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A Novel Optimization AHBeeP Algorithm for Routing in MANET

  • A. V. Zade
  • R. M. Tugnayat
  • G. B. Regulwar
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  • 26 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 103)

Abstract

The world around us is becoming increasingly complex every day and changes dynamically. The problems that we face require adaptive and scalable systems that can offer solutions with ever-rising level of autonomy. Traditional approaches are becoming obsolete because they were designed for a simpler world. Therefore, any advancement in understanding and solving complex problems can have an impact on the entire set of disciplines in engineering, biology, sociology, etc. In this paper the ant colony optimization (ACO), genetic algorithm is evaluated and compares their performance with the novel proposed adaptive honey bee protocol (AHBeeP). The algorithms, stimulated by the supportive behavior of nature in colonies of animals and social insects, were initially applied to solve the traditional optimization problems. In today’s scenario, the main challenge is to transfer the packets of data from source system to destination system. In the proposed approach, the optimization is used for transferring the data packets based on the honey bees intelligence to communicate each other in the form of dancing language that can be useful for finding the shortest route in the wireless networks and also in optimized way of pathfinding.

Keywords

Swarm intelligence ACO AHBeeP Waggle dance 

References

  1. 1.
    Giagkos A, Wilson MS (2014) BeeIP—a Swarm intelligence based routing for wireless ad hoc networks. Inf Sci 265:23–35 (Elsevier)CrossRefGoogle Scholar
  2. 2.
    Zade AV, Tugnayat RM (2014) Ant Colony Optimization (ACO) in disaster information network. Int J Innov Eng Res Technol (IJIERT) 1(2). ISSN: 2394-3696Google Scholar
  3. 3.
    Zade AV, Tugnayat RM (2015) A honey bee swarm intelligence algorithm for communication networks. Int J Eng Sci Res Technol (IJESRT) 4(1):644–647. ISSN: 2277-9655Google Scholar
  4. 4.
    Kirby J, de Oca MAM, Senger S, Rossi LF, Shen C-C (2013) Tracking time-dependent scalar fields with swarms of mobile sensors. In: IEEE 7th international conference on self-adaptive and self-organizing systems. IEEE Computer Society, June 2013. ISSN: 978-0-7695-5129Google Scholar
  5. 5.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-Tr06, Oct 2005Google Scholar
  6. 6.
    Iliea S, Badica C (2013) Multi-agent distributed framework for swarm intelligence. In: Proceedings of international conference on computational science, ICCS 2013, vol 18. Elsevier, pp 611–620 1877-0509Google Scholar
  7. 7.
    Kiatwuthiamorn J, Thammano A (2013) A novel optimization algorithm based on the natural behavior of the ant colonies. Procedia Comput Sci 20:90–95, 1877-0509 (Elsevier)CrossRefGoogle Scholar
  8. 8.
    Momen S (2013) Ant-inspired decentralized task allocation strategy in groups of mobile agents. Procedia Comput Sci 20:169–176, 1877-0509 (Elsevier)CrossRefGoogle Scholar
  9. 9.
    Gunes M, Sorges U, Bouazizi I (2002) ARA-the ant colony based routing algorithm for MANET’s. In: Proceedings of the international conference on parallel processing workshops (ICPPW’02), 1530-2016/02. IEEE Computer SocietyGoogle Scholar
  10. 10.
    Benavidez P, Nagothu K, Ray AK, Shaneyfelt T (2008) Multi-domain robotic swarm communication system. In: SoSE ‘08 Proceedings of IEEE international conference on system of systems engineering, Singapore, June 2008, pp 1–6. ISBN: 978-1-4244-2172-5Google Scholar
  11. 11.
    Baras JS, Mehta H (2003) A probabilistic emergent routing algorithm for mobile ad hoc networks. In: Proceedings of WiOpt ’03: modeling and optimization in mobile, adhoc and wireless networks, Sophia-Antipolis, France, 3–5 Mar 2003Google Scholar
  12. 12.
    Yuce B, Packianather MS, Mastrocinque E, Pham DT, Lambiase A (2013) Honey bees inspired optimization method: the bees algorithm. Insects J 4:646–662. ISSN 2075-4450CrossRefGoogle Scholar
  13. 13.
    Friedman R, Shulman AK (2013) A density-driven publish subscribe service for mobile adhoc networks. J Ad Hoc Netw 11(1):522–540CrossRefGoogle Scholar
  14. 14.
    von Frisch Karl (1967) The dance language and orientation of bees. The Belknap Press of Harvard University Press, Cambridge, MAGoogle Scholar
  15. 15.
    Nezami OM, Bahrampour A, Jamshidlou P (2013) Dynamic Diversity Enhancement in Particle Swarm Optimization (DDEPSO) algorithm for preventing from premature convergence. Procedia Comput Sci 24:54–65. In: 17th Asia Pacific symposium on intelligent and evolutionary systems, IES 2013. Elsevier, 1877-0509Google Scholar
  16. 16.
    Biradar A, Thool R (2014) Reliable genetic algorithm based intelligent routing for MANET. In: World congress on computer applications and information systems (WCCAIS). IEEE Xplore, 17–19 Jan 2014. ISBN: 978-1-4799-3351-8Google Scholar
  17. 17.
    Fahmy IMA, Nassef L, Hefny HA (2012) Predicted Energy-Efficient Bee-inspired Routing (PEEBR) path selection optimization. In: 2012, Proceedings of IEEE on 8th international conference informatics and systems (INFOS). ISBN: 978-1-4673-0828-1, 14–16 May 2012Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • A. V. Zade
    • 1
  • R. M. Tugnayat
    • 2
  • G. B. Regulwar
    • 2
  1. 1.Research Scholar, SGBA, UniversityAmravatiIndia
  2. 2.Principal, Shankarprasad Agnihotri College of EngineeringWardhaIndia

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