Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/49143

TitleTheoretical and practical convergence of a self-adaptive penalty algorithm for constrained global optimization
Author(s)Costa, M. Fernanda P.
Francisco, Rogério Brochado
Rocha, Ana Maria A. C.
Fernandes, Edite Manuela da G. P.
KeywordsGlobal optimization
Self-adaptive penalty
Firefly algorithm
Issue dateSep-2017
PublisherSpringer
JournalJournal of Optimization Theory and Applications
Abstract(s)This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems.
TypeArticle
URIhttp://hdl.handle.net/1822/49143
DOI10.1007/s10957-016-1042-7
ISSN0022-3239
e-ISSN1573-2878
Publisher versionhttps://link.springer.com/article/10.1007/s10957-016-1042-7
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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