Skip to main content

Search Bias in Constructive Metaheuristics and Implications for Ant Colony Optimisation

  • Conference paper
Ant Colony Optimization and Swarm Intelligence (ANTS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3172))

Abstract

Constructive metaheuristics explore a tree of constructive decisions, the topology of which is determined by the way solutions are represented and constructed. Some solution representations allow particular solutions to be reached on a greater number of paths in this construction tree than other solutions, which can introduce a bias to the search. A bias can also be introduced by the topology of the construction tree. This is particularly the case in problems where certain solution representations are infeasible. This paper presents an examination of the mechanisms that determine the topologies of construction trees and the implications for ant colony optimisation. The results provide insights into why certain assignment orders perform better in problems such as the quadratic and generalised assignment problems, in terms of both solution quality and avoiding infeasible solutions.

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 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Beasley, J.E.: OR-library: Distributing test problems by electronic mail. J. Oper. Res. Soc. 41, 1069–1072 (1990)

    Google Scholar 

  2. Blum, C.: Theoretical and practical aspects of ant colony optimization. PhD dissertation, Université Libre de Bruxelles (2004)

    Google Scholar 

  3. Blum, C., Sampels, M.: Ant colony optimization for fop shop scheduling: A case study on different pheromone representations. In: Proceedings of CEC 2002, pp. 1558–1563 (2002)

    Google Scholar 

  4. Blum, C., Sampels, M.: When model bias is stronger than selection pressure. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 893–902. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Blum, C., Sampels, M., Zlochin, M.: On a particularity in model-based search. In: Proceedings of GECCO 2002, New York, pp. 35–42 (2002)

    Google Scholar 

  6. Costa, D., Hertz, A.: Ants can colour graphs. J. Oper. Res. Soc. 48, 295–305 (1997)

    MATH  Google Scholar 

  7. Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  8. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Trans. Sys. Man Cyb. B 26(1), 1–13 (1996)

    Google Scholar 

  9. Leguizamón, G., Michalewicz, Z.: A new version of ant system for subset problems. In: Proceedings of CEC 1999, pp. 1459–1464 (1999)

    Google Scholar 

  10. Lourenço, H.R., Serra, D.: Adapative search heuristics for the generalized assignment problem. Mathware Soft Comput. 9(2), 209–234 (2002)

    MATH  MathSciNet  Google Scholar 

  11. Maniezzo, V., Colorni, A.: The ant system applied to the quadratic assignment problem. IEEE Trans. Knowledge Data Eng. 11(5), 769–778 (1999)

    Article  Google Scholar 

  12. Montgomery, J.: Search bias in constructive metaheuristics and implications for ant colony optimisation. Technical Report TR04-04, Faculty of Information Technology, Bond University, Australia (2004)

    Google Scholar 

  13. Randall, M.: Heuristics for ant colony optimisation using the generalised assignment problem. In: Proceedings of CEC 2004, Portland, OR, USA (2004)

    Google Scholar 

  14. Solnon, C.: Ants can solve constraint satisfaction problems. IEEE Trans. Evol. Comput. 6(4) (2001)

    Google Scholar 

  15. Taillard, É.D., Gambardella, L.M.: Adaptive memories for the quadratic assignment problem. Technical Report IDSIA-87-97, IDSIA (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Montgomery, J., Randall, M., Hendtlass, T. (2004). Search Bias in Constructive Metaheuristics and Implications for Ant Colony Optimisation. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2004. Lecture Notes in Computer Science, vol 3172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28646-2_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-28646-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22672-7

  • Online ISBN: 978-3-540-28646-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics