CFP last date
20 May 2024
Reseach Article

Efficient Pheromone adjustment techniques in ACO for Ad Hoc Wireless network

by Sharvani G S, T. M. Rangaswamy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 6
Year of Publication: 2012
Authors: Sharvani G S, T. M. Rangaswamy
10.5120/6268-8424

Sharvani G S, T. M. Rangaswamy . Efficient Pheromone adjustment techniques in ACO for Ad Hoc Wireless network. International Journal of Computer Applications. 44, 6 ( April 2012), 29-32. DOI=10.5120/6268-8424

@article{ 10.5120/6268-8424,
author = { Sharvani G S, T. M. Rangaswamy },
title = { Efficient Pheromone adjustment techniques in ACO for Ad Hoc Wireless network },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 6 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number6/6268-8424/ },
doi = { 10.5120/6268-8424 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:51.427746+05:30
%A Sharvani G S
%A T. M. Rangaswamy
%T Efficient Pheromone adjustment techniques in ACO for Ad Hoc Wireless network
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 6
%P 29-32
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ant Colonies have emergent problem solving nature like food foraging of Ants etc. ,. Such problem solving nature of Ant Colonies have inspired emergence of efficient routing algorithms, especially in Ad Hoc Wireless Networks (AWN). They get inspiration from Ants which use simple rules with no direct communication and finds the shortest path by sensing the chemical called 'Pheromone'. Ant Colony Optimization (ACO) is one such routing algorithm. ACO is Agent based routing algorithm and such agent based routing algorithms provide adaptive and efficient utilization of resources in a dynamic environment and also cater for for load balancing and fault management. However there are few issues with ACO to be addressed while adapting ACO for routing on AWN. . One of the Major issues is load balancing due to a problem called 'Stagnation'. Stagnation occurs when all the packets starts travelling on the optimal path and loses packets due to congestion. There are many techniques adopted in ACO to alleviate this problem. This paper focuses on study of different techniques to address the stagnation problem.

References
  1. C Siva Ram Murthy & B S Manoj " Ad Hoc Wireless Networks Architectures and protocols" Pearson Education , 2nd Edition 2005
  2. Bonabeau, E, Dorigo M and Theraulaz G, "Swarm Intelligence from Natural to Artificial Systems", Oxford University Press, 1999.
  3. Bonabeau, E, Dorigo M and Theraulaz G, " Inspiration for optimization from social insect behavior, Vol 406, 39-42, 0028-0836, 2000
  4. 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
  5. Dorigo M, Birattari M and Stuzle T," Ant Colony Optimization. Artificial Ants as a computational Intelligence Technique ", Technical Report , IRDIA, 1-12, 1781-3794, 2006
  6. 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
  7. 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
  8. Purkayastha P and Baras J S , " Convergence results for ant routing algorithm via stochastic approximation and optimization, proceedings of the 46th IEE conference on decision and control ,pp 340-345 2007.
  9. De Rango F , Tropea M , Provato A, Sanmaria A F and Marano S , " Minimum Hop Count and Load Balancing Metrics based on Ant Behavior over HAP Mesh", IEEE GLOBECOMM, pp 1-6, New Orleans, 2008.
  10. 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
  11. Niaz Morshed Cowdhury, Syed Murtoza , Ershadul H. Choudhury " A new adaptive routing approach based on ANT Colony Optimization(ACO) for Ad Hoc Wireless Networks, Proceedings of International workshop on internet and distributed Computing Systems 2008, pp 51-56
  12. Ducatelle F, Di Caro G and Gambardella L M, " Principles and applications of Swarm Intelligence for adaptive routing in telecommunications networks", Swarm Intelligence,2010.
  13. Dorigo M, V Maniezzo and A Colorni, " The Ant system : Optimization by a Colony of Cooperating Agents. ". IEEE Transactions on Systems , Man and cybernetics-Part B, pp 29-41, 1996
  14. T Stutzle, H H Hoos ,"Max-Min Ant system" Future Generation Computing Syst,(2000), PP 889-914.
  15. T Stutzle, H H Hoos, " Improvements on the ant system: Introducing the max min ant system, in : Third International Conference on Artificial Neural Networks and Genetic Algorithms, Springer Verlag, University of East Anglia, Norwich, 1998, pp 245-249
  16. 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.
  17. 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
  18. David C Mathew, " Improved Lower Limits for Pheromone Trails in ACO", G Rudolf et al(Eds), LNCS 5199, pp 508-517, Springer Verlag, 2008.
  19. 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.
  20. 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
  21. Alaa Alijanaby, KU Ruhana Kumahamud, Norita Md Norwawi, "Interacted Multiple Ant a. 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
  22. Raka Jovanovic and Milan Tuba, " An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem", Elsvier,Applied Soft Computing, PP 5360-5366,2011.
  23. 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
  24. Zar Ch Su Su Hlaing, May Aye Lhine, " An Ant Colony Optimisation Algorithm for solving Traveling Salesman Problem", International Conference on Information Communication and management( IPCSIT), Vol,6, pp 54-59, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Adhoc Wireless Netwrok Swarm Intelligence Ant Colony Optimization Stagnation