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This paper presents a new approach for solving network routing optimization problems. In particular, the goal is to optimize the traffic in the network structured event-driven systems as well as to provide means for efficient adaptation of the system to changes in the environment-i.e. when some nodes and/or links fail. Many network routing optimization problems belong to the class of NP hard problems, which can only be solved by using some heuristic approach. This paper provides a path to an incoming packet much faster than existing routing algorithms. This is basically an AI concept, which is useful to get from the source to the destination. DFS (depth first search) and BFS (breath first search) searching techniques a routing algorithm. DB routing is based on a general-purpose metaheuristic named ant colony optimization, which is a framework for building ant-inspired algorithms. DB is applied as the routing algorithm in a simulated packet-switched point-to-point network. It is investigated whether DB is able to obtain an increase in speed of transmission when packets are sent between two distinct nodes. To this end, it is investigated how prioritizing different heuristics effect the quality of the routing performed. It is concluded that DB behaves differently depending on the relative priority of positive feedback negative feedback and local heuristics, and that it is possible to adjust the parameters to achieve distribution of traffic over several paths when the network is heavily loaded.