Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/20886

TitleA hybrid genetic pattern search augmented Lagrangian method for constrained global optimization
Author(s)Costa, L.
Espírito Santo, I. A. C. P.
Fernandes, Edite Manuela da G. P.
KeywordsGlobal Optimization
Augmented Lagrangian
Genetic algorithm
Pattern Search
Issue date15-May-2012
PublisherElsevier
JournalApplied Mathematics and Computation
Abstract(s)Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems. Thus, in order to reduce the total number of function evaluations, local and global techniques may be combined. Moreover, the hybridization may provide a more effective trade-off between exploitation and exploration of the search space. In this study, we propose a new hybrid genetic algorithm based on a local pattern search that relies on an augmented Lagrangian function for constraint-handling. The local search strategy is used to improve the best approximation found by the genetic algorithm. Convergence to an $\varepsilon$-global minimizer is proved. Numerical results and comparisons with other stochastic algorithms using a set of benchmark constrained problems are provided.
TypeArticle
URIhttp://hdl.handle.net/1822/20886
DOI10.1016/j.amc.2012.03.025
ISSN0096-3003
Publisher versionhttp://www.sciencedirect.com/
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
LES/ALG - Artigos em revistas científicas internacionais com arbitragem

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