Skip to main content

Interleaving Constraint Propagation: An Efficient Cooperative Search with Branch and Bound

  • Conference paper
Hybrid Metaheuristics (HM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7919))

Included in the following conference series:

Abstract

The main characteristic of any constraint solver is Constraint propagation. Then it is very important to be able to manage constraint propagation as efficiently as possible, we present a hybrid solver based on a Branch and Bound algorithm combined with constraint propagation to reduce the search space. Based on some observations of the solving process constraint propagation is triggered by some rules. The results show that constraint propagation is profitable, but also that it is too costly to be executed at each node of the search tree, we show that is possible to make reasonable use of constraint propagation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Apt, K.R.: Principles of Constraint Programming. Cambridge Univ. Press (2003)

    Google Scholar 

  2. Beasley, J.E.: Or-library: distributing test problems by electronic mail. JORS 41(11), 1069–1072 (1990)

    Google Scholar 

  3. Berkelaar, M.: lpsolve—simplex-based code for linear and integer programming

    Google Scholar 

  4. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)

    Article  Google Scholar 

  5. Castro, C., Monfroy, E., Figueroa, C., Meneses, R.: An approach for dynamic split strategies in constraint solving. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 162–174. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Crawford, B., Soto, R., Monfroy, E., Palma, W., Castro, C., Paredes, F.: Parameter tuning of a choice-function based hyperheuristic using particle swarm optimization. Expert Syst. Appl. 40(5), 1690–1695 (2013)

    Article  Google Scholar 

  7. El-Abd, M., Kamel, M.: A taxonomy of cooperative search algorithms. In: Blesa, M.J., Blum, C., Roli, A., Sampels, M. (eds.) HM 2005. LNCS, vol. 3636, pp. 32–41. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Gent, I., Walsh, T.: Csplib: a benchmark library for constraints. Technical report, APES-09-1999 (1999), http://csplib.cs.strath.ac.uk/

  9. Hamadi, Y. Monfroy, E., Saubion, F.: What is autonomous search? In: The Ten years of CP-AI-OR. Springer (2010) (to appear)

    Google Scholar 

  10. Hamadi, Y., Monfroy, E., Saubion, F. (eds.): Autonomous Search. Springer (2012)

    Google Scholar 

  11. Monfroy, E., Castro, C., Crawford, B.: Adaptive enumeration strategies and metabacktracks for constraint solving. In: Yakhno, T., Neuhold, E.J. (eds.) ADVIS 2006. LNCS, vol. 4243, pp. 354–363. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Monfroy, E., Castro, C., Crawford, B., Figueroa, C.: Adaptive hybridization strategies. In: ACM Symposium on Applied Computing, pp. 922–923 (2011)

    Google Scholar 

  13. Monfroy, E., Castro, C., Crawford, B., Soto, R., Paredes, F., Figueroa, C.: A reactive and hybrid constraint solver. Journal of Experimental and Theoretical Artificial Intelligence 25(1), 1–22 (2013)

    Article  Google Scholar 

  14. Rice, J.: The algorithm selection problem. Technical Report CSD-TR 152, Purdue Univ. (1975)

    Google Scholar 

  15. Rodosek, R., Wallace, M., Hajian, M.: A new approach to integrating mixed integer programming and clp. Baltzer Journal (1998)

    Google Scholar 

  16. Schulte, C., Tack, G., Lagerkvist, M.: Gecode: Generic constraint development environment. In: INFORMS Annual Meeting (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Monfroy, E., Crawford, B., Soto, R. (2013). Interleaving Constraint Propagation: An Efficient Cooperative Search with Branch and Bound. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. HM 2013. Lecture Notes in Computer Science, vol 7919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38516-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38516-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38515-5

  • Online ISBN: 978-3-642-38516-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics