IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/162712.html
   My bibliography  Save this article

A Study on Many-Objective Optimization Using the Kriging-Surrogate-Based Evolutionary Algorithm Maximizing Expected Hypervolume Improvement

Author

Listed:
  • Chang Luo
  • Koji Shimoyama
  • Shigeru Obayashi

Abstract

The many-objective optimization performance of the Kriging-surrogate-based evolutionary algorithm (EA), which maximizes expected hypervolume improvement (EHVI) for updating the Kriging model, is investigated and compared with those using expected improvement (EI) and estimation (EST) updating criteria in this paper. Numerical experiments are conducted in 3- to 15-objective DTLZ1-7 problems. In the experiments, an exact hypervolume calculating algorithm is used for the problems with less than six objectives. On the other hand, an approximate hypervolume calculating algorithm based on Monte Carlo sampling is adopted for the problems with more objectives. The results indicate that, in the nonconstrained case, EHVI is a highly competitive updating criterion for the Kriging model and EA based many-objective optimization, especially when the test problem is complex and the number of objectives or design variables is large.

Suggested Citation

  • Chang Luo & Koji Shimoyama & Shigeru Obayashi, 2015. "A Study on Many-Objective Optimization Using the Kriging-Surrogate-Based Evolutionary Algorithm Maximizing Expected Hypervolume Improvement," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-15, October.
    • Handle: RePEc:hin:jnlmpe:162712
    DOI: 10.1155/2015/162712
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2015/162712.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2015/162712.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2015/162712?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
    ---><---

    Citations

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


    Cited by:

    1. Yuri Galerkin & Aleksey Rekstin & Lyubov Marenina & Aleksandr Drozdov & Olga Solovyeva & Vasiliy Semenovskiy, 2020. "Optimization of Return Channels of High Flow Rate Centrifugal Compressor Stages Using CFD Methods," Energies, MDPI, vol. 13(22), pages 1-23, November.

    More about this item

    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:hin:jnlmpe:162712. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.