Investigation on Bio-Inspired Population Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32797
Investigation on Bio-Inspired Population Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks

Authors: C. Rajan, K. Geetha, C. Rasi Priya, R. Sasikala

Abstract:

Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution. Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algorithms leads to the efficient transmission of data in mobile ad hoc networks. The algorithms that are inspired by the principles of naturally-distributed/collective behavior of social colonies have shown excellence in dealing with complex optimization problems. Thus some of the bio-inspired metaheuristic algorithms help to increase the efficiency of routing in ad hoc networks. This survey work presents the overview of bio-inspired metaheuristic algorithms which support the efficiency of routing in mobile ad hoc networks.

Keywords: Ant colony optimization algorithm, Genetic algorithm, naturally inspired algorithms and particle swarm optimization algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099682

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3567

References:


[1] Alexandros Giagkos, Myra S. Wilson, “BeeIP – A Swarm Intelligence based routing for wireless ad hoc networks” in Information Sciences 265, 23–35, 2014.
[2] Alireza Sajedi Nasab, Vali Derhamia, Leyli Mohammad Khanlib and Ali Mohammad Zareha Bidokia, “Energy-aware multicast routing in manet based on particle swarm optimization”, Procedia Technology 1, 434 – 438, 2012.
[3] Anjum A. Mohammed, Gihan Nagib, “Optimal Routing In Ad-Hoc Network Using Genetic Algorithm” in Int. J. Advanced Networking and Applications, Volume: 03, Issue: 05, Pages: 1323-1328, 2012.
[4] Anshu Garg, Amit Sharma, Prof. (Dr.) Ajay Pratap and Ankita Singh, “Applied Multiagent Ant Based Hybrid Routing Algorithm For Mobile Ad Hoc Networks”, International Journal. EnCoTe, v0102, 28 – 34, 2012.
[5] Beheshti, Z., Shamsuddin, S. M., Yuhaniz, S. S., “Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems”, in Journal of Global Optimization, 57:549-573, 2013.
[6] Chenn-Jung Huang, Yi-Ta Chuang and Kai-Wen Hu, “Using particle swam optimization for QoS in ad-hoc multicast”, in Engineering Applications of Artificial Intelligence 22, 1188–1193, 2009.
[7] Debajit Sensarma and Koushik Majumder, “An Efficient Ant Based QoS Aware Intelligent Temporally Ordered Routing Algorithm for MANETs”, in International Journal of Computer Networks & Communications (IJCNC), Vol.5, No.4, 2013.
[8] Dhamodharan. T, Vimalanand. S and Chandrasekar. C, “Bio Inspired and Evolutionary Approaches to Optimize MANET Routing”, in International Journal of Computing Academic Research (IJCAR), ISSN 2305-9184 Volume 2, Number 3, pp. 88-98, 2013.
[9] Dorigo. M, “Optimization, Learning and Natural Algorithms (in Italian)”, PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, pp.140, 1992.
[10] Dorigo. M, Maniezzo. V, Colorni. A, “The ant system: optimization by a colony of cooperating agents”, IEEE Transactions on Systems, Man, and Cybernetics-Part B 26(1):29-41, 1996.
[11] Elisa Valentina Onet and Ecaterina Vladu, “Nature inspired algorithms and Artificial Intelligence”, Journal of Computer Science, 2005.
[12] Gurpreet Singh, Neeraj Kumar and Anil Kumar Verma, “OANTALG: An Orientation Based Ant Colony Algorithm for Mobile Ad Hoc Networks” in Wireless PersCommun, Springer Science, Business Media New York, 2014.
[13] Holland, J. H., “Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence” Michigan, Ann Arbor, University of Michigan Press, 1975.
[14] Humayun Bakht, “Computing Unplugged, Wireless infrastructure, Some Applications of Mobile ad hoc networks”, 2003.
[15] Jianping Wang, Eseosa Osagie, Parimala Thulasiraman and Ruppa K. Thulasiram, “HOPNET: A hybrid ant colony optimization algorithm for mobile ad hoc network”, in Ad Hoc Networks 7, 690–705, 2009.
[16] Jun Sun, Wei Fang, Xiaojun Wu, Zhenping Xie and Wenbo Xu, “QoS multicast routing using a quantum-behaved particle swarm optimization algorithm”, Engineering Applications of Artificial Intelligence 24, 123– 131, 2011.
[17] Karaboga, D., An idea based on honey bee swarm for numerical optimization, Technical Report, TR06, 2005.
[18] Kennedy, J. and Eberhart, R., “Particle swarm optimization”, Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948, 1995.
[19] Lazar, A., Reynolds, R. G., “Heuristic knowledge discovery for archaeological data using genetic algorithms and rough sets”, Artificial Intelligence Laboratory, Department of Computer Science, Wayne State University, 2003.
[20] Manoj Kumar Patel, Manas Ranjan Kabat and Chita Ranjan Tripathy, “A hybrid ACO/PSO based algorithm for QoS multicast routing problem”, Ain Shams Engineering Journal 5, 113–120, 2014.
[21] Nancharaiah. B, Chandra Mohan. B, “The performance of a hybrid routing intelligent algorithm in a mobile ad hoc network”, Computers and Electrical Engineering, 1255-1264, 2014.
[22] Pankaj Vidhate, Yogita Wankhade, “Route Optimization in Manets with ACO and GA”, in IJRET: International Journal of Research in Engineering and Technology, Volume: 02 Issue: 11, 2013.
[23] Peng-Yeng Yin, Ray-I. Chang, Chih-Chiang Chao and Yen-Ting Chu, “Niched ant colony optimization with colony guides for QoS multicast routing”, in Journal of Network and Computer Applications 40, 61–72, 2014.
[24] Qinghai Bai, “Analysis of Particle Swarm Optimization Algorithm”, in Computer and Information Science, Vol.3, No.1, 2010.
[25] Rajan. C, Shanthi. N, “Swarm Optimized Multicasting for Wireless Network”, Life Science Journal; 10(4s), 2013.
[26] 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.
[27] Sajjad Jahanbakhsh Gudakahriz, Shahram Jamali and Mina Vajed Khiavi, “Energy Efficient Routing in Mobile Ad Hoc Networks by Using Honey Bee Mating Optimization”, Journal of Advances in Computer Research, Vol. 3, No. 4, 2012.
[28] Sharvani. G. S, Ananth. A. G and Rangaswamy. T. M, “Efficient Stagnation Avoidance For Manets With Local Repair Strategy Using Ant Colony Optimization”, in International Journal of Distributed and Parallel Systems (IJDPS), Vol.3, No.5, 2012.
[29] Shengxiang Yang, Hui Cheng, and Fang Wang, “Genetic Algorithms With Immigrants and Memory Schemes for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks”, IEEE Transactions On Systems, Man and Cybernetics Part C: Applications And Reviews, Vol. 40, No. 1, 2010.
[30] Shivakumar, B. L, Amudha, T, “A Novel Nature-inspired Algorithm to solve Complex Generalized Assignment Problems”, International Journal of Research and Innovation in Computer Engineering, Vol 2, Issue 3, 280-284, 2012.
[31] Ting Lu and Jie Zhu, “Genetic Algorithm for Energy-Efficient QoS Multicast Routing”, IEEE Communications Letters, Vol. 17, No. 1, 2013.
[32] Wang H, Meng X, Li S, Xu H, “A tree-based particle swarm optimization for multicast routing”, in Computer Networks; 54: 2775– 86, 2010.
[33] Wang H, Xu H, Yi S, Shi Z, “A tree-growth based ant colony algorithm for QoS multicast routing problem”, ExpSystAppl 2011;38: 11787–95, 2011.
[34] WANG Ya-li, SONG Mei, WEI Yi-fei, WANG Ying-he and WANG Xiao-jun, “Improved ant colony-based multi-constrained QoS energysaving routing and throughput optimization in wireless Ad-hoc networks”, The Journal of China Universities of Posts and Telecommunications, 21(1): 43–53, 2014.
[35] Zahra Beheshti, Siti Mariyam Hj. Shamsuddin, “A Review of Population-based Meta-Heuristic Algorithms”, in Int. J. Advance. Soft Comput. Appl., Vol. 5, No. 1, 2013.
[36] Zhenyu Liu, Marta Z. Kwiatkowska, and Costas Constantinou, “A Biologically Inspired QoS Routing Algorithm for Mobile Ad Hoc Networks”, International Journal of Wireless and Mobile Computing (IJWMC), 2009.