Abstract
A Novel Fast Multi -objective Evolutionary Algorithm for QoS Multicast Routing in MANET
Highlights
Multicast routing has drawn a lot of attention in recent years, since it enables a source to send messages to multiple destinations concurrently
We divide the multicast routing problem into two segments: one is formed by the multicast group and the core via improved Core Based Tree (CBT) protocol that uses new policy in selecting core showed in Eq (7) and (8); the other is the combination of the source and the core, using the proposed method to find the optimum path from the source to the core
For the specificity of multicast routing problem (MRP) in mobile ad hoc network (MANET), coding methods can be divided into two categories: one is that the individual is represented by a tree 14, whether this method could traverse the whole state space or not needs to be proven despite it can eliminate cycles and invalid paths after genetic operations; the other is path coding, which utilizes the visiting sequence of nodes as the coding principle that conforms to Dejong’s block assumption
Summary
Multicast routing has drawn a lot of attention in recent years, since it enables a source to send messages to multiple destinations concurrently. In 2008, Qu et al propose a set of node-based rate constraints to model the interference relationship among nodes in a wireless ad hoc network and to provide rate constraints for its QoS flows, they demonstrat that, the algorithm can always admit the feasible flows as well as make full use of the bandwidth resource. Yang et al and Ikeda et al focus on creating a robust path to find solution for specified networks; the genetic algorithm is proposed and, respectively, the individuals of the population are represented by trees, algorithm uses the single point crossover and a mutation operation where the “tree junctions” are chosen randomly, the algorithm employs the elitist model where the individual with the highest fitness value in a population is left unchanged in the generation, the simulation results show that the algorithm is reasonably fast on small and medium size networks.
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