Continuous GRASP with a local active-set method for bound-constrained global optimization

Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic—based on the CGRASP and GENCAN methods—for finding approximate solutions for continuous global optimization problems subject to box constraints....

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Bibliographic Details
Published inJournal of global optimization Vol. 48; no. 2; pp. 289 - 310
Main Authors Birgin, Ernesto G., Gozzi, Erico M., Resende, Mauricio G. C., Silva, Ricardo M. A.
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.10.2010
Springer Nature B.V
Subjects
Algorithms
Computer Science
Construction
Heuristic
Mathematics
Mathematics and Statistics
Methods
Operations Research/Decision Theory
Optimization
Optimization techniques
Real Functions
Stochastic models
Studies
GENCAN
Global optimization
CGRASP
Active-set methods
Stochastic methods
Heuristic
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Summary:Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic—based on the CGRASP and GENCAN methods—for finding approximate solutions for continuous global optimization problems subject to box constraints. Experimental results illustrate the relative effectiveness of CGRASP–GENCAN on a set of benchmark multimodal test functions.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-009-9494-z