• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Editorial Staff
    • Publication Ethics
    • Indexing and Abstracting
    • Related Links
    • FAQ
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter Telegram
Journal of Computer & Robotics
Articles in Press
Current Issue
Journal Archive
Volume Volume 11 (2018)
Volume Volume 10 (2017)
Volume Volume 9 (2016)
Volume Volume 8 (2015)
Volume Volume 7 (2014)
Volume Volume 6 (2013)
Volume Volume 5 (2012)
Issue Issue 2
Issue Issue 1
Volume Volume 4 (2011)
Volume Volume 3 (2010)
Volume Volume 1 (2008)
Jalaeian-F, M. (2012). Augmented Downhill Simplex a Modified Heuristic Optimization Method. Journal of Computer & Robotics, 5(2), 1-6.
Mohsen Jalaeian-F. "Augmented Downhill Simplex a Modified Heuristic Optimization Method". Journal of Computer & Robotics, 5, 2, 2012, 1-6.
Jalaeian-F, M. (2012). 'Augmented Downhill Simplex a Modified Heuristic Optimization Method', Journal of Computer & Robotics, 5(2), pp. 1-6.
Jalaeian-F, M. Augmented Downhill Simplex a Modified Heuristic Optimization Method. Journal of Computer & Robotics, 2012; 5(2): 1-6.

Augmented Downhill Simplex a Modified Heuristic Optimization Method

Article 1, Volume 5, Issue 2, Summer and Autumn 2012, Page 1-6  XML PDF (266 K)
Author
Mohsen Jalaeian-F*
Department of Electrical Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
Abstract
Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm; so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, random search is considered as a global exploration operator in combination with DSM as a local exploitation method. Thus, presented algorithm is a derivative-free, fast, simple and nonlinear optimization method that is easy to be implemented numerically. Efficiency and reliability of the presented algorithm are compared with several other optimization methods, namely traditional downhill simplex, random search and steepest descent. Simulations verify the merits of the proposed method.
Keywords
Augmented Downhill Simplex Method (ADSM); Downhill Simplex; global optimization; global exploration
Statistics
Article View: 1,274
PDF Download: 721
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

Creative Commons
Journal Management System. Designed by sinaweb.