An artificial fish swarm algorithm based hyperbolic augmented Lagrangian method

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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 ϵkϵ as k. Preliminary numerical experiments show that the proposed paradigm compares favorably with other penalty-type methods.

Keywords

Augmented Lagrangian
Hyperbolic penalty
Artificial fish swarm
Stochastic convergence

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