Optimal Shortest Path in Mobile Ad-Hoc Network Based on Fruit Fly Optimization Algorithm

  • Saad M. Darwish
  • Amr Elmasry
  • Shaymaa H. IbrahimEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Mobile ad hoc network (MANET) can be defined as a self-configuring group of mobile devices or nodes. These nodes able to change locations and configure themselves rapidly, freely and dynamically, thus routing is big challenging in mobile ad hoc network. The important role of routing is selecting the route for transferring the data from the source node to destination node in network efficiently based on the routing table, which contains routing metrics such as cost, load, distance, and delay. To solve this problem used many shortest path search techniques. Based on network performance, the routing algorithms find the best path to take. In this paper proposed the fruit fly optimization algorithm (FOA) to find the optimal shortest route in a mobile ad hoc network. The simulation results show that the performance of fruit fly optimization algorithm is better than the classical algorithm (Dijkstra Algorithm) in terms of scalability and complexity time.


Mobile Ad-hoc networks (MANET) Routing protocol Shortest route Fruit fly optimization algorithm (FOA) 


  1. 1.
    Raza, N., Aftab, M., Akbar, M., Ashraf, O., Irfan, M.: Mobile ad-hoc networks applications and its challenges. Commun. Netw. 8(3), 131–136 (2016)Google Scholar
  2. 2.
    Ghosekar, P., Katkar, G., Ghorpade, P.: Mobile ad-hoc networking: imperatives and challenges. Int. J. Comput. Appl. Spec. Issue MANETs 3, 153–158 (2010)Google Scholar
  3. 3.
    Jayakumar, G., Gopinath, G.: Ad-hoc mobile wireless networks routing protocols – a review. J. Comput. Sci. 3(8), 574–582 (2007)Google Scholar
  4. 4.
    Sharma, A., Gupta, A.: A survey on path weight-based routing over wireless mesh networks. Int. J. Innovations Eng. Technol. 3(4), 15–20 (2014)Google Scholar
  5. 5.
    Devarajan, K., Padmathilagam, V.: Diversified optimization techniques for routing protocols in mobile ad-hoc wireless networks. J. Eng. Appl. Sci. 10(12), 5229–5239 (2015)Google Scholar
  6. 6.
    Kushwaha, S., Kumar, A., Kumar, N.: Routing protocols and challenges faced in ad-hoc wireless networks. Adv. Electron. Electric Eng. 4(2), 207–212 (2014)Google Scholar
  7. 7.
    Manjunath, M., Manjaiah, D.: PAR: petal ant routing algorithm for mobile ad hoc network. Int. J. Comput. Netw. Commun. 7(2), 45–58 (2015)Google Scholar
  8. 8.
    Sumitha, J.: Routing algorithms in networks. Res. J. Recent Sci. 3(ISC-2013), 1–3 (2014)Google Scholar
  9. 9.
    Pettie, S., Ramachandran, V., Sridhar, S.: Experimental evaluation of a new shortest path algorithm. In: LNCS, vol. 2409, pp. 126–142 (2002)Google Scholar
  10. 10.
    AbuRyash, H., Tamimi, A.: Comparison studies for different shortest path algorithms. Int. J. Comput. Appl. 14(8), 5879–5986 (2015)Google Scholar
  11. 11.
    Roy, D., Das, S., Ghosh, S.: Comparative analysis of genetic algorithm and classical algorithms in fractional programming. In: Advanced Computing and Systems for Security, vol. 396, pp. 249–270 (2015)Google Scholar
  12. 12.
    Vasiljevic, D.: Classical and Evolutionary Algorithms in the Optimization of Optical Systems, 1st edn. Kluwer Academic Publishers, London (2002)zbMATHGoogle Scholar
  13. 13.
    Pan, W.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl.-Based Syst. 26(2), 69–74 (2012)Google Scholar
  14. 14.
    Allah, R.: Hybridization of fruit fly optimization algorithm and firefly algorithm for solving nonlinear programming problems. Int. J. Swarm Intell. Evol. Comput. 5(2), 1–10 (2016)Google Scholar
  15. 15.
    Kaur, S., Sawhney, R., Vohra, R.: MANET link performance parameters using ant colony optimization approach. Int. J. Comput. Appl. 47(8), 40–45 (2012)Google Scholar
  16. 16.
    Kumari, E., Kannammal, A.: Dynamic shortest path routing in mobile ad-hoc networks using modified artificial bee colony optimization algorithm. Int. J. Comput. Sci. Inf. Technol. 5(6), 7423–7426 (2014)Google Scholar
  17. 17.
    Jang, K.: A tabu search algorithm for routing optimization in mobile ad-hoc networks. Telecommun. Syst. 51(2–3), 177–191 (2012)Google Scholar
  18. 18.
    Zakaria, A., Saman, M., Nor, A., Hassan, H.: Finding shortest routing solution in mobile ad hoc networks using firefly algorithm and queuing network analysis. J. Technol. 77(18), 17–22 (2015)Google Scholar
  19. 19.
    Persis, D., Robert, T.: Reliable mobile ad-hoc network routing using firefly algorithm. Intell. Syst. Appl. 8(5), 10–18 (2016)Google Scholar
  20. 20.
    Biradar, A., Thool, R.: Effectiveness of genetic algorithm in reactive protocols for MANET. Int. J. Eng. Res. Technol. 2(7), 1757–1761 (2013)Google Scholar
  21. 21.
    Shan, D., Cao, G., Dong, H.: LGMS-FOA: An improved fruit fly optimization algorithm for solving optimization problems. Hindawi Publishing Corporation Math. Probl. Eng. 2013, 1–10 (2013)zbMATHGoogle Scholar
  22. 22.
    Jiang, T., Wang, J.: Study on path planning method for mobile robot based on fruit fly optimization algorithm. Appl. Mech. Mater. 536–537, 970–973 (2014)Google Scholar
  23. 23.
    Iscan, H., Gunduz, M.: Parameter analysis on fruit fly optimization algorithm. J. Comput. Commun. 2(4), 137–141 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Saad M. Darwish
    • 1
  • Amr Elmasry
    • 2
  • Shaymaa H. Ibrahim
    • 1
    Email author
  1. 1.Department of Information Technology, Institute of Graduate Studies and ResearchAlexandria UniversityAlexandriaEgypt
  2. 2.Department of Computer Engineering and Systems, Faculty of EngineeringAlexandria UniversityAlexandriaEgypt

Personalised recommendations