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We propose in this letter a bio-inspired Quality of Service (QoS) routing algorithm that is based on the trial/error paradigm combined with a continuous adaptive function to optimize three QoS criteria: static cumulative cost path and dynamic end-to-end delay and residual bandwidth. Our proposed approach uses a model that combines a stochastic planned pre-navigation for the exploration phase and a deterministic approach for the backward phase. We adopt a unified framework of online learning to develop a cost function. We evaluated the performance of our QoS-routing algorithm and the simulation results demonstrate substantial performance improvements for networks with dynamically changing traffic.