Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6422768 | Journal of Computational and Applied Mathematics | 2014 | 9 Pages |
Abstract
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 ϵkâϵ as kââ. Preliminary numerical experiments show that the proposed paradigm compares favorably with other penalty-type methods.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
M. Fernanda P. Costa, Ana Maria A.C. Rocha, Edite M.G.P. Fernandes,