IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-030-52119-6_13.html
   My bibliography  Save this book chapter

Approximate Bilevel Optimization with Population-Based Evolutionary Algorithms

In: Bilevel Optimization

Author

Listed:
  • Kalyanmoy Deb

    (Michigan State University)

  • Ankur Sinha

    (Indian Institute of Management)

  • Pekka Malo

    (Aalto University School of Business)

  • Zhichao Lu

    (Michigan State University)

Abstract

Population-based optimization algorithms, such as evolutionary algorithms, have enjoyed a lot of attention in the past three decades in solving challenging search and optimization problems. In this chapter, we discuss recent population-based evolutionary algorithms for solving different types of bilevel optimization problems, as they pose numerous challenges to an optimization algorithm. Evolutionary bilevel optimization (EBO) algorithms are gaining attention due to their flexibility, implicit parallelism, and ability to customize for specific problem solving tasks. Starting with surrogate-based single-objective bilevel optimization problems, we discuss how EBO methods are designed for solving multi-objective bilevel problems. They show promise for handling various practicalities associated with bilevel problem solving. The chapter concludes with results on an agro-economic bilevel problem. The chapter also presents a number of challenging single and multi-objective bilevel optimization test problems, which should encourage further development of more efficient bilevel optimization algorithms.

Suggested Citation

  • Kalyanmoy Deb & Ankur Sinha & Pekka Malo & Zhichao Lu, 2020. "Approximate Bilevel Optimization with Population-Based Evolutionary Algorithms," Springer Optimization and Its Applications, in: Stephan Dempe & Alain Zemkoho (ed.), Bilevel Optimization, chapter 0, pages 361-402, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-52119-6_13
    DOI: 10.1007/978-3-030-52119-6_13
    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:spochp:978-3-030-52119-6_13. 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.