Optimization Of Manet Routing Using Hybrid DSR with ABC Algorithm

Authors

  • Eswar Patnala Research Scholar ,Gst,Gitam University, A.P, India
  • Srinivasa Rao Giduturi Associate Professor ,Gst, Gitam University, A.P, India

Keywords:

ABC Algorithm, Swarm Optimization, Scout bees, Onlooker bees, Employed bees, meta-heuristics, evolution strategy

Abstract

Different mobile nodes are linked together through wireless networks using a system that is independent, self-configuring, and infrastructure-free called a MANET. AODV, DSDV, TORA, DSR& other routing protocols are used in Mobile Ad Hoc Networks (MANET). Mobility, overhead, battery depletion, delay, and interference are just a few of the problems and limitations that MANETs face because of their constantly changing topologies and lack of infrastructure. The best and optimal solution can be discovered using a variety of optimization techniques. Meta heuristics called "nature inspired algorithms" imitate nature to solve optimization issues, ushering in a new era of computation. The Artificial Bee Colony (ABC) program, developed by Karaboga, reproduces foraging behaviorof honey bees. Since ABC's beginnings, extensive study has been done to improve its effectiveness and expand its applications.

Downloads

Download data is not yet available.

References

Kamaldeep Kaur and Lokesh Pawar, Review of various optimization Techniques in MANET Routing Protocols. Volume 4, Issue 8 ,International Journal of Science, Engineering and Technology Research (IJSETR),

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. D. Karaboga and B. Basturk (2007), Journal of Global Optimization, 39(3), pp. 459–471.D. Karaboga.

7ah, R.Ghazal, ,N.Garg,andTairan.H(2018). 7(4), p.69.Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization. Computers,

D.Karaboga, &B.Akay(2012), 192, pp.120-142.A modified Artificial Bee Colony algorithm for real-parameter optimization. Information Sciences,

N.Karaboga, C.Ozturk ,Gorkemli, D.Karaboga (2012). 42(1), pp.21-57,A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review

B.Akay ,D.Karaboga,(2011). 11(3), pp.3021-3031,A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing,

26(4), pp.2090-2101. IEEE Transactions on Power Delivery, M.El-Hawary&F.Abu-Mouti,(2011),Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm.

T.Chua, ,P.Suganthan.M.FatihTasgetiren andQ.Pan, (2011). 181(12), pp.2455-2468,A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences,

A.Singh ,9(2), pp.625-631. (2009),An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Applied Soft Computing

. B.Sirinaovakul, T.Achalakul andA.Banharnsakun(2011),The best-so-far selection in Artificial Bee Colony algorithm. Applied Soft Computing, 11(2), pp.2888-2901

K.Xiangy,W.Zhen. (2013),An Improved Artificial Bee Colony Algorithm for Global Optimization. Information Technology Journal, 12(24), pp.8362-8369.

H.Garg. and R.Ghazali, N.Tairan, H.Shah, (2018). 7(4), p.69.Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization. Computers

S.Nandy,Training a Feed-Forward Neural Network with Artificial Bee Colony based Back propagation Method. International Journal of Computer Science and Information Technology, (2012). 4(4), pp.33-46,

W.Yeh, , and T.Hsieh, H.Hsiao(2011). 11(2), pp.2510-2525,Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Applied Soft Computing,

B. Akay, (2013),A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Applied Soft Computing, 13(6), pp.3066-3091.

S.NabaviNiakiand.A.LashkarAraM.Kefayat, (2015),92, pp.149-161.A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Conversion and Management,

, F.Kang., Ma, Z. Li, H and. Li, J.(2011). 6(3),Artificial Bee Colony Algorithm with Local Search for Numerical Optimization. Journal of Software

Jagdish Chand Bansal, Harish Sharma, Shimpi Singh Jadon,Artificial bee colony algorithm: a survey, Int. J. Advanced Intelligence Paradigms, Vol. 5, Nos. 1/2, 2013,

Prof. Virendra Umale. (2020). Design and Analysis of Low Power Dual Edge Triggered Mechanism Flip-Flop Employing Power Gating Methodology. International Journal of New Practices in Management and Engineering, 6(01), 26 - 31. https://doi.org/10.17762/ijnpme.v6i01.53

Meena , B. S. . (2023). Plant Health Prediction and Monitoring Based on convolution Neural Network in North-East India. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 12–19. https://doi.org/10.17762/ijritcc.v11i2s.6024

Kothandaraman, D., Praveena, N., Varadarajkumar, K., Madhav Rao, B., Dhabliya, D., Satla, S., & Abera, W. (2022). Intelligent forecasting of air quality and pollution prediction using machine learning. Adsorption Science and Technology, 2022 doi:10.1155/2022/5086622

Downloads

Published

16.07.2023

How to Cite

Patnala, E. ., & Giduturi, S. R. . (2023). Optimization Of Manet Routing Using Hybrid DSR with ABC Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 1124–1131. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3372

Issue

Section

Research Article