Convergence in Evolutionary Programs with Self-Adaptation
Evolutionary Computation
Convergence properties of augmented Lagrangian methods for constrained global optimization
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Unified theory of augmented Lagrangian methods for constrained global optimization
Journal of Global Optimization
Global minimization using an Augmented Lagrangian method with variable lower-level constraints
Mathematical Programming: Series A and B
An augmented Lagrangian fish swarm based method for global optimization
Journal of Computational and Applied Mathematics
Novel fish swarm heuristics for bound constrained global optimization problems
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
Augmented Lagrangian functions for constrained optimization problems
Journal of Global Optimization
Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization
Computational Optimization and Applications
An approach to constrained global optimization based on exact penalty functions
Journal of Global Optimization
A genetic algorithm based augmented Lagrangian method for constrained optimization
Computational Optimization and Applications
A penalty function-based differential evolution algorithm for constrained global optimization
Computational Optimization and Applications
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This paper aims to present a hyperbolic augmented Lagrangian (HAL) framework with guaranteed convergence to an @e-global minimizer of a constrained nonlinear optimization problem. The bound constrained subproblems that emerge at each iteration k of the framework are solved by an improved artificial fish swarm algorithm. Convergence to an @e^k-global minimizer of the HAL function is guaranteed with probability one, where @e^k-@e as k-~. Preliminary numerical experiments show that the proposed paradigm compares favorably with other penalty-type methods.