IDEAS home Printed from https://ideas.repec.org/a/spr/joheur/v25y2019i4d10.1007_s10732-018-9384-y.html
   My bibliography  Save this article

Diversification-based learning in computing and optimization

Author

Listed:
  • Fred Glover

    (University of Colorado – Boulder
    OptTek Systems)

  • Jin-Kao Hao

    (LERIA, Université d’Angers
    Institut Universitaire de France)

Abstract

Diversification-based learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent opposition-based learning (OBL) framework introduced in Tizhoosh (in: Proceedings of international conference on computational intelligence for modelling, control and automation, and international conference on intelligent agents, web technologies and internet commerce (CIMCA/IAWTIC-2005), pp 695–701, 2005), which has become the focus of numerous research initiatives in machine learning and metaheuristic optimization. We unify and extend earlier proposals in metaheuristic search (Glover, in Hao J-K, Lutton E, Ronald E, Schoenauer M, Snyers D (eds) Artificial evolution, Lecture notes in computer science, Springer, Berlin, vol 1363, pp 13–54, 1997; Glover and Laguna Tabu search, Springer, Berlin, 1997) to give a collection of approaches that are more flexible and comprehensive than OBL for creating intensification and diversification strategies in metaheuristic search. We also describe potential applications of DBL to various subfields of machine learning and optimization.

Suggested Citation

  • Fred Glover & Jin-Kao Hao, 2019. "Diversification-based learning in computing and optimization," Journal of Heuristics, Springer, vol. 25(4), pages 521-537, October.
    • Handle: RePEc:spr:joheur:v:25:y:2019:i:4:d:10.1007_s10732-018-9384-y
    DOI: 10.1007/s10732-018-9384-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10732-018-9384-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10732-018-9384-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fred Glover, 2014. "Exterior Path Relinking for Zero-One Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 5(3), pages 1-8, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emanuel Vega & Ricardo Soto & Broderick Crawford & Javier Peña & Carlos Castro, 2021. "A Learning-Based Hybrid Framework for Dynamic Balancing of Exploration-Exploitation: Combining Regression Analysis and Metaheuristics," Mathematics, MDPI, vol. 9(16), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
    2. Fernando Stefanello & Vaneet Aggarwal & Luciana S. Buriol & Mauricio G. C. Resende, 2019. "Hybrid algorithms for placement of virtual machines across geo-separated data centers," Journal of Combinatorial Optimization, Springer, vol. 38(3), pages 748-793, October.
    3. Ricardo N. Liang & Eduardo A. J. Anacleto & Cláudio N. Meneses, 2022. "Data structures for speeding up Tabu Search when solving sparse quadratic unconstrained binary optimization problems," Journal of Heuristics, Springer, vol. 28(4), pages 433-479, August.

    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:joheur:v:25:y:2019:i:4:d:10.1007_s10732-018-9384-y. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.