IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-38391-5_105.html
   My bibliography  Save this book chapter

Improved Evolutionary Strategy Genetic Algorithm for Nonlinear Programming Problems

In: The 19th International Conference on Industrial Engineering and Engineering Management

Author

Listed:
  • Hui-xia Zhu

    (Northeast Agriculture University)

  • Fu-lin Wang

    (Northeast Agriculture University)

  • Wen-tao Zhang

    (Northeast Agriculture University)

  • Qian-ting Li

    (Northeast Agriculture University)

Abstract

Genetic algorithms have unique advantages in dealing with optimization problems. In this paper the main focus is on the improvement of a genetic algorithm and its application in nonlinear programming problems. In the evolutionary strategy algorithm, the optimal group preserving method was used and individuals with low fitness values were mutated. The crossover operator uses the crossover method according to the segmented mode of decision variables. This strategy ensured that each decision variable had the opportunity to produce offspring by crossover, thus, speeding up evolution. In optimizing the nonlinear programming problem with constraints, the correction operator method was introduced to improve the feasible degree of infeasible individuals. MATLAB simulation results confirmed the validity of the proposed method. The method can effectively solve nonlinear programming problems with greatly improved solution quality and convergence speed, making it an effective, reliable and convenient method.

Suggested Citation

  • Hui-xia Zhu & Fu-lin Wang & Wen-tao Zhang & Qian-ting Li, 2013. "Improved Evolutionary Strategy Genetic Algorithm for Nonlinear Programming Problems," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), The 19th International Conference on Industrial Engineering and Engineering Management, edition 127, chapter 0, pages 993-1003, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-38391-5_105
    DOI: 10.1007/978-3-642-38391-5_105
    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:sprchp:978-3-642-38391-5_105. 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.