  In this paper we compare the two intelligent route generation system and its
performance capability in graded networks using Artificial Bee Colony (ABC)
algorithm and Genetic Algorithm (GA). Both ABC and GA have found its importance
in optimization technique for determining optimal path while routing operations
in the network. The paper shows how ABC approach has been utilized for
determining the optimal path based on bandwidth availability of the links and
determines better quality paths over GA. Here the nodes participating in the
routing are evaluated for their QoS metric. The nodes which satisfy the minimum
threshold value of the metric are chosen and enabled to participate in routing.
A quadrant is synthesized on the source as the centre and depending on which
quadrant the destination node belongs to, a search for optimal path is
performed. The simulation results show that ABC speeds up local minimum search
convergence by around 60% as compared to GA with respect to traffic intensity,
and opens the possibility for cognitive routing in future intelligent networks.
