Skip to main content
SpringerLink
Log in

An Experimental Study on Ant Colony Optimization Hyper-Heuristics for Solving the Knapsack Problem

  • Conference paper
  • First Online:
Pattern Recognition (MCPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10880))

Included in the following conference series:

Abstract

The knapsack problem is a fundamental problem that has been extensively studied in combinatorial optimization. The reason is that such a problem has many practical applications. Several solution techniques have been proposed in the past, but their performance is usually limited by the complexity of the problem. Hence, this paper studies a novel hyper-heuristic approach based on the ant colony optimization algorithm to solve the knapsack problem. The hyper-heuristic is used to produce rules that decide which heuristic to apply given the current problem state of the instance being solved. We test the hyper-heuristic model on sets with a variety of knapsack problem instances. Our resulting data seems promising.

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

Access this chapter

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 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
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

References

  1. Schiff, K.: Ant colony optimization algorithm for the 0–1 knapsack problem. Technical Transactions. Autom. Control R 110(AC-3), 39–52 (2013)

    Google Scholar 

  2. Sahni, S.: Approximate algorithms for the 0/1 knapsack problem. J. ACM 22(1), 115–124 (1975)

    Article  MathSciNet  Google Scholar 

  3. Burke, E.K., Gendreau, M., 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)

    Article  Google Scholar 

  4. Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Company, Scituate (2004)

    MATH  Google Scholar 

  5. Zhang, J.: Comparative study of several intelligent algorithms for knapsack problem. Procedia Environ. Sci. 11, 163–168 (2011)

    Article  Google Scholar 

  6. Ke, L., Feng, Z., Ren, Z., Wei, X.: An ant colony optimization approach for the multidimensional knapsack problem. J. Heuristics 16(1), 65–83 (2010)

    Article  Google Scholar 

  7. Ren, Z., Feng, Z.: An ant colony optimization approach to the multiple-choice multidimensional knapsack problem. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, GECCO 2010, pp. 281–288. ACM (2010)

    Google Scholar 

  8. Du, D., Zu, Y.: Greedy strategy based self-adaption ant colony algorithm for 0/1 knapsack problem. In: Park, J.J.J.H., Pan, Y., Chao, H.-C., Yi, G. (eds.) Ubiquitous Computing Application and Wireless Sensor. LNEE, vol. 331, pp. 663–670. Springer, Dordrecht (2015). https://doi.org/10.1007/978-94-017-9618-7_70

    Chapter  Google Scholar 

  9. He, L., Huang, Y.: Research of ant colony algorithm and the application of 0/1 knapsack. In: 2011 6th International Conference on Computer Science Education (ICCSE), pp. 464–467 (2011)

    Google Scholar 

  10. Changdar, C., Mahapatra, G.S., Pal, R.K.: An ant colony optimization approach for binary knapsack problem under fuzziness. Appl. Math. Comput. 223, 243–253 (2013)

    MathSciNet  MATH  Google Scholar 

  11. Aziz, Z.A.: Ant colony hyper-heuristics for travelling salesman problem. Procedia Comput. Sci. 76, 534–538 (2015)

    Article  Google Scholar 

  12. Chen, P.C., Kendall, G., Berghe, G.V.: An ant based hyper-heuristic for the travelling tournament problem. In: 2007 IEEE Symposium on Computational Intelligence in Scheduling, pp. 19–26 (2007)

    Google Scholar 

  13. Ferreira, A.S., Pozo, A., Gonçalves, R.A.: An ant colony based hyper-heuristic approach for the set covering problem. Adv. Distrib. Comput. Artif. Intell. J. 4, 1–21 (2015)

    Google Scholar 

  14. Cuesta-Cañada, A., Garrido, L., Terashima-Marín, H.: Building hyper-heuristics through ant colony optimization for the 2D bin packing problem. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 654–660. Springer, Heidelberg (2005). https://doi.org/10.1007/11554028_91

    Chapter  Google Scholar 

  15. López-Camacho, E., Terashima-Marin, H., Ross, P., Ochoa, G.: A unified hyper-heuristic framework for solving bin packing problems. Expert Syst. Appl. 41(15), 6876–6889 (2014)

    Article  Google Scholar 

  16. Gaertner, D., Clark, K.: On optimal parameters for ant colony optimization algorithms. In: Proceedings of the International Conference on Artificial Intelligence 2005, pp. 83–89. CSREA Press (2005)

    Google Scholar 

  17. Wei, X.: Parameters analysis for basic ant colony optimization algorithm in TSP. Int. J. u- e-Serv. Sci. Technol. 7(4), 159–170 (2014)

    Article  Google Scholar 

  18. Pisinger, D.: Where are the hard knapsack problems? Comput. Opera. Res. 32(9), 2271–2284 (2005)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This research was partially supported by CONACyT Basic Science Projects under grants 241461, 221551 and 287479, and ITESM Research Group with Strategic Focus in Intelligent Systems.

Author information

Authors and Affiliations

  1. Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico

    Bronson Duhart, Fernando Camarena, José Carlos Ortiz-Bayliss, Ivan Amaya & Hugo Terashima-Marín

Authors
  1. Bronson Duhart
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Camarena
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Carlos Ortiz-Bayliss
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ivan Amaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hugo Terashima-Marín
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Carlos Ortiz-Bayliss .

Editor information

Editors and Affiliations

  1. National Institute of Astrophysics, Optics and Electronics, Sta. Maria Tonantzintla, Puebla, Mexico

    José Francisco Martínez-Trinidad

  2. National Institute of Astrophysics, Optics and Electronics, Sta. Maria Tonantzintla, Puebla, Mexico

    Jesús Ariel Carrasco-Ochoa

  3. Autonomous University of Puebla, Puebla, Puebla, Mexico

    José Arturo Olvera-López

  4. University of South Florida, Tampa, Florida, USA

    Sudeep Sarkar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Duhart, B., Camarena, F., Ortiz-Bayliss, J.C., Amaya, I., Terashima-Marín, H. (2018). An Experimental Study on Ant Colony Optimization Hyper-Heuristics for Solving the Knapsack Problem. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-92198-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92197-6

  • Online ISBN: 978-3-319-92198-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation