Skip to main content
Log in

Fuzzy Rule Selection Using Hybrid Artificial Bee Colony with 2-Opt Algorithm for MANET

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The Mobile Ad-hoc Networks (MANET) is an independent and self-governing hosts of wireless communication that communicate using wireless links thus forming a dynamic and temporary network without any centralized infrastructure. The MANET nodes will not be stationary and the sender and the receiver may not always take similar paths of routing. This way routing becomes quite complicated. A technique that has emerged recently is known as the Opportunistic Routing (OR) which chooses one set of candidates for the purpose of forwarding packets (being compared to that of conventional forwarding made to an approach with one node). It also takes into consideration the nature of the broadcast. This work proposes fuzzy logic with hybrid optimization approach for optimal route selection in MANET applications. The proposed hybrid optimization is based on 2-Opt algorithm and the Artificial Bee Colony (ABC). A fuzzy rule system depends on the end-to-end delay at a node time tends to leave the network there are several packets that are dropped and many different route requests that are generated. The results of the simulation demonstrated the proposed fuzzy rule selection and its efficiency by using the ABC-2 Opt algorithm on being compared with the selection of rule by using the ABC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Jadhav AR, Kale ND (2014) Improved cooperative opportunistic routing protocol in MANET using channel reuse method based on ant Colony optimization approach. International Journal of Information & Computation Technology 4(15):1491–1497

    Google Scholar 

  2. Lakshmi KD, Ramesh R (2014) Energy efficient position based opportunistic routing protocol for MANETS. International Journal of Computer Science and Network Security (IJCSNS) 14(6):31

    Google Scholar 

  3. Sharma V, Alam B, Doja MN (2017) Comparative study of fuzzy logic mobility based FLM-AODV routing protocol and AODV in MANETs. Indonesian Journal of Electrical Engineering and Computer Science 7(1):158–163

    Article  Google Scholar 

  4. Ahmadi M, Shojafar M, Khademzadeh A, Badie K, Tavoli R (2015) A hybrid algorithm for preserving energy and delay routing in mobile ad-hoc networks. Wirel Pers Commun 85(4):2485–2505

    Article  Google Scholar 

  5. Fister I Jr, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186

    MATH  Google Scholar 

  6. Smys S, Bala GJ, Raj JS (2010) Self-organizing hierarchical structure for wireless networks. In: 2010 international conference on advances in computer engineering. IEEE, pp 268–270

  7. Smys S, Bala GJ, Raj JS (2009) Construction of virtual backbone to support mobility in MANET—A less overhead approach. In: 2009 international conference on application of information and communication technologies. IEEE, pp 1–4

  8. Rajalakshmi S, Maguteeswaran R (2015) Quality of service routing in Manet using a hybrid intelligent algorithm inspired by cuckoo search. Sci World J 2015:1–8

    Article  Google Scholar 

  9. Rajan C, Shanthi N (2015) Genetic based optimization for multicast routing algorithm for MANET. Sadhana 40(8):2341–2352

    Article  MathSciNet  Google Scholar 

  10. Chaudhry R, Tapaswi S, Kumar N (2018) Forwarding zone enabled PSO routing with network lifetime maximization in MANET. Appl Intell:1–28

  11. Jayavenkatesan R, Mariappan A (2017) Energy efficient multipath routing for MANET based on hybrid ACO-FDRPSO. Int J Pure Appl Math 115(6):185–191

    Google Scholar 

  12. Kaur I, Rao ALN (2018) SO-FPSO-KM: self-organized fuzzy particle swarm optimization based key management algorithm in MANET. Journal of Advanced Research in Dynamic and Control Systems (JARDCS) 03:26–39

    Google Scholar 

  13. Bagis A, Konar M (2016) Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling. Trans Inst Meas Control 38(5):579–592

    Article  Google Scholar 

  14. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  15. Mahmood MS (2016) Hybrid fuzzy logic and artificial bee colony algorithm for intrusion detection and classification. Iraqi Journal of Science 57(1A):241–252

    Google Scholar 

  16. Caraveo C, Valdez F, Castillo O (2016) Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Appl Soft Comput 43:131–142

    Article  Google Scholar 

  17. Kocer HE, Akca MR (2014) An improved artificial bee colony algorithm with local search for traveling salesman problem. Cybern Syst 45(8):635–649

    Article  Google Scholar 

  18. Ashuri B, Tavakolan M (2011) Fuzzy enabled hybrid genetic algorithm–particle swarm optimization approach to solve TCRO problems in construction project planning. J Constr Eng Manag 138(9):1065–1074

    Article  Google Scholar 

  19. El-Wakeel AS, Smith AC (2015) Hybrid fuzzy-particle swarm optimization-simplex (F-PSO-S) algorithm for optimum design of PM drive couplings. Electric Power Components and Systems 43(13):1560–1571

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Logesh Babu.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Logesh Babu, R., Balasubramanie, P. Fuzzy Rule Selection Using Hybrid Artificial Bee Colony with 2-Opt Algorithm for MANET. Mobile Netw Appl 25, 585–595 (2020). https://doi.org/10.1007/s11036-019-01354-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01354-z

Keywords

Navigation