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MulRoGA: A Multicast Routing Genetic Algorithm approach considering multiple objectives
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DOI：10.1007/s10489-008-0148-5.png)
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We solve a multicast routing problem by means of a genetic algorithm (GA) without using multicast trees. The source-destination routes need to fulfill two conflicting objectives: maximization of the common links and minimization of the route sizes. The proposed GA can be characterized by its representation of network links and routes in a variable size multi-chromosome problem; local viability restrictions in order to generate the initial population and define variation operators; selection operators in order to choose the most promising individuals thus preserving diversity, and the fitness function in order to handle the conflicting multiple objectives. The proposed model is called a Multicast Routing Genetic Algorithm (MulRoGA). The model was tested on the 33-node European GA parts per thousand ANT WAN network backbone and three other networks (66-node, 100-node and 200-node) randomly generated using the Waxman model on a network topology generator BRITE. On considering each network, a number of solutions were found for changes in the size and node members of the multicast groups, and the source node. The results of the MulRoGA operation suggest a consistent and robust performance in the various cases including comparisons with the methods of unicast shortest path routing, shortest path tree routing (SPT), and simulated annealing (SA) heuristic.
Keyword:
Multicast routing
Multiobjective optimization
Genetic algorithms
Source-based multicasting
期刊
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3.5
论文数:
7.6K
被引数:
1.7W
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