A Pareto based approach with elitist learning strategy for MPLS/GMPS networks

Modern telecommunication networks are based on diverse applications that highlighted the status of efficient use of network resources and performance optimization. Various methodologies are developed to address multi-objectives optimization within the traffic engineering of MPLS/ GMPLS networks. However, Pareto based approach can be used to achieve the optimization of multiple conflicting objective functions concurrently. We considered two objective functions such as routing and load balancing costs functions. In the paper, we introduce a heuristics algorithm for solving multi-objective multiple constrained optimization (MCOP) in MPLS/ GMPLS networks. The paper proposes the application of a Pareto based particle swarm optimization (PPSO) for such network's type and through a comparative analysis tests its efficiency against another modified version; Pareto based particle swarm optimization with elitist learning strategy (PPSO_ELS). The simulation results showed that the former proposed approach not only solved the MCOP problem but also provide effective solution for exploration problem attached with PPSO algorithm.