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The continuous increase of traffic in converged networks has generated the need to improve bandwidth distribution. MPLS networks have proven to be effective, but still require optimization. This work is the result of my Master Thesis in Data Networks carried out at the National University of La Plata (Argentina). A taxonomy of heuristic and metaheuristic strategies is presented to distribute traffic efficiently, minimizing costs and complying with capacity and demand constraints. Five bio-inspired algorithms have been developed based on swarming behaviors, such as bird flocks, ant colonies and chiroptera. These algorithms have been applied to test networks of different sizes to evaluate their effectiveness and determine the optimal parameters. The results obtained are promising and offer new perspectives to address traffic engineering challenges in MPLS networks. In summary the work focuses on optimizing traffic distribution in MPLS networks using bio-inspired approaches in highly interconnected environments.