Optimization Online


Gradient-Controlled, Typical-Distance Clustering for Global Optimization

I.G. TSOULOS (sheridan***at***cs.uoi.gr)
I.E. LAGARIS (lagaris***at***cs.uoi.gr)

Abstract: We present a stochastic global optimization method that employs a clustering technique which is based on a typical distance and a gradient test. The method aims to recover all the local minima inside a rectangular domain. A new stopping rule is used. Comparative results on a set of test functions are reported.

Keywords: Stochastic Global optimization, Multistart, Stopping rules

Category 1: Global Optimization

Category 2: Global Optimization (Stochastic Approaches )

Citation: Preprint, no 4-5/2004 Dept. of Computer Science, University of Ioannina, Greece

Download: [Postscript][PDF]

Entry Submitted: 05/17/2004
Entry Accepted: 05/17/2004
Entry Last Modified: 05/17/2004

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Programming Society