IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v57y2013i2p583-611.html
   My bibliography  Save this article

Differential evolution with multi-constraint consensus methods for constrained optimization

Author

Listed:
  • Noha Hamza
  • Ruhul Sarker
  • Daryl Essam

Abstract

Constrained optimization is an important research topic that assists in quality planning and decision making. To solve such problems, one of the important aspects is to improve upon any constraint violation, and thus bring infeasible individuals to the feasible region. To achieve this goal, different constraint consensus methods have been introduced, but no single method performs well for all types of problems. Hence, in this research, for solving constrained optimization problems, we introduce different variants of the Differential Evolution algorithm, with multiple constraint consensus methods. The proposed algorithms are tested and analyzed by solving a set of well-known bench mark problems. For further improvements, a local search is applied to the best variant. We have compared our algorithms among themselves, as well as with other state of the art algorithms. Those comparisons show similar, if not better performance, while also using significantly lower computational time. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Noha Hamza & Ruhul Sarker & Daryl Essam, 2013. "Differential evolution with multi-constraint consensus methods for constrained optimization," Journal of Global Optimization, Springer, vol. 57(2), pages 583-611, October.
  • Handle: RePEc:spr:jglopt:v:57:y:2013:i:2:p:583-611
    DOI: 10.1007/s10898-012-9987-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10898-012-9987-z
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10898-012-9987-z?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. Y. Xiao & D. Michalski & J.M. Galvin & Y. Censor, 2003. "The Least-Intensity Feasible Solution for Aperture-Based Inverse Planning in Radiation Therapy," Annals of Operations Research, Springer, vol. 119(1), pages 183-203, March.
    2. John W. Chinneck, 2004. "The Constraint Consensus Method for Finding Approximately Feasible Points in Nonlinear Programs," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 255-265, August.
    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. Jose-Cruz Nuñez-Perez & Vincent-Ademola Adeyemi & Yuma Sandoval-Ibarra & Francisco-Javier Perez-Pinal & Esteban Tlelo-Cuautle, 2021. "Maximizing the Chaotic Behavior of Fractional Order Chen System by Evolutionary Algorithms," Mathematics, MDPI, vol. 9(11), pages 1-22, May.

    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. Matthias Ehrgott & Çiğdem Güler & Horst Hamacher & Lizhen Shao, 2010. "Mathematical optimization in intensity modulated radiation therapy," Annals of Operations Research, Springer, vol. 175(1), pages 309-365, March.
    2. Laurence Smith & John Chinneck & Victor Aitken, 2013. "Improved constraint consensus methods for seeking feasibility in nonlinear programs," Computational Optimization and Applications, Springer, vol. 54(3), pages 555-578, April.
    3. John W. Chinneck, 2004. "The Constraint Consensus Method for Finding Approximately Feasible Points in Nonlinear Programs," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 255-265, August.
    4. Shafiu Jibrin & James W. Swift, 2015. "Constraint Consensus Methods for Finding Strictly Feasible Points of Linear Matrix Inequalities," Journal of Optimization, Hindawi, vol. 2015, pages 1-16, January.
    5. Gino J. Lim & Michael C. Ferris & Stephen J. Wright & David M. Shepard & Matthew A. Earl, 2007. "An Optimization Framework for Conformal Radiation Treatment Planning," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 366-380, August.
    6. Michael Ferris & Rikhardur Einarsson & Ziping Jiang & David Shepard, 2006. "Sampling issues for optimization in radiotherapy," Annals of Operations Research, Springer, vol. 148(1), pages 95-115, November.
    7. Richard J. Caron & Tim Traynor & Shafiu Jibrin, 2010. "Feasibility and Constraint Analysis of Sets of Linear Matrix Inequalities," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 144-153, February.
    8. Felisa Preciado-Walters & Mark Langer & Ronald Rardin & Van Thai, 2006. "Column generation for IMRT cancer therapy optimization with implementable segments," Annals of Operations Research, Springer, vol. 148(1), pages 65-79, November.

    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:jglopt:v:57:y:2013:i:2:p:583-611. 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.