INDEXED IN 

International Journal of Modern Science and Technology, 1(4), 2016, Pages 115-120. 


Effective Optimization of Network Reliability using New Adaptive Genetic Algorithm

​A. Sriram1, P. Deepan2
1 Department of Electronics and Communication Engineering, Arasu Engineering College, Kumbakonam, India.
2 Department of Computer Science and Engineering, Arasu Engineering College, Kumbakonam, India.

​​Abstract
To search the optimal route is a complex task in wireless sensor network because high optimization depends upon a number of parameters. Most of the multimedia applications require the k shortest paths during the communication between a single source and multiple destinations. This problem is known as multimedia multicast routing and has been proved to be NP-complete. In this paper proposes a genetic algorithm to determine the k shortest paths with bandwidth constraints from a single source node to multiple destinations nodes. The algorithm uses the connection matrix of a given network, and the bandwidth of the links to obtain the k shortest paths. The success of Genetic Algorithm depends upon the number of operators such as selection, mutation and crossover. Needless to say crossover is most innovative. The performance of the proposed approach has been compared with k shortest path algorithm and improvement has been observed.

​​Keywords: ​Optimization; Neural Networks; Evolutionary Algorithm; Genetic Algorithms. 

​References

  1. Stalling W. High-Speed Networks TCP/IP and ATM Design Principles, Prentice-Hall, 1998.
  2. Ali MK, Kamoun F. Neural networks for shortest path computation and routing in computer networks. IEEE Trans Neural Networks 4 (1993) 941-954.
  3. Park DC, Choi SE. A neural network basedmulti-destination routing algorithm for communication network. Proc Joint Conf Neural Networks (1998) 1673–1678.
  4. Ahn CW, Ramakrishna RS, Kang CG, Choi IC. Shortest path routing algorithm using Hopfield neural network. Electron Lett 37 (2001) 1176-1178.
  5. Munemoto M, Takai Y, Sato Y. A migration scheme for the genetic adaptive routing algorithm. Proc IEEE Int Conf Systems Man and Cybernetics (1998) 2774-2779.
  6. Mahdi SA, Mustafa M, Abu Ali AN. Hybrid dynamic routing protocol for finding an optimal path to routers. International Journal of Academic Research 3 (2011) 992-1000.
  7. Inagaki J, Haseyama M, Kitajima H. A genetic algorithm for determining multiple routes and its applications. Proc IEEE Int Symp Circuits and Systems (1999) 137-140.
  8. Leung Y, Li G, Xu ZB. A genetic algorithm for the multiple destination routing problems. IEEE Trans Evol Compute 2 (1998) 150-161.
  9. Xiawei Z, Changjia C, Gang Z. A genetic algorithm for multi casting routing problem. Proc Int Conf Communication Technology (2000) 1248-1253.
  10. Zakir AH. Genetic algorithm for the travelling salesman problem using sequential constructive crossover operator. International Journal of Biometrics and Bioinformatics. 3 (2010) 96-105.
  11. Kumar P. Cardinality based approach for reliability redundancy optimization of flow networks. Reliability Theory and Applications 7 (2012) 63-71.
  12. Holland J. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.
  13. Zhang Q, Leung YW. An orthogonal genetic algorithm for multimedia multicast routing. IEEE Trans Evol Compute 3 (1999) 53-62.
  14. Pan H, Wang IY. The bandwidth allocation of ATM through genetic algorithm. Proc IEEE Globe Com (1991) 125-129.
  15. Mustafa ME, Eid SMA. A genetic algorithm for joint optimization of capacity and flow assignment in packet switched networks. Proc 17th National Radio Science Conf (2000) C5.1-C5.6.

ISSN 2456-0235

International Journal of Modern Science and Technology