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Computer Science
Computer Networks

ROUTING ALGORITHMS

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Abstract

A new agent-based routing algorithm using optimization techniques is implemented in this paper. The different optimization techniques are Ant, Bee, Ant Bee, Ant GA, Ant PSO, GA, PSO, Ant Dijkstra are the combinations used in the packet delivery between the networks. Routing is the process of selecting best paths in a network. The Routing is also used to forward network traffic among networks. Otherwise routing is a process of carrying the data from source to destination in the network. The outputs of these algorithms are determined by the simulation time and throughput. The experiments are implemented with the NS2 software platform, which is based on the basics of C, C++, and TCL Scripting Language. The results of these algorithms show that Ant-PSO is much better than the other algorithms in the packet delivery between the networks.

Figures (10)
colony of honeybees can extend itself over long distances (up to 14 km) and 1n muluple irections simultaneously to exploit eploying its foragers to good fields ectar or pollen that can be collected a large number of food sources. A colony prospers by . In principle, flower patches with plentiful amounts of with less effort should be visited by more bees, whereas atches with less nectar or pollen should receive fewer bees. The foraging process begins in a slony by scout bees being sent to mdomly from one patch to anothe xploration, keeping a percentage of tl 1ose scout bees that found a patch wh combination of some constituents, s search for promising flower patches. Scout bees move r. During the harvesting season, a colony continues its he population as scout bees. When they return to the hive, ich is rated above a certain quality threshold (measured as uch as sugar content) deposit their nectar or pollen and go ) the "dance floor" to perform a dance known as the waggle dance. This dance is essential for olony communication, and contains three pieces of information regarding a flower patch: the
colony of honeybees can extend itself over long distances (up to 14 km) and 1n muluple irections simultaneously to exploit eploying its foragers to good fields ectar or pollen that can be collected a large number of food sources. A colony prospers by . In principle, flower patches with plentiful amounts of with less effort should be visited by more bees, whereas atches with less nectar or pollen should receive fewer bees. The foraging process begins in a slony by scout bees being sent to mdomly from one patch to anothe xploration, keeping a percentage of tl 1ose scout bees that found a patch wh combination of some constituents, s search for promising flower patches. Scout bees move r. During the harvesting season, a colony continues its he population as scout bees. When they return to the hive, ich is rated above a certain quality threshold (measured as uch as sugar content) deposit their nectar or pollen and go ) the "dance floor" to perform a dance known as the waggle dance. This dance is essential for olony communication, and contains three pieces of information regarding a flower patch: the
A Monthly Double-Blind Peer Reviewed Refereed Open Access International e-J ournal - Included in the International Serial Directories
A Monthly Double-Blind Peer Reviewed Refereed Open Access International e-J ournal - Included in the International Serial Directories
John Holland, from the University of Michigan began his work on genetic algorithms at the beginning of the 60s. A first achievement was the publication of Adaptation in Natural and Artificial System’ in 1975. Holland had a double aim: to improve the understanding of natural adaptation process, and to design artificial systems having properties similar to natural systems®. The basic idea is as fo solution, or a better solu ow: the genetic pool of a given population potentially contains the tion, to a given adaptive problem. This solution is not "active" because the genetic combination on which it relies is split between several subjects.Only the association of different genomes can consider that the shorteni lead to the solution. Simplistically speaking, we could by example ng of the paw and the extension of the fingers of our brachiosaurus are controlled by 2 "genes". No subject has such a genome, but during reproduction and crossover, new genetic combination occurs and, finally, a subject can inherit a "good gene" from both
John Holland, from the University of Michigan began his work on genetic algorithms at the beginning of the 60s. A first achievement was the publication of Adaptation in Natural and Artificial System’ in 1975. Holland had a double aim: to improve the understanding of natural adaptation process, and to design artificial systems having properties similar to natural systems®. The basic idea is as fo solution, or a better solu ow: the genetic pool of a given population potentially contains the tion, to a given adaptive problem. This solution is not "active" because the genetic combination on which it relies is split between several subjects.Only the association of different genomes can consider that the shorteni lead to the solution. Simplistically speaking, we could by example ng of the paw and the extension of the fingers of our brachiosaurus are controlled by 2 "genes". No subject has such a genome, but during reproduction and crossover, new genetic combination occurs and, finally, a subject can inherit a "good gene" from both
Fig 5.2 Ant Genetic Routing Algorithm
Fig 5.2 Ant Genetic Routing Algorithm
and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic
and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic
Dijkstra's algorithm, conceived by computer scientist Edsger Dijkstra in 1956.It is a graph swarm irrespective of the fact whether it is better or worse than the worst particle present in the
Dijkstra's algorithm, conceived by computer scientist Edsger Dijkstra in 1956.It is a graph swarm irrespective of the fact whether it is better or worse than the worst particle present in the
Table 1: Packets Dropped Ratio
Table 1: Packets Dropped Ratio

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