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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.8, No.4, Apr. 2016

Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem

Full Text (PDF, 607KB), PP.43-48


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Author(s)

Sudip Kumar Sahana, Mohammad AL-Fayoumi, Prabhat Kumar Mahanti

Index Terms

Ant Colony Optimization (ACO);Modified Ant Colony Optimization (MACO);Pheromone initialization;Routing;Meta-heuristics;Convergence

Abstract

It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO) algorithm which is based on Ant Colony System (ACS) with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO) shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.

Cite This Paper

Sudip Kumar Sahana, Mohammad AL-Fayoumi, Prabhat Kumar Mahanti,"Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.4, pp.43-48, 2016. DOI: 10.5815/ijisa.2016.04.05

Reference

[1]M. Dorigo, L. M. Gambardella, “Ant Colony System: A cooperative learning approach to the travelling salesman problem”, IEEE Transaction on Evolutionary Computation, pp. 53-56, 1997.

[2] S. Goss, S. Aron, J. L. Deneubourg, and J. M. Pasteels, “Self-organized shortcuts in the Argentine Ant”, Naturwissenschanften, vol. 76, pp. 579-581, 1989.

[3]Kewen Li, Jing Tian “The Multicast Routing QoS Based on the Improved ACO Algorithm”,  Journal of networks, vol. 4, no. 6, pp 505-510,August 2009. 

[4]Hua Wang, Zhao Shi, Shuai Li. “Multicast routing for delay variation bound using a modified ant colony algorithm”, Journal of Network and Computer Applications, Vol 32, pp 258-272, 2009.

[5]Hua Wang, Zhao Shi, Jun Ma, Gang Wang “The Tree-Based Ant Colony Algorithm for Multi-Constraints Multicast Routing”, 9th International Conference on Advanced Communication Technology, Vol-3, pp 1544-1547, Feb 2007.

[6]Saad Ghaleb Yaseen and Nada M. A. AL-Slamy “Ant colony optimization” IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.6, pp. 38 – 43, June 2008.

[7]Hongtao Shi, Yucai Dong, Lianghai Yi, Dongyun Zheng, Hong Ju, Weidong Li & Erchang Ma “Study on the Route Optimization of Military Logistics Distribution in Wartime Based on the Ant Colony Algorithm” Computer and Information Science, vol.3 no.1, pp. 139-143, Feb 2010.

[8]John E. Bell, Patrick R. McMullen “Ant colony optimization techniques for the vehicle routing problem” Science direct Advanced Engineering Informatics, 18, pp. 41-48, July 2004.

[9]A. J. Frank, L. D. Wittie, and A. J. Bernstein, “Multicast communication on network computers,” IEEE Software, vol. 2, no. 3, pp. 49–61, May 1985.

[10]Ying Wang Jianying Xie, “Ant Colony Optimization for Multicast Routing”, IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on Circuits and Systems, Tianjin, pp. 54-57, 2000.

[11]Ping Yuan and Long Hai, “An Improved ACO Algorithm for Multicast in Ad hoc Networks”, International Conference on Communications and Mobile Computing, Shenzhen,  pp 234-238, 2010.

[12]Hua Wang,Zhao Shi,Shuai Li, “Multicast Routing for delay variation bound using a modified Ant Colony algorithm”, Journal of network and computer Application, vol. 32, No.1, , pp. 258-272, 2009.

[13]H. Wang, H. Xu, S. Yi, Z. Shi, “A tree-growth based ant colony algorithm for QoS multicast routing problem”, Exp Syst Appl, 38, pp. 11787–11795, 2011.

[14]Y. Huang, "Research on QoS Multicast Tree Based on    Ant Colony Algorithm", Applied Mechanics and Materials, Vols.635-637, pp. 1734-1737, Sep. 2014.

[15]J. Zhou, Q. Cao, C. Li and R. Huang, “A genetic algorithm based on extended sequence and topology encoding for the multicast protocol in two-tiered WSN”,  Exp Syst Appl, 37 (2) , pp. 1684–1695, 2010.

[16]Li C, Cao C, Li Y and Yu Y, “Hybrid of genetic algorithm and particle swarm optimization for multicast QoS routing”. In: IEEE international conference controls automation, pp. 2355–59, 2007.

[17]Chen Xi-hong, Liu Shao-wei, Guan Jiao, Liu Qiang, "Study on QoS Multicast Routing Based on ACO-PSO Algorithm, “International Conference on  Intelligent Computation Technology and Automation (ICICTA)”, vol.3, pp.534-537, 11-12 May 2010. 

[18]H. Wang, X. Meng, S. Li and H. Xu, “A tree-based particle swarm optimization for multicast routing” , Computer  Networks, 54 , pp. 2775–2786, 2010.

[19]H. Wang, X. Meng, M. Zhang and Y. Li,  “Tabu search algorithm for RP selection in PIM-SM multicast routing”,  Elsevier Computer Communication, 33,  pp. 35–42, 2009.

[20]S.K. Sahana, and A. Jain, “High Performance Ant Colony Optimizer (HPACO) for Travelling Salesman Problem (TSP)”, 5th International Conference on ICSI, Hefei, China, In: Advances in Swarm Intelligence, Vol 8794,  

Springer International Publishing, Lecture Notes in Computer Science (LNCS), pp 165-172, 2014.

[21]S.K. Sahana, and A. Jain, “Modified Ant Colony Optimizer (MACO) for the Travelling Salesman Problem”, Computational Intelligence and Information Technology CIIT 2012, Chennai, In: ACEEE Conference Proceedings Series 3 by Elsevier , pp 267-276, 3-4 Dec, 2012.

[22]S.K.Sahana, A.Jain and P.K. Mahanti, “Ant Colony Optimization for Train Scheduling:An Analysis”, I.J. Intelligent Systems and Applications,Vol-6,Number-2, pp29-36 ,2014.

[23]S.K.Sahana and K. Kumar, “Hybrid Synchronous Discrete Distance Time Model for Traffic Signal Optimization”, International Conference on Computational Intelligence & Data Mining 2014, Burla, Orrisa, India, In: Series Smart Innovation, Systems and Technologies,Vol-31, Book Computational Intelligence in Data Mining, Springer India, pp 23-33, December 20-21, 2014. 

[24]S.Srivastava, S.K.Sahana, D. Pant, and P.K. Mahanti, “Hybrid Microscopic Discrete Evolutionary Model for Traffic Signal Optimization”, Journal of Next Generation Information Technology (JNIT), Volume 6(2), pp1-6, 29th May,2015.