A New Algorithm for Box-Constrained Global Optimization

S. Fanelli ()
Additional contact information
S. Fanelli: Università di Roma “Tor Vergata”

Journal of Optimization Theory and Applications, 2011, vol. 149, issue 1, No 9, 175-196

Abstract: Abstract An important class of deterministic methods for global optimization is based on the theory of terminal attractors and repellers. Unfortunately, the utilization of scalar repellers is unsuitable, when the dimension n of the problem assumes values of operational interest. In previous papers the author et al. showed that BFSG-type methods, approximating the Hessian of twice continuously differentiable functions with a structured matrix, are very efficient to compute local minima, particularly in the secant case. On the other hand, the algorithms founded on the classical αBB technique are often ineffective for computational reasons. In order to increase the power of repellers in the tunneling phases, the utilization of repeller matrices with a proper structure is certainly promising and deserves investigation. In this work, it is shown that a BFGS-type method of low complexity, implemented in the local optimizations, can be effectively matched with proper repeller matrices in the tunneling phases. The novel algorithm FBαBB, which can be applied in the frame of the αBB computational scheme, is very efficient in terms of Number of Functions Generations (NFG), Success Rates (SR) in the evaluation of the global minimum and Number of Local Searches (NLS).

Keywords: Global optimization; BFGS methods; Tunneling techniques; αBB method; Box-constrained problems (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-010-9780-4 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:149:y:2011:i:1:d:10.1007_s10957-010-9780-4

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

DOI: 10.1007/s10957-010-9780-4

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 ().