Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/49143
Título: | Theoretical and practical convergence of a self-adaptive penalty algorithm for constrained global optimization |
Autor(es): | Costa, M. Fernanda P. Francisco, Rogério Brochado Rocha, Ana Maria A. C. Fernandes, Edite Manuela da G. P. |
Palavras-chave: | Global optimization Self-adaptive penalty Firefly algorithm |
Data: | Set-2017 |
Editora: | Springer |
Revista: | Journal of Optimization Theory and Applications |
Resumo(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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/49143 |
DOI: | 10.1007/s10957-016-1042-7 |
ISSN: | 0022-3239 |
e-ISSN: | 1573-2878 |
Versão da editora: | https://link.springer.com/article/10.1007/s10957-016-1042-7 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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AdapFA_CGO_CFRF.pdf | 358,23 kB | Adobe PDF | Ver/Abrir |