IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2014-12.html
   My bibliography  Save this paper

Efficient Identification of the Pareto Optimal Set

Author

Listed:
  • Ingrida Steponavice
  • Rob J Hyndman
  • Kate Smith-Miles
  • Laura Villanova

Abstract

In this paper, we focus on expensive multiobjective optimization problems and propose a method to predict an approximation of the Pareto optimal set using classification of sampled decision vectors as dominated or nondominated. The performance of our method, called EPIC, is demonstrated on a set of benchmark problems used in the multiobjective optimization literature and compared with state-of the-art methods, ParEGO and PAL. The initial results are promising and encourage further research in this direction.

Suggested Citation

  • Ingrida Steponavice & Rob J Hyndman & Kate Smith-Miles & Laura Villanova, 2014. "Efficient Identification of the Pareto Optimal Set," Monash Econometrics and Business Statistics Working Papers 12/14, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2014-12
    as

    Download full text from publisher

    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp12-14.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Ingrida Steponavičė & Rob J. Hyndman & Kate Smith-Miles & Laura Villanova, 2017. "Dynamic algorithm selection for pareto optimal set approximation," Journal of Global Optimization, Springer, vol. 67(1), pages 263-282, January.

    More about this item

    Keywords

    multiobjective optimization; classification; expensive black-box function;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

    Statistics

    Access and download statistics

    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:msh:ebswps:2014-12. 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: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.html .

    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.