Multiobjective Genetic Algorithm for Multicast Routing

This paper presents a multiobjective genetic algorithm to solve the multicast routing problem without using multicast trees. The mechanism to find routes aims to fulfill two conflicting objectives: maximization of the common links in source-destination routes and minimization of the route sizes. The proposed GA can be characterized by representation of network links in a permutation problem, local viability restrictions to generate the initial population with a significant number of feasible routes, variation operators with viability constraints, selection operators to select the most promising and preserve diversity, and fitness function to deal with the conflicting objectives. The model was tested in three networks: the 33-nodes European GEANT WAN network backbone and two networks (66-node and 100-node) randomly generated using the Waxman model at BRITE network topology generator. The multicast results suggest promising performance compared with the unicast shortest path routing.