Skip to main content
SpringerLink
Log in

An Ant-Based Framework for Very Strongly Constrained Problems

  • Conference paper
  • First Online:
Ant Algorithms (ANTS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2463))

Included in the following conference series:

Abstract

Metaheuristics in general and ant-based systems in particular have shown remarkable success in solving combinatorial optimization problems. However, a few problems exist for which the best performing heuristic algorithm is not a metaheuristic. These few are often characterized by a very highly constrained search space. This is a situation in which it is not possible to de.ne any e.cient neighborhood, thus no local search is available. The paradigmatic case is the set partitioning problem, a problem for which standard Integer Programming solvers outperform metaheuristics. This paper presents an extended ant framework improving the e.ectiveness of ant-based systems to such problems. Computational results are presented both on standard set partitioning problem instances and on vertical fragmentation problem instances. This last is a real world problem arising in data warehouse logical design.

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 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.

References

  1. A. Atamtürk, G.L. Nemhauser, and M.W.P. Savelsbergh. A combined lagrangian, linear programming and implication heuristic for large-scale set partitioning problems. Journal of Heuristics, 1:247–259, 1995.

    Article  Google Scholar 

  2. P.C. Chu and J.E. Beasley. Constraint handling in genetic algorithms: the set partitoning problem. Journal of Heuristics, 4:323–357, 1998.

    Article  MATH  Google Scholar 

  3. M. Golfarelli, V. Maniezzo, and S. Rizzi. Materialization of Fragmented Views in Multidimensional Databases. Technical Report TR-001-02, Scienze dell’Informazione, University of Bologna, 2002.

    Google Scholar 

  4. V. Maniezzo. Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing, 11(4):358–369, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  5. V. Maniezzo, A. Carbonaro, M. Golfarelli, and S. Rizzi. An ANTS Algorithm for Optimizing the Materialization of Fragmented Views in Data Warehouses: Preliminary Results. In Applications of Evolutionary Computing, volume 2037 of Lecture Notes in Computer Science, pages 80–89. Springer Verlag, 2001.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bologna, Italy

    Vittorio Maniezzo & Matteo Milandri

Authors
  1. Vittorio Maniezzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matteo Milandri
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IRIDIA, Université de Bruxelles, CP 194/6 Avenue Franklin D. Roosevelt 50, 1050, Brussels, Belgium

    Marco Dorigo , Gianni Di Caro  & Michael Sampels ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Maniezzo, V., Milandri, M. (2002). An Ant-Based Framework for Very Strongly Constrained Problems. In: Dorigo, M., Di Caro, G., Sampels, M. (eds) Ant Algorithms. ANTS 2002. Lecture Notes in Computer Science, vol 2463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45724-0_19

Download citation

  • DOI: https://doi.org/10.1007/3-540-45724-0_19

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44146-5

  • Online ISBN: 978-3-540-45724-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation