Frequency Distribution Based Hyper-Heuristic for the Bin-Packing Problem

  • He Jiang
  • Shuyan Zhang
  • Jifeng Xuan
  • Youxi Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6622)


In the paper, we investigate the pair frequency of low-level heuristics for the bin packing problem and propose a Frequency Distribution based Hyper-Heuristic (FDHH). FDHH generates the heuristic sequences based on a pair of low-level heuristics rather than an individual low-level heuristic. An existing Simulated Annealing Hyper-Heuristic (SAHH) is employed to form the pair frequencies and is extended to guide the further selection of low-level heuristics. To represent the frequency distribution, a frequency matrix is built to collect the pair frequencies while a reverse-frequency matrix is generated to avoid getting trapped into the local optima. The experimental results on the bin-packing problems show that FDHH can obtain optimal solutions on more instances than the original hyper-heuristic.


hyper-heuristic frequency distribution bin-packing pair frequency 


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  1. 1.
    Burke, E.K., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An Emerging Direction in Modern Search Technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer, Dordrecht (2003)CrossRefGoogle Scholar
  2. 2.
    Ochoa, G., Vaquez-Rodríguez, J.A., Petrovic, S., Burke, E.K.: Dispatching Rules for Production Scheduling: a Hyper-heuristic Landscape Analysis. In: Proceedings of the IEEE CEC, Trondheim, Norway, pp. 1873–1880 (2009)Google Scholar
  3. 3.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: A Survey of Hyper-heuristics. Technical Report, School of Computer Science and Information Technology, University of Nottingham, Computer Science (2009)Google Scholar
  4. 4.
    Ross, P., Marin-Blazquez, J.G., Schulenburg, S., Hart, E.: Learning a Procedure that Can Solve Hard Bin-packing Problems: A new GA-based Approach to Hyper-heuristics. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1295–1306. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Bai, R., Blazewicz, J., Burke, E.K., Kendall, G., McCollum, B.: A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support. Technical report, School of CSiT, University of Nottingham (2007)Google Scholar
  6. 6.
    Qu, R., Burke, E.K.: Hybridisations within a Graph Based Hyper-heuristic Framework for University Timetabling Problems. JORS 60, 1273–1285 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Qu, R., Burke, E.K., McCollum, B.: Adaptive Automated Construction of Hybrid Heuristics for Exam Timetabling and Graph Colouring Problems. EJOR 198, 392–404 (2008)CrossRefzbMATHGoogle Scholar
  8. 8.
    Bilgin, B., Ozcan, E., Korkmaz, E.E.: An Experimental Study on Hyper-heuristics and Final Exam Scheduling. In: PATAT 2006, pp. 394–412. Springer, Berlin (2007)Google Scholar
  9. 9.
    Vazquez-Rodriguez, J.A., Petrovic, S., Salhi, A.: A Combined Meta-heuristic with Hyper-heuristic Approach to the Scheduling of the Hybrid Flow Shop with Sequence Dependent Setup Times and Uniform Machines. In: Proceedings of the 3rd Multidisciplinary International Scheduling Conference, Paris, France, pp. 506–513 (2007)Google Scholar
  10. 10.
    Han, L., Kendall, G.: Guided Operators for a Hyper-heuristic Genetic Algorithm. In: Gedeon, T(T.) D., Fung, L.C.C. (eds.) AI 2003. LNCS (LNAI), vol. 2903, pp. 807–820. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. John Wiley & Sons, Chichester (1990)zbMATHGoogle Scholar
  12. 12.
    Thabtah, F., Cowling, P.: Mining the Data from a Hyperheuristic Approach Using Associative Classification. Expert Systems with Applications 34(2), 1093–1101 (2008)CrossRefGoogle Scholar
  13. 13.
    Chakhlevitch, K., Cowling, P.: Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 23–33. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Ren, Z., Jiang, H., Xuan, J., Luo, Z.: Ant Based Hyper Heuristics with Space Reduction: A Case Study of the p-Median Problem. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 546–555. Springer, Heidelberg (2010)Google Scholar
  15. 15.
    Cross-domain Heuristic Search Challenge,
  16. 16.
    Fleszar, K., Hindi, K.S.: New Heuristics for One-dimensional Bin-packing. Computers and Operations Research 29(7), 821–839 (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Alvim, A.C.F., Ribeiro, C.C., Glover, F., Aloise, D.J.: A Hybrid Improvement Heuristic for the One Dimensional Bin Packing Problem. Journal of Heuristics 10, 205–229 (2004)CrossRefGoogle Scholar
  18. 18.
    Falkenauer, E.: A Hybrid Grouping Genetic Algorithm for Bin Packing. Journal of Heuristics 2, 5–30 (1996)CrossRefGoogle Scholar
  19. 19.
    Scholl, A., Klein, R., Jurgens, C.: BISON: A Fast Hybrid Procedure for Exactly Solving the One Dimensional Bin Packing Problem. Computers & Operations Research 24(7), 627–645 (1997)CrossRefzbMATHGoogle Scholar
  20. 20.
    Valerio de Carvalho, J.M.: Exact Solution of Bin-packing Problems Using Column Generation and branch-and-bound. Annals of Operations Research 86, 629–659 (1999)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • He Jiang
    • 1
  • Shuyan Zhang
    • 2
  • Jifeng Xuan
    • 3
  • Youxi Wu
    • 4
  1. 1.School of SoftwareDalian University of TechnologyChina
  2. 2.School of Software TechnologyZhengzhou UniversityChina
  3. 3.School of Mathematical SciencesDalian University of TechnologyChina
  4. 4.School of Computer Science and SoftwareHebei University of TechnologyChina

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