An artificial fish swarm algorithm based hyperbolic augmented Lagrangian method

This paper aims to present a hyperbolic augmented Lagrangian (HAL) framework with guaranteed convergence to an ϵ-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 f...

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Bibliographic Details
Main Author: Costa, M. Fernanda P. (author)
Other Authors: Rocha, Ana Maria A. C. (author), Fernandes, Edite Manuela da G. P. (author)
Format: article
Language:eng
Published: 2014
Subjects:
Augmented Lagrangian
Hyperbolic penalty
Artificial fish swarm
Stochastic convergence
Science & Technology
Online Access:http://hdl.handle.net/1822/26658
Country:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/26658
Description
Summary:This paper aims to present a hyperbolic augmented Lagrangian (HAL) framework with guaranteed convergence to an ϵ-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 ϵk-global minimizer of the HAL function is guaranteed with probability one, where ϵk→ϵ as k→∞. Preliminary numerical experiments show that the proposed paradigm compares favorably with other penalty-type methods.

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