Since the advent of Telecommunication networks in the early 60’s, routing has become a recurrent problem with evergrowing complexity due to the simultaneous share of resources, stringent Quality of Service (QoS) constraints and unmanageable network scales (size, speed and exchanged data volume) by conventional route finding schemes. This paper considers a particular class of routing problems where the route to be found needs to simultaneously fulfill different requirements in terms of e.g. maximum latency, loss rate or any other cost measure. The manuscript delves into the application of the Coral Reefs Optimization and the Firey Algorithm, two of the latest bio-inspired meta-heuristic techniques reported to outperform other approximative solvers in a wide range of optimization scenarios. Results obtained from Monte Carlo simulations over synthetic network instances will shed light on the comparative performance of these two algorithms, with emphasis on their convergence speed and statistical significance.