The week's pick
Random Articles
Reseach Article
Analysis of Different Pheromone Decay Techniques for ACO based Routing in Ad Hoc Wireless Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 56 - Number 2 |
| Year of Publication: 2012 |
| Authors: Sharvani G S, A G Ananth, T M Rangaswamy |
10.5120/8866-2833
|
Sharvani G S, A G Ananth, T M Rangaswamy . Analysis of Different Pheromone Decay Techniques for ACO based Routing in Ad Hoc Wireless Networks. International Journal of Computer Applications. 56, 2 ( October 2012), 31-38. DOI=10.5120/8866-2833
Abstract
Ant Colony Optimization (ACO) technique deals with exploratory behavior of ants while finding food by following a path based on the concentration of the pheromone. A major limitation with ACO algorithm is "stagnation". This occurs when all ants try to follow same path to reach the destination due to higher pheromone concentration and causes congestion when applied to Adhoc Wireless Network (AWN). In the present paper, a detailed analysis of ACO based different pheromone decay techniques such as Discrete, Exponential and Polynomial has been carried out. Pheromone intensity and probability of choosing path for packet transmission are used as parameters for the analysis. It is found that the Discrete decay is not preferable for Congestive network as it leaves large amount of pheromone traces. The polynomial decay technique choose better path and avoid longest path which lead to delay at the time of packet delivery. The Exponential decay has been found to exhibit better performance compared to Discrete and Polynomial decay techniques, However it loses the pheromone traces very fast. The Efficient fine tuning of the exponential decay model can be achieved by using stability factor '?'. The present analysis shows that for values of '?'< 0. 08 the probability of selection of the longest optimal paths is < 1%, where as for '?' > 0. 09 the probability of selection of the longest optimal path increases to 18%. . The introduction of the stability factor '?' improves AWN performance in terms of packet delivery. The results are presented and discussed in the present paper.
References
- Bonabeau, E, Dorigo M and Theraulaz G, "Swarm Intelligence from Natural to Artificial Systems", Oxford University Press, 1999.
- Bonabeau, E, Dorigo M and Theraulaz G, " Inspiration for optimization from social insect behavior, Vol 406, 39-42, 0028-0836, 2000
- Di Caro G, Ducatelle F and Gambarella L M, "AntHocNet" Ant-based Hybrid Routing algorithm for MANETs", In : IDSIA-25-04-2004 Technical Report , 1-12 Dalle Molle Institute for Artificial Intelligence, Switzerland , 2004
- F Neuman D Sudholt, C Witt," Rigorous analyses for the combination of ant colony optimization and local search", ANTS 2008, Proceedings of the 6th International Conference on ACO and Swarm Intelligence, Springer-Verlag, Berlin, pp 132-143 , 2008
- Rajagopalan S and Shen C C, "ANSI:"A swrm Intelligence-based unicast routing protocol for Hybrid AWN,", Journal of System Architecture, Special issues on Nature Inspired Applied Systems, 2007, 485-504
- Saleem M and Farooq M. A frame work for empirical evaluation of nature inspired routing protocols for wireless sensor networks", In proceedings of the IEEE congress on evolutionary computing, PP 5751-758, 2007.
- Saleem M, Khayam S and Farooq M," A formal performance modeling framework for bio Inspired ad Hoc routing protocols", in ACM GECO, PP 103-110, New York Acm, 2008.
- Dorigo M, Birattari M and Stuzle T," Ant Colony Optimisation. Artificial Ants as a computational Intelligence Technique ", Technical Report , IRDIA, 1-12, 1781-3794, 2006
- De Rango F , Tropea M , Provato A, Sanmaria A F and Marano S , " Minimum Hop Count and Load Balancing Metrics based on Ant Behaviour over HAP Mesh", IEEE GLOBECOMM, pp 1-6, New Orleans, 2008.
- De Rango F , Tropea M," Energy saving and Load balancing in wireless adhoc networks through ant basaed routing", SPECTS, Vol 41, 978-1-2-4244-4165-5,2009
- Ducatelle F, Di Caro G and Gambardella L M, "Principles and applications of Swarm Intelligence for adaptive routing in telecommunications networks", Swarm Intelligence, 2010.
- T Stutzle, H H Hoos ,"Max-Min Ant system" Future Generation Computing Syst,(2000), PP 889-914.
- R Kumar, M K Tiwari and R Shankar, "Scheduling of flexible manufacturing systems: an ant colony optimization approach", Proceedings Instn Mech Engrs Vol 217, Part B: J Engineering Manufacture, 2003,pp 1443-1453.
- Kuan Yew Wong, Phen Chiak See, " A New minimum pheromone threshold strategy(MPTS) for Max-min ant system ", Applied Soft computing, Vol 9, 2009, pp 882-888
- David C Mathew, "Improved Lower Limits for Pheromone Trails in ACO", G Rudolf et al (Eds), LNCS 5199, pp 508-517, Springer Verlag, 2008.
- Laalaoui Y, Drias H, Bouridah A and Ahmed R B, " Ant Colony system with stagnation avoidance for the scheduling of real time tasks", Computational Intelligence in scheduling, IEEE symposium, 2009, pp 1-6.
- E Priya Darshini, " Implementation of ACO algorithm for EDGE detection and Sorting Salesman problem", International Journal of Engineering science and Technology, Vol 2, pp 2304-2315, 2010
- Alaa Alijanaby, KU Ruhana Kumahamud, Norita Md Norwawi, "Interacted Multiple Ant Colonies optimization Frame work: an experimental study of the evaluation and the exploration techniques to control the search stagnation", International Journal of Advancements in computing Technology Vol 2, No 1, March 2010, pp 78-85
- Raka Jovanovic and Milan Tuba, " An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem", Elsevier, Applied Soft Computing, PP 5360-5366,2011.
- Priyanka Sharma, Dr K Kotecha, " Optimization in stagnation avoidance of ACO based routing of Multimedia Traffic over Hybrid MANETs", International Journal of computer science and technology, IJCST, Issue 2, ISSN: 2229-4333(print), 0976-8491(online), 2011
- Zar Ch Su Hlaing, May Aye Lhine, " An Ant Colony Optimization Algorithm for solving Traveling Salesman Problem", International Conference on Information Communication and management( IPCSIT), Vol,6, pp 54-59, 2011
- Sharvani G S and Dr. T M Rangaswamy, " Efficient Pheromone Adjustment Techniques in ACO for Ad Hoc Wireless network, IJCA(0975-8887), Vol 44-No 6, pp 29-32 April 2012.
- K. V. Viswanatha Cauvery N. K. , "Enhanced Ant Colony Based Algorithm for Routing in Mobile AdHoc Network," World Academy of Science, Engineering and Technology, p. 46, 2008.
Index Terms
Keywords