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Genetic Algorithm for Shortest Path in Ad Hoc Networks

  • Hala KhankhourEmail author
  • Jâafar AbouchabakaEmail author
  • Otman AbdounEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 92)

Abstract

The decentralized nature of ad hoc wireless networks makes them suitable for a variety of applications, where the central nodes cannot be invoked and can improve the scalability of large map networks, the topology of the ad hoc network may change rapidly and unexpectedly. Mobile Ad hoc (VANET) are used for communication between vehicles that helps vehicles to behave intelligently during vehicle collisions, accidents…one of the most problems confronted in this network, is finding the shortest path (SP) from the source to the destination of course within a short time. In this paper Genetic Algorithm is an excellent approach to solving complex problem in optimization with difficult constraints and network topologies, the developed genetic algorithm is compared with another algorithm which contains a topology database for evaluate the quality of our solution and between Dijkstra’s algorithm. The results simulation affirmed the potential of the proposed genetic algorithm.

Keywords

Genetic algorithm Population size Shortest path Ad hoc Optimal routing VANET 

References

  1. 1.
    Giri, A.K., Lobiyala, D.K., Katti, C.P.: Optimization of value of parameters in Ad-hoc on demand multipath distance vector routing using teaching-learning based optimization. In: 3rd International Conference on Recent Trends in Computing (ICRTC 2015), Elsevier (2015)Google Scholar
  2. 2.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, USA (1975)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, USA (1989)zbMATHGoogle Scholar
  4. 4.
    Mardle, S., Pascoe, S.: An overview of genetic algorithms for the solution of optimization problems. Comput. High. Educ. Econ. 13(1), 16–20 (1999)Google Scholar
  5. 5.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996).  https://doi.org/10.1007/978-3-662-03315-9CrossRefzbMATHGoogle Scholar
  6. 6.
    Lakshmanaprabu, S.K., Shankar, K., Rani, S.S., Abdulhay, E., Arunkumar, N., Ramirez, G., Uthayakumar, J., et al.: An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: towards smart cities. J. Cleaner Prod. 217, 584–593 (2019)CrossRefGoogle Scholar
  7. 7.
    Bello-Salau, H., Aibinu, A.M., Wang, Z., Onumanyi, A.J., Onwuka, E.N., Dukiya, J.J.: An optimized routing algorithm for vehicle ad-hoc networks. Eng. Sci. Technol. Int. J. (2019) Google Scholar
  8. 8.
    Harrabia, S., Jaffar, I.B., Ghedira, K.: Novel optimized routing scheme for vanets. Procedia Comput. Sci. 98, 32–39 (2016)CrossRefGoogle Scholar
  9. 9.
    Lerman, I., Ngouenet, F.: Algorithmes génétiques séquentiels et parallèles pour une représentation affine des proximités, Rapport de Recherche de l’INRIA Rennes - Projet REPCO 2570, INRIA (1995)Google Scholar
  10. 10.
    Ahn, C.W., Ramakrishna, R.S.: A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans. Evol. Comput. 6(6), 566–576 (2002)CrossRefGoogle Scholar
  11. 11.
    Ali, K., Badreddine, S.: Algorithme génétique Université des sciences et de la technologie Houari BoumedieneGoogle Scholar
  12. 12.
    Stalling, W.: High-Speed Networks: TCP/IP and ATM Design Principles. Prentice-Hall, Englewood Cliffs (1998)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Science, Department of Computer ScienceIBN Tofail UniversityKénitraMorocco
  2. 2.Department of Computer Science Polydisciplinary FacultyAbdelmalek Essaadi UniversityLaracheMorocco

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