An Efficient Approach towards Network Routing using Genetic Algorithm

Authors

  • Alaa Obeidat The Hashemite University, Zarqa, Jordan
  • Mohammed Al-shalabi World Islamic Sciences and Education University, World Islamic Sciences and Education University Amman , Jordan

DOI:

https://doi.org/10.15837/ijccc.2022.5.4815

Keywords:

RGA, Genetic Algorithm, Shortest Path, Network Field

Abstract

The network field has been very popular in recent times and has aroused much of the attention of researchers. The network must keep working with the varying infrastructure and must adapt to rapid topology changes. Graphical representation of the networks with a series of edges varying over time can help in analysis and study. This paper presents a novel adaptive and dynamic network routing algorithm based on a Regenerate Genetic Algorithm (RGA) with the analysis of network delays. With the help of RGA at least a very good path, if not the shortest one, can be found starting from the origin and leading to a destination. Many algorithms are devised to solve the shortest path (SP) problem for example Dijkstra algorithm which can solve polynomial SP problems. These are equally effective in wired as well as wireless networks with fixed infrastructure. But the same algorithms offer exponential computational complexity in dealing with the real-time communication for rapidly changing network topologies. The proposed genetic algorithm (GA) provides more efficient and dynamic solutions despite changes in network topology, network change, link or node deletion from the network, and the network volume (with numerous routes).

References

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and Cayirci. Wireless sensor networks: A survey. Computer Networks, 38(4):393-422.

https://doi.org/10.1016/S1389-1286(01)00302-4

D. Estrin, D. Culler, K. Pister, and G. Sukhatme. Connecting the physical world with pervasive networks. IEEE Pervasive Computing, pages 59 - 69.

https://doi.org/10.1109/MPRV.2002.993145

Vasilakos, A. Saltouros, M.P. Atlassis, A.F. Pedrycz, W. Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques, Systems, Man and Cybernetics,Part C, IEEE Transactions on, Volume 33, Issue 3, Page(s):297 - 312.

https://doi.org/10.1109/TSMCC.2003.817354

T. Cormen. Introduction to Algorithms, MIT Press, Cambridge MA.

S. Dulman, T. Nieberg, J. Wu, and P. Havinga. Trade-off between traffic overhead and reliability in multipath routing for wireless sensor networks.In Proceedings of the Wireless Communications and Networking Conference.

W. Stalling, High-Speed Networks: TCP/IP and ATM Design Principles. Englewood Cliffs, NJ: Prentice-Hall.

M. K. Ali and F. Kamoun, "Neural networks for shortest path computation and routing in computer networks," IEEE Trans. Neural Networks, vol. 4, pp. 941-954.

https://doi.org/10.1109/72.286889

Shami, S.H., Kirkwood, I.M.A., and Sinclair, M.C. Evolving Simple fault-tolerant routing rules using geneticprogramming, electronics Letters, 33(17):1440-1441.

https://doi.org/10.1049/el:19970996

Li J, Li Y, Pardalos PM (2016) Multi-depot vehicle routing problem with time windows under shared depot resources. J Comb Optim 31(2):515-532

https://doi.org/10.1007/s10878-014-9767-4

R. K. Jha, P. Kharg. A Comparative Performance Analysis of Routing Protocols in MANET using NS3 Simulator. I. J. Computer Network and Information Security, 2015, 4, 62-68

https://doi.org/10.5815/ijcnis.2015.04.08

C. Avin, M. Koucký, and Z. Lotker Z. How to Explore a Fast-Changing World (Cover Time of a Simple Random Walk on Evolving Graphs). In: Aceto L., Damgård I., Goldberg L.A., Halldórsson M.M., Ingólfsdóttir A., Walukiewicz I. (eds) Automata, Languages and Programming. ICALP 2008. Lecture Notes in Computer Science, vol 5125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70575-8_11

https://doi.org/10.1007/978-3-540-70575-8_11

V. N. Wijayaningrum, W. F. Mahmudy. Optimization of Ship's Route Scheduling Using Genetic Algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 2016, 2(1): 180- 186

https://doi.org/10.11591/ijeecs.v2.i1.pp180-186

P. Kora, P. Yadlapalli. Crossover Operators in Genetic Algorithms: A Review. International Journal of Computer Applications (0975 - 8887), 2017, 162(10)

https://doi.org/10.5120/ijca2017913370

P. Kora, S. R. Krishna. Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block. SpringerPlus, Springer, 2015, 4(1)

https://doi.org/10.1186/s40064-015-1240-z

Z. Qiongbing, D. Lixin. A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems. Expert System with Applications, 2016, 60 (30): 183-189

https://doi.org/10.1016/j.eswa.2016.04.005

P. Dwivedi, V. Kant, and K. K. Bharadwaj. Learning path recommendation based on modified variable length genetic algorithm. Educ Inf Technol, 2018 23, 819-836

https://doi.org/10.1007/s10639-017-9637-7

C. K. H. Lee, K.L. Choy, G. T. S. Ho, and C. H. Y. Lam. A slippery genetic algorithm-based process mining system for achieving better quality assurance in the garment industry. Expert Systems with Applications, 2016, 46:236-248

https://doi.org/10.1016/j.eswa.2015.10.035

N. Gupta, N. Patel, B. N. Tiwari, and M. Khosravy. Genetic Algorithm Based on Enhanced Selection and Log-Scaled Mutation Technique. Proceedings of the Future Technologies Conference (FTC) 2018, 730-748

https://doi.org/10.1007/978-3-030-02686-8_55

S. N. Pawar, R. S. Bichkar. Genetic algorithm with variable length chromosomes for network intrusion detection. International Journal of Automation and Computing, 2015, 12: 337-342

https://doi.org/10.1007/s11633-014-0870-x

M. Moza, S. Kumar. Improving the Performance of Routing Protocol using Genetic Algorithm. International Journal of Computer Network and Information Security(IJCNIS), 2016, 8 (7): 10- 16

https://doi.org/10.5815/ijcnis.2016.07.02

Z. H. Ahmad. Performance Analysis of Hybrid Genetic Algorithms for the Generalized Assignment Problem. IJCSNS International Journal of Computer Science and Network Security, 2019, 19(9).

A. B. A. Hassanat, E. Alkafaween. On enhancing genetic algorithms using new crossovers. International Journal of Computer Applications in Technology, 2017, 55(3)

https://doi.org/10.1504/IJCAT.2017.084774

C. Lamini, S. Benhlima, and A. Elbekri. Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning. The First International Conference On Intelligent Computing in Data Sciences. Procedia Computer Science 127, 2018, 180-189

https://doi.org/10.1016/j.procs.2018.01.113

S. Anand, N. Afreen, and S. Yazdani. A Novel and Efficient Selection Method in Genetic Algorithm. International Journal of Computer Applications, 2015, 129 (15): 7-12

https://doi.org/10.5120/ijca2015907067

Additional Files

Published

2022-09-29

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.