Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/30773
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Rocha, Ana Maria A. C. | por |
dc.contributor.author | Costa, M. Fernanda P. | por |
dc.contributor.author | Fernandes, Edite Manuela da G. P. | por |
dc.date.accessioned | 2014-11-06T11:40:30Z | - |
dc.date.available | 2014-11-06T11:40:30Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Rocha, Ana Maria A. C., Costa, M. Fernanda P., and Fernandes, Edite M. G. P. (2014). A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues. Journal of Global Optimization, 1-25. | por |
dc.identifier.issn | 1573-2916 | - |
dc.identifier.uri | https://hdl.handle.net/1822/30773 | - |
dc.description.abstract | This paper presents a filter-based artificial fish swarm algorithm for solving non- convex constrained global optimization problems. Convergence to an ε-global minimizer is guaranteed. At each iteration k, the algorithm requires a (ρ(k),ε(k))-global minimizer of a bound constrained bi-objective subproblem,where as k →∞ ,ρ(k) →0 gives the constraint violation tolerance and ε(k) → ε is the error bound defining the accuracy required for the solution.The subproblems are solved by a population-based heuristic known as artificial fish swarm algorithm. Each subproblem relies on the approximate solution of the previous one, randomly generated new points to explore the search space for a global solution, and the filter methodology to accept non-dominated trial points.Convergence to a (ρ(k),ε(k))-global minimizer with probability one is guaranteed by probability theory. Preliminary numeri- cal experiments show that the algorithm is very competitive when compared with known deterministic and stochastic methods. | por |
dc.description.sponsorship | Fundação para a Ciência e a Tecnologia (FCT) | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.rights | openAccess | por |
dc.subject | Global optimization | por |
dc.subject | Artificial fish swarm | por |
dc.subject | Filter method | por |
dc.subject | Stochastic convergence | por |
dc.subject | Artificial fish swarm | por |
dc.title | A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | http://link.springer.com/ | por |
oaire.citationStartPage | 239 | por |
oaire.citationEndPage | 263 | por |
oaire.citationIssue | 2 | por |
oaire.citationTitle | Journal of Global Optimization | por |
oaire.citationVolume | 60 | por |
dc.identifier.doi | 10.1007/s10898-014-0157-3 | por |
dc.subject.fos | Engenharia e Tecnologia::Outras Engenharias e Tecnologias | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Journal of Global Optimization | por |
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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AMR_JOGO_2014.pdf | 279,59 kB | Adobe PDF | Ver/Abrir |