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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.This work was supported by COMPETE [POCI-01-0145-FEDER-007043]; FCT-Fundacao para a Ciencia e Tecnologia within the Project Scope [UID/CEC/00319/2013]; and partially supported by CMAT-Centre of Mathematics of the University of Minho
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