IDEAS home Printed from https://ideas.repec.org/p/sce/scecfa/444.html
   My bibliography  Save this paper

Particle Swarm Optimization in Economics

Author

Listed:
  • Mico Mrkaic

    (University of Maribor)

Abstract

Particle swarm optimization (PSO) is a population based stochastic optimization technique. PSO is similar to optimization with Genetic Algorithms (GA). In PSO, the potential solutions (particles) move through the problem space by following the current optimum particles. Experience shows that PSO is robust accross different families of optimization problems. We use PSO in some typical economic models where the problems of local extremum points are present, for example principal agent problems, and study the performance of PSO. We also compare the performance of PSO to the performance of other stochastic optimization techniques, for example simmulated annealing

Suggested Citation

  • Mico Mrkaic, 2006. "Particle Swarm Optimization in Economics," Computing in Economics and Finance 2006 444, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:444
    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.

    More about this item

    Keywords

    Stochastic optimization; principal agent models;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:sce:scecfa:444. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/sceeeea.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.