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https://hdl.handle.net/1822/42899
Título: | Extensions of firefly algorithm for nonsmooth nonconvex constrained optimization problems |
Autor(es): | Francisco, Rogério Brochado Costa, M. Fernanda P. Rocha, Ana Maria A. C. |
Palavras-chave: | Firefly algorithm Constrained global optimization Stochastic ranking |
Data: | Jul-2016 |
Editora: | Springer International Publishing AG |
Revista: | Lecture Notes in Computer Science |
Citação: | Rogério B. Francisco, M. Fernanda P. Costa, and Ana Maria A.C. Rocha, Extensions of Firefly Algorithm for Nonsmooth Nonconvex Constrained Optimization Problems, O. Gervasi et al. (Eds.): ICCSA 2016, Part I, LNCS 9786, pp. 402--417, 2016. |
Resumo(s): | Firefly Algorithm (FA) is a stochastic population-based algorithm based on the flashing patterns and behavior of fireflies. Original FA was created and successfully applied to solve bound constrained optimization problems. In this paper we present extensions of FA for solving nonsmooth nonconvex constrained global optimization problems. To handle the constraints of the problem, feasibility and dominance rules and a fitness function based on the global competitive ranking, are proposed. To enhance the speed of convergence, the proposed extensions of FA invoke a stochastic local search procedure. Numerical experiments to validate the proposed approaches using a set of well know test problems are presented. The results show that the proposed extensions of FA compares favorably with other stochastic population-based methods. |
Tipo: | Artigo em ata de conferência |
Descrição: | Publicado em: "Computational science and its applications – ICCSA 2016: 16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part I". ISBN 978-3-319-42084-4 |
URI: | https://hdl.handle.net/1822/42899 |
ISBN: | 9783319420844 |
DOI: | 10.1007/978-3-319-42085-1_31 |
ISSN: | 0302-9743 |
Versão da editora: | www.springerlink.com |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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AMR_Rogerio_ICCSA_2016.pdf | 780,44 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons