Multicasting Ad-Hoc Network Based on Genetic Algorithm Approach
Throughout this dissertation, a new approach for multicasting in Ad-hoc networks was developed. This approach is based on two schemes; firstly, a sub-optimal spanning tree of key nodes was built, secondly, the genetic algorithm was used to find this spanning tree. Actually, finding the optimal spanning tree of dominating or key nodes causes NP-was very hard, therefore, several heuristic approaches have been introduced in order to find a sub-optimal one. On the other hand, genetic algorithm is designed of individuals each represents distinguishable tree, and could provide means to tackle this problem by searching for a structure of a suitable spanning tree that can be optimized in order to meet the performance indexes related to the multicast problem. Our model was compared with simple flooding, the results showed the ability of our model to reduce broadcast storm problem while simple flooding causes broadcast storm problem with high probability, and reachability factor of our model is very close to the simple flooding. On the other hand, the complexity of our model is not high compared with the minimum spanning tree technique.
Keywords- Multicast, Ad-Hoc Optimized, Neural Network.