Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2013: Intelligent Data Engineering and Automated Learning – IDEAL 2013 pp 594–602Cite as

Multilevel Bee Swarm Optimization for Large Satisfiability Problem Instances

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

Abstract

Nowadays, real word problems are often characterized by very large and complex search spaces. Finding high-quality solutions to NP-hard problems in a scalable and efficient manner has become a priority in the field of combinatorial optimization. This paper presents a new framework to handle large satisfiability problem instances (SAT) using a method designed by hybridizing two promising approaches. The first one is a bio-inspired metaheuristic called Bee Swarm Optimization (BSO) and the second one is the multilevel paradigm. The results obtained by comparing BSO with and without the multilevel concept showed that the latter improved significantly the performance of BSO.

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

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   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Drias, H., Sadeg, S., Yahi, S.: Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 318–325. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  2. Walshaw, C.: Multilevel Refinement for Combinatorial Optimisation: Boosting Metaheuristic Performance. In: Blum, C., Aguilera, M.J.B., Roli, A., Sampels, M. (eds.) Hybrid Metaheuristics. SCI, vol. 114, pp. 261–289. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Cook, S.: The Complexity of Theorem-Proving Procedures. In: Proceeding 3rd Annual ACM Symposium on the Theory of Computing, New York, pp. 151–198 (1971)

    Google Scholar 

  4. Davis, M., Logemann, G., Loveland, D.: A Machine Program for Theorem Proving. Communications of the ACM 5(7), 394–397 (1962), doi:10.1145/368273.368557

    Article  MathSciNet  MATH  Google Scholar 

  5. Biere, A., Heule, M., Maaren, H.V., Walsh, T.: A history of Satisfiability. In: Handbook of Satisfiability, pp. 3–55. IOS Press, Amsterdan (2009)

    Google Scholar 

  6. Bouhmala, N.: A Multilevel Memetic Algorithm for Large SAT-Encoded Problems. Evolutionary Computation 20(4), 641–664 (2012)

    Article  Google Scholar 

  7. Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artificial Intelligence Review 31(1-4), 61–85 (2009)

    Article  Google Scholar 

  8. Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing 11(6), 4135–4151 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, USTHB, LRIA, Algiers, Algeria

    Marwa Djeffal & Habiba Drias

Authors
  1. Marwa Djeffal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Habiba Drias
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Djeffal, M., Drias, H. (2013). Multilevel Bee Swarm Optimization for Large Satisfiability Problem Instances. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_72

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41278-3_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation