Please use this identifier to cite or link to this item:

TitleA filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues
Author(s)Rocha, Ana Maria A. C.
Costa, M. Fernanda P.
Fernandes, Edite Manuela da G. P.
KeywordsGlobal optimization
Artificial fish swarm
Filter method
Stochastic convergence
Artificial fish swarm
Issue date2014
JournalJournal of Global Optimization
CitationRocha, 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.
Abstract(s)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.
Publisher version
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

Files in This Item:
File Description SizeFormat 
AMR_JOGO_2014.pdf279,59 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID