IBFO_PSO: Evaluating the Performance of Bio-Inspired Integrated Bacterial Foraging Optimization Algorithm and Particle Swarm Optimization Algorithm in MANET Routing

Authors: K. Geetha, P. Thangaraj, C. Rasi Priya, C. Rajan, S. Geetha

Abstract:

This paper presents the performance of Integrated Bacterial Foraging Optimization and Particle Swarm Optimization (IBFO_PSO) technique in MANET routing. The BFO is a bio-inspired algorithm, which simulates the foraging behavior of bacteria. It is effectively applied in improving the routing performance in MANET. In results, it is proved that the PSO integrated with BFO reduces routing delay, energy consumption and communication overhead.

Keywords: Ant Colony Optimization, Bacterial Foraging Optimization, Hybrid Routing Intelligent Algorithm, Naturally inspired algorithms, Particle Swarm Optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107503

References:

[1] Manjula Poojary, B. Renuka, “Ant Colony Optimization Routing to Mobile Ad Hoc Networks in URBAN Environments”, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 2 (6), 2776-2779, 2011.
[2] Dweepna Garg and Parth Gohil, “Ant Colony Optimized Routing for Mobile Ad Hoc Networks (MANET)”, International Journal of Smart Sensors and Ad Hoc Networks (IJSSAN), ISSN No. 2248-9738 (Print), Vol-2, Iss-3,4, 2012.
[3] Narendhar. S and Amudha. T, “A Hybrid Bacterial Foraging Algorithm for Solving Job Shop Scheduling Problems”, International Journal of Programming Languages and Applications (IJPLA), Vol.2, No.4, October 2012.
[4] Abdullah Konak, Orhan Dengiz and Alice E. Smith, “Improving Network Connectivity in Ad Hoc Networks Using Particle Swarm Optimization and Agents”, International Series in Operations Research and Management Sciences 158.
[5] Swagatam Das, Arijit Biswas, Sambarta Dasgupta, and Ajith Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications”.
[6] R. Vijay, “Intelligent Bacterial Foraging Optimization Technique to Economic Load Dispatch Problem”. International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Volume-2, Issue-2. May 2012.
[7] Rehab F. Abdel-Kader, “An Improved Discrete PSO with GA Operators for Efficient QoS-Multicast Routing”, International Journal of Hybrid Information Technology, Vol. 4, No. 2, April, 2011.
[8] S. M. ELseuofi, “Quality of Service Using PSO Algorithm”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 4, No 1, Feb 2012.
[9] Preeti Gulia and Sumita Sihag. “Enhance Security in MANET using Bacterial Foraging Optimization Algorithm”. International Journal of Computer Applications (0975 – 8887). Volume 84 – No 1, December 2013.
[10] Riya Mary Thomas, “Survey of Bacterial Foraging Optimization Algorithm”, International Journal of Science and Modern Engineering (IJISME), ISSN: 2319-6386, Volume-1, Issue-4. March 2013.
[11] Xiaohui Yan, Yunlong Zhu, Hao Zhang, Hanning Chen, and Ben Niu, “An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning”, Hindawi Publishing Corporation, Discrete Dynamics in Nature and Society, Volume 2012, Article ID 409478, 20 pages.
[12] Gautam Mahapatra and Soumya Banerjee, “A Study of Bacterial Foraging Optimization Algorithm and its Applications to Solve Simultaneous Equations”. International Journal of Computer Applications (0975 – 8887). Volume 72– No.5 May 2013.
[13] Jing Dang, Anthony Brabazon, Michael O’Neill, and David Edelman, “Option Model Calibration Using a Bacterial Foraging Optimization Algorithm”, M. Giacobini et al. (Eds.): EvoWorkshops 2008, LNCS 4974, pp. 113–122, 2008.
[14] Dian Palupi Rini, Siti Mariyam Shamsuddinl and Siti Sophiyati Yuhaniz, “Particle Swarm Optimization: Technique, System and Challenges”, International Journal of Computer Applications (0975 – 8887), Volume 14– No.1, January 2011.
[15] DONG Chaojun and QIU Zulian, “Particle Swarm Optimization Algorithm Based on the Idea of Simulated Annealing”, IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.10, October 2006.
[16] Anant Baijal, Vikram Singh Chauhan and T Jayabarathi, “Application of PSO, Artificial Bee Colony and Bacterial Foraging Optimization algorithms to economic load dispatch: An analysis”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011.
[17] Rajan. C, Shanthi. N, Rasi Priya. C and Geetha. K, “Investigation on Novel based Metaheuristic Algorithms for Combinatorial Optimization Problems in Ad Hoc Networks”, World Academy of Science, Engineering and Technology, vol: 8; no: 6, 967-972, 2014.
[18] Rajan. C, Geetha. K, Rasi Priya. C and Sasikala. R, “Investigation on Bio-Inspired Population Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks”, World Academy of Science, Engineering and Technology, vol: 9, no: 3, 102-109, 2015.
[19] Humayun Bakht, “Computing Unplugged, Wireless infrastructure, Some Applications of Mobile ad hoc networks”, April-2003.
[20] Elisa Valentina Onet and Ecaterina Vladu, “Nature inspired algorithms and Artificial Intelligence”, Journal of Computer Science, 2005.
[21] C. Rajan, K. Geetha, Crasi Priya, S. Geetha,” Investigation on Novel Based Naturally-Inspired Swarm Intelligence Algorithms for Optimization Problems in Mobile Ad Hoc Networks”, World Academy of Science, Engineering and Technology International Journal of Mathematical, Computational, Natural and Physical Engineering Vol:9, No:3, 2015.