An artificial fish swarm algorithm based hyperbolic augmented Lagrangian method
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摘要
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.
论文关键词:Augmented Lagrangian,Hyperbolic penalty,Artificial fish swarm,Stochastic convergence
论文评审过程:Received 13 February 2013, Revised 22 July 2013, Available online 23 August 2013.
论文官网地址:https://doi.org/10.1016/j.cam.2013.08.017