IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v1y2010i3p67-77.html
   My bibliography  Save this article

Unit Commitment by Evolving Ant Colony Optimization

Author

Listed:
  • K. Vaisakh

    (Andhra University, India)

  • L. R. Srinivas

    (S.R.K.R. Engineering College, India)

Abstract

Ant Colony Optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem, and multistage decision making of ACO has an edge over other conventional methods. In this paper, the authors propose the Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, start up cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on the systems with number of generating units in the range of 10 to 60. The test results are encouraging and compared with those obtained by other methods.

Suggested Citation

  • K. Vaisakh & L. R. Srinivas, 2010. "Unit Commitment by Evolving Ant Colony Optimization," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(3), pages 67-77, July.
    • Handle: RePEc:igg:jsir00:v:1:y:2010:i:3:p:67-77
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jsir.2010070105
    Download Restriction: no
    ---><---

    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:igg:jsir00:v:1:y:2010:i:3:p:67-77. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.