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TitleA shifted hyperbolic augmented Lagrangian-based artificial fish two swarm algorithm with guaranteed convergence for constrained global optimization
Author(s)Rocha, Ana Maria A. C.
Costa, M. Fernanda P.
Fernandes, Edite Manuela da G. P.
KeywordsGlobal optimization
Augmented Lagrangian
Shifted hyperbolic penalty
Artificial fish swarm
Nelder–Mead search
Issue date2016
PublisherTaylor & Francis
JournalEngineering Optimization
CitationAna Maria A.C. Rocha, M. Fernanda P. Costa & Edite M.G.P. Fernandes (2016) A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization, Engineering Optimization, 48:12, 2114-2140, DOI: 10.1080/0305215X.2016.1157688
Abstract(s)This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ε-global minimizer is proved. At each iteration k, the algorithm requires the ε(k)-global minimization of a bound constrained optimization subproblem, where ε(k) → ε. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder–Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based 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

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