This paper aims to present a hyperbolic augmented Lagrangian (HAL) framework with guaranteed convergence to an -global minimizer of a constrained nonlinear optimization problem. The bound constrained subproblems that emerge at each iteration of the framework are solved by an improved artificial fish swarm algorithm. Convergence to an -global minimizer of the HAL function is guaranteed with probability one, where as . Preliminary numerical experiments show that the proposed paradigm compares favorably with other penalty-type methods.