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 @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.

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