Augmented Lagrangian combined to evolutionary heuristic for energy efficiency in OCDMA networks

Abstract 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.