Optimization by random search with jumps

We give a random optimization (RO) algorithm to optimize a real‐valued function of n real variables. During the optimization process, interpolation points are examined to follow valleys, and jumps to new starting points are executed to avoid numerous iterations in local minima. Convergence with prob...

Full description

Saved in:
Bibliographic Details
Published inInternational journal for numerical methods in engineering Vol. 60; no. 7; pp. 1301 - 1315
Main Authors Li, Chunshien, Priemer, Roland, Cheng, Kuo-Hsiang
Format Journal Article
LanguageEnglish
Published Chichester, UK John Wiley & Sons, Ltd 21.06.2004
Subjects
derivative-free optimization
global minimum
interpolation check
jump
multi-variable function optimization
random optimization
random search
Online AccessGet full text

Cover

More Information
Summary:We give a random optimization (RO) algorithm to optimize a real‐valued function of n real variables. During the optimization process, interpolation points are examined to follow valleys, and jumps to new starting points are executed to avoid numerous iterations in local minima. Convergence with probability one to the global minimum of a function is proved. The proposed RO method is a simple, derivative‐free and computationally moderate algorithm, with excellent performance compared to other RO methods. Seven functions, which are commonly used to test the performance of optimization methods, are used to evaluate the performance of the RO algorithm given here. Copyright © 2004 John Wiley & Sons, Ltd.
Bibliography:ark:/67375/WNG-ZVG5FHHH-5
ArticleID:NME1014
istex:4E9D0EAD2802598568F90F46C726EA8DEEDA8874
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0029-5981
1097-0207
DOI:10.1002/nme.1014