management Science, pp 320–353. Springer, New York (2003)CrossRefGoogle Scholar
  22. 22.
    Lü, Z, Hao, J.-K.: Adaptive neighborhood search for nurse rostering. Eur. J. Oper. Res. 218, 865–876 (2012)CrossRefGoogle Scholar
  23. 23.
    Meignan, D.: A heuristic approach to schedule reoptimization in the context of interactive optimization In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation (GECCO14), pp 461–468. ACM, New York (2014)Google Scholar
  24. 24.
    Meignan, D., Schwarze, S., Voß, S.: Two look-ahead strategies for local-search metaheuristics. In: Pardalos, P.M., Resende, M.G.C., Vogiatzis, C., Walteros, J.L. (eds.) Learning and Intelligent Optimization, Vol. 8426 of Lecture Notes in Computer Science, pp 187–202. Springer International Publishing (2014)Google Scholar
  25. 25.
    Ochoa, G., Hyde, M., Curtois, T., Vazquez-Rodriguez, J.A., Walker, J., Gendreau, M., Kendall, G., McCollum, B., Parkes, A.J., Petrovic, S., Burke, E.K.: HyFlex: A benchmark framework for cross-domain heuristic search. In: Hao, J.-K., Middendorf, M (eds.) , EvoCOP 2012, Vol. 7245 of Lecture Notes in Computer Science, pp 136–147. Springer, New York (2012)Google Scholar
  26. 26.
    Ochoa, G., Walker, J., Hyde, M., Curtois, T.: Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework. In: Coello Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M (eds.) Parallel Problem Solving from Nature - PPSN XII, Vol. 7492 of Lecture Notes in Computer Science, pp 418–427. Springer, New York (2012)Google Scholar
  27. 27.
    Papazek, P., Raidl, G.R., Rainer-Harbach, M., Hu, B.: A PILOT/VND/GRASP hybrid for the static balancing of public bicycle sharing systems. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A (eds.) Computer Aided Systems Theory - EUROCAST 2013, Vol. 8111 of Lecture Notes in Computer Science, pp 372–379. Springer, New York (2013)Google Scholar
  28. 28.
    Petering, M.E.H., Hussein, M.I.: A new mixed integer program and extended look-ahead heuristic algorithm for the block relocation problem. Eur. J. Oper. Res. 231, 120–130 (2013)CrossRefzbMATHGoogle Scholar
  29. 29.
    Runarsson, T.P., Schoenauer, M., Sebag, M.: Pilot, rollout and Monte Carlo tree search methods for job shop scheduling. In: Hamadi, Y., Schoenauer, M (eds.) Learning and Intelligent Optimization, Vol. 7219 of Lecture Notes in Computer Science, pp 160–174. Springer, New York (2012)Google Scholar
  30. 30.
    Schwarze, S., Voß, S.: Look ahead hyper heuristics. In: Fink, A., Geiger, M.J. (eds.) Proceedings of the 14th EU/ME Workshop, pp 91–97 (2013)Google Scholar
  31. 31.
    Voß, S., Fink, A., Duin, C.: Looking ahead with the pilot method. Ann. Oper. Res. 136, 285–302 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Whitley, D.L., Gordon, V.S., Mathias, K.E.: Lamarckian evolution, the Baldwin effect and function optimization. In: Davidor, Y., Schwefel, H.P, Männer, R (eds.) Parallel Problem Solving From Nature–PPSN III, pp 6–15. Springer, Berlin (1994)Google Scholar

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of OsnabrückOsnabrückGermany
  2. 2.Institute of Information Systems, University of HamburgHamburgGermany
  3. 3.Escuela de Ingeniería IndustrialPontificia Universidad Católica de ValparaísoValparaísoChile

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