This paper proposes combining the augmented Lagrangian method (ALM) with evolutionary heuristic methods, as well as quasi-Newton optimization methods applied to the energy efficiency (EE) maximization in the optical code division multiple access (OCDMA) communication network. The particle swarm optimization (PSO) and a hybridization between the PSO and the gravitational search algorithm (GSA) called PSOGSA have been deployed. The ALM structure replaces the objective function and allows a best fit to the problem, and ultimately provide more information about the solution. Numerical results demonstrate the robustness and low-complexity of hybrid ALM-PSO, while the ALM associated with PSOGSA attains robustness at cost of high-complexity. In turn, the usually ALM combined with Broyden-Fletcher-Goldfarb-Shanno (BFGS) method presents convergence for a restrict scenarios, failing to perform suitably for networks with large numbers of users.

本文提出将增强拉格朗日方法（ALM）与进化启发式方法相结合，以及将拟牛顿优化方法应用于光码分多址（OCDMA）通信网络中的能效（EE）最大化。已经部署了粒子群优化（PSO）以及PSO与称为PSOGSA的重力搜索算法（GSA）之间的混合。ALM结构取代了目标函数，并最适合该问题，并最终提供了有关解决方案的更多信息。数值结果证明了混合ALM-PSO的鲁棒性和低复杂度，而与PSOGSA相关的ALM以高复杂度为代价获得了鲁棒性。反过来，