Skip to main content

Two Frameworks for Cross-Domain Heuristic and Parameter Selection Using Harmony Search

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 382)

Abstract

Harmony Search is a metaheuristic technique for optimizing problems involving sets of continuous or discrete variables, inspired by musicians searching for harmony between instruments in a performance. Here we investigate two frameworks, using Harmony Search to select a mixture of continuous and discrete variables forming the components of a Memetic Algorithm for cross-domain heuristic search. The first is a single-point based framework which maintains a single solution, updating the harmony memory based on performance from a fixed starting position. The second is a population-based method which co-evolves a set of solutions to a problem alongside a set of harmony vectors. This work examines the behaviour of each framework over thirty problem instances taken from six different, real-world problem domains. The results suggest that population co-evolution performs better in a time-constrained scenario, however both approaches are ultimately constrained by the underlying metaphors.

Keywords

  • Harmony search
  • Hyper-heuristics
  • Combinatorial optimisation
  • Metaheuristics
  • Memetic algorithms

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-662-47926-1_10
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-662-47926-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Geem, Z.W., Kim, J.H., Loganathan, G.V.: New heuristic optimization algorithm: Harmony search. Simulation 76(2), 60–68 (2001)

    CrossRef  Google Scholar 

  2. Al-Betar, M.A., Khader, A.T.: A harmony search algorithm for university course timetabling. Ann. Oper. Res. 194(1), 3–31 (2012)

    MATH  MathSciNet  CrossRef  Google Scholar 

  3. Wang, L., Pan, Q.K., Tasgetiren, M.F.: Minimizing the total flow time in a flow shop with blocking by using hybrid harmony search algorithms. Expert Syst. Appl. 37(12), 7929–7936 (2010)

    CrossRef  Google Scholar 

  4. Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Ser, J.D., Bilbao, M., Salcedo-Sanz, S., Geem, Z.: A survey on applications of the harmony search algorithm. Eng. Appl. Artif. Intell. 26(8), 1818–1831 (2013)

    CrossRef  Google Scholar 

  5. Cowling, P.I., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)

    CrossRef  Google Scholar 

  6. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.: A classification of hyper-heuristics approaches. In: Handbook of Metaheuristics, 2nd edn., pp. 49–468 (2010)

    Google Scholar 

  7. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: A survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)

    CrossRef  Google Scholar 

  8. Drake, J.H., Özcan, E., Burke, E.K.: An improved choice function heuristic selection for cross domain heuristic search. In: Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 307–316. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  9. Drake, J.H., Özcan, E., Burke, E.K.: Modified choice function heuristic selection for the multidimensional knapsack problem. In: Sun, H., Yang, Chin-Yu., Lin, C.-W., Pan, J.-S., Snasel, V., Abraham, A. (eds.) Genetic and Evolutionary Computing. AISC, vol. 329, pp. 225–234. Springer, Heidelberg (2015)

    Google Scholar 

  10. Drake, J.H., Hyde, M., Ibrahim, K., Özcan, E.: A genetic programming hyper-heuristic for the multidimensional knapsack problem. Kybernetes 43(9–10), 1500–1511 (2014)

    Google Scholar 

  11. Drake, J.H., Kililis, N., Özcan, E.: Generation of VNS components with grammatical evolution for vehicle routing. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 25–36. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  12. López-Camacho, E., Terashima-Marín, H., Ross, P.: A hyper-heuristic for solving one and two-dimensional bin packing problems. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 257–258 (2011)

    Google Scholar 

  13. Kiraz, B., Uyar, A.S., Özcan, E.: Selection hyper-heuristics in dynamic environments. J. Oper. Res. Soc. 64(12), 1753–1769 (2013)

    CrossRef  Google Scholar 

  14. Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyper-heuristic for timetabling and rostering. J. Heuristics 9(6), 451–470 (2003)

    CrossRef  Google Scholar 

  15. Özcan, E., Misir, M., Ochoa, G., Burke, E.K.: A reinforcement learning – great deluge hyper-heuristic for examination timetabling. International Journal of Applied Metaheuristic Computing 1(1), 39–59 (2010)

    CrossRef  Google Scholar 

  16. Drake, J.H., Özcan, E., Burke, E.K.: A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem. Evolutionary computation (2015)

    Google Scholar 

  17. Fisher, H., Thompson, G.: Probabilistic learning combinations of local job-shop scheduling rules. In: Factory Scheduling Conference, Carnegie Institute of Technology (1961)

    Google Scholar 

  18. Gibbs, J., Kendall, G., Özcan, E.: Scheduling english football fixtures over the holiday period using hyper-heuristics. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 496–505. Springer, Heidelberg (2010)

    Google Scholar 

  19. Garrido, P., Castro, C.: Stable solving of cvrps using hyperheuristics. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 255–262 (2009)

    Google Scholar 

  20. Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. International series in operations research and management science, 457–474 (2003)

    Google Scholar 

  21. Özcan, E., Bilgin, B., Korkmaz, E.E.: Hill climbers and mutational heuristics in hyperheuristics. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 202–211. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  22. Ochoa, G., Hyde, M., Curtois, T., Vazquez-Rodriguez, J. A., Walker, J., Gendreau, M., et al.: Hyflex: a benchmark framework for cross-domain heuristic search. In: Evolutionary Computation in Combinatorial Optimization, pp. 136–147 (2012)

    Google Scholar 

  23. 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, C.A. (ed.) LION 2011. LNCS, vol. 6683, pp. 631–634. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  24. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program. C3P Report, 826 (1989)

    Google Scholar 

  25. Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (2006)

    Google Scholar 

  26. Ochoa, G., Walker, J., Hyde, M., Curtois, T.: Adaptive evolutionary algorithms and extensions to the hyflex hyper-heuristic framework. In: Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 418–427. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  27. Anwar, K., Khader, A.T., Al-Betar, M.A., Awadallah, M.: Harmony search-based hyper-heuristic for examination timetabling. In: 2013 IEEE 9th International Colloquium on Signal Processing and its Applications (CSPA), pp. 176–181 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paul Dempster .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dempster, P., Drake, J.H. (2016). Two Frameworks for Cross-Domain Heuristic and Parameter Selection Using Harmony Search. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-47926-1_10

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47925-4

  • Online ISBN: 978-3-662-47926-1

  • eBook Packages: EngineeringEngineering (R0)