EconPapers    
Economics at your fingertips  
 

A New Filled Function Method with Two Parameters for Global Optimization

Fei Wei, Yuping Wang () and Hongwei Lin
Additional contact information
Fei Wei: Xidian University
Yuping Wang: Xidian University
Hongwei Lin: Xidian University

Journal of Optimization Theory and Applications, 2014, vol. 163, issue 2, No 9, 510-527

Abstract: Abstract The filled function method is an effective approach to find the global minimizer of multi-modal functions. The conventional filled functions are often numerically unstable due to the exponential or logarithmic term and the sensitivity to parameters. In this paper, a new filled function is proposed, which is continuously differentiable, not sensitive to parameters, and not easy to cause overflow. Then a new local search algorithm is given. Based on this, a new filled function method is proposed. The simulations indicate that the proposed method is numerically stable to the variations of the initial points and the parameters. The comparison with some existing algorithms shows that the proposed method is more efficient and effective.

Keywords: Filled function; Global optimization; Uniform design; Local search; 65K05; 90-08 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10957-013-0515-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:163:y:2014:i:2:d:10.1007_s10957-013-0515-1

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-013-0515-1

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:joptap:v:163:y:2014:i:2:d:10.1007_s10957-013-0515-1