GASANT: An ant-inspired least-cost QoS multicast routing approach based on genetic and simulated annealing algorithms
Keywords:Multicast Routing, Quality of Service (QoS), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Simulated Annealing (SA)
Computing least-cost multicast routing tree while satisfying QoS constraints has become a key issue especially by growing communication networks. To solve this problem, a triplex algorithm called GASANT which is based on Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Simulated Annealing (SA) has been proposed in this paper. Through ACO, we have both provided improved initial population to feed GA and reduced search process. Besides, SA has been deployed to refrain GA from getting stuck into local optimum solutions. Simulation results assert that GASANT not only has high speed convergence time, but also generates least-cost multicast routing trees of high QoS.
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