IDEAS home Printed from https://ideas.repec.org/h/spr/lnechp/978-3-540-85646-7_7.html
   My bibliography  Save this book chapter

Clusters of Non-dominated Solutions in Multiobjective Combinatorial Optimization: An Experimental Analysis

In: Multiobjective Programming and Goal Programming

Author

Listed:
  • Luís Paquete

    (University of Coimbra)

  • Thomas Stützle

    (Université Libre de Bruxelles)

Abstract

This paper presents an analysis on the level of clustering of approximate non-dominated solutions for several instances of the Biobjective Travelling Salesman Problem and the Biobjective Quadratic Assignment Problem. The sets of approximate non-dominated solutions are identified by high performing stochastic local search algorithms. A cluster is here defined as a set of non-dominated solutions such that for any solution of the set there is at least one other that is at a maximum distance k for a given neighborhood function. Of Particular interest is to find k for which all approximate non-dominated solutions are in a single cluster. The results obtained from this analysis indicate that the degree of clustering depends strongly on the problem but also on the type of instances of each problem. These insights also suggest that different general-purpose search strategies should be used for the two problems and also for different instance features.

Suggested Citation

  • Luís Paquete & Thomas Stützle, 2009. "Clusters of Non-dominated Solutions in Multiobjective Combinatorial Optimization: An Experimental Analysis," Lecture Notes in Economics and Mathematical Systems, in: Vincent Barichard & Matthias Ehrgott & Xavier Gandibleux & Vincent T'Kindt (ed.), Multiobjective Programming and Goal Programming, pages 69-77, Springer.
    • Handle: RePEc:spr:lnechp:978-3-540-85646-7_7
    DOI: 10.1007/978-3-540-85646-7_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lnechp:978-3-540-85646-7_7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.