IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-319-26024-2_6.html
   My bibliography  Save this book chapter

Performance of an Intensification Strategy Based on Learning in a Metaheuristic: Meta-RaPS with Path Relinking

In: Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling

Author

Listed:
  • Arif Arin

    (Middle East Technical University, Universiteler Mahallesi)

  • Ghaith Rabadi

    (Old Dominion University)

Abstract

Intensification and diversification in metaheuristics are two main strategies to enhance the search process and solution quality. In Meta-RaPS (Metaheuristic for Randomized Priority Search), a recent memoryless metaheuristic, intensification and diversification strategies are controlled only by the level of randomness specified by its parameters. We introduce in this paper a Path Relinking (PR) learning algorithm and integrate it into Meta-RaPS to intelligently enhance its intensification capability by learning “good” attributes of the best solutions. To evaluate its performance, the proposed Meta-RaPS PR is tested on the 0-1 Multidimensional Knapsack Problem (MKP). The results show that applying PR as an intensification strategy in Meta-RaPS is very effective as it outperformed other approaches used in the literature with this problem. The PR approach also transformed the memoryless nature of Meta-RaPS into an “intelligent” algorithm.

Suggested Citation

  • Arif Arin & Ghaith Rabadi, 2016. "Performance of an Intensification Strategy Based on Learning in a Metaheuristic: Meta-RaPS with Path Relinking," International Series in Operations Research & Management Science, in: Ghaith Rabadi (ed.), Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, edition 1, chapter 0, pages 109-126, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-26024-2_6
    DOI: 10.1007/978-3-319-26024-2_6
    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:isochp:978-3-319-26024-2_6. 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.