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|Title:||A 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.
Artiﬁcial ﬁsh swarm
Artificial fish swarm
|Journal:||Journal of Global Optimization|
|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.|
|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.|
|Appears in Collections:||CAlg - Artigos em revistas internacionais/Papers in international journals|
CMAT - Artigos em revistas com arbitragem / Papers in peer review journals