We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Ant Colony Optimization: A Swarm Intelligence based Technique

by Manju, Chander Kant
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 10
Year of Publication: 2013
Authors: Manju, Chander Kant
10.5120/12779-9387

Manju, Chander Kant . Ant Colony Optimization: A Swarm Intelligence based Technique. International Journal of Computer Applications. 73, 10 ( July 2013), 30-33. DOI=10.5120/12779-9387

@article{ 10.5120/12779-9387,
author = { Manju, Chander Kant },
title = { Ant Colony Optimization: A Swarm Intelligence based Technique },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 10 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 30-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number10/12779-9387/ },
doi = { 10.5120/12779-9387 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:44.723749+05:30
%A Manju
%A Chander Kant
%T Ant Colony Optimization: A Swarm Intelligence based Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 10
%P 30-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Divide and conquer techniques are one way to solve large and difficult problems. Division of large work into smaller parts and combining the solution of small problems to get the solution of large one has been a practice in computer research since long time. Swarm also exhibits the behavior of division of work and cooperation to achieve difficult tasks. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples which show that nature has been an unending source of inspiration. Artificial Swarm/Ant foraging utilizes various forms of indirect communication, involving the implicit transfer of information from agent to agent through modification of the environment. Using this approach, one can design efficient searching methods that can find solution to complex optimization problems. Over times, several algorithms have been designed and used that are inspired by the foraging behavior of real ants colonies to find solutions to difficult problems. In this paper the idea of Ant Colonies is presented with brief introduction to its applications in different areas of problem solving in computer science.

References
  1. Dorigo, M. , and Stützle, T. "Ant Colony Optimization", MIT Press, 2004Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  2. Parpinelli, R. S. , and Lopes, H. S. 2011 "New inspirations in swarm intelligence: a survey", International Journal of Bio-Inspired Computation, Vol. 3, No. 1, pp. 1-16.
  3. Dorigo, M. , Maniezzo, V. & Colorni, A. 1996 " Ant System: Optimization by a Colony of Cooperating Agents", IEEE Transactions on Systems, Man, and Cybernetics-Part B, Vol. 2, No. 1, pp. 29-41.
  4. Fraga, H. 2008 "Firefly luminescence: a historical perspective and recent developments", Journal of Photochemical & Photobiological Sciences, Vol. 7, pp. 146–158.
  5. Havens, T. , Spain, C. , Salmon, N. and Keller, J. 2008 "Roach infestation optimization", IEEE Swarm Intelligence Symposium, September, pp. 1–7.
  6. Karaboga, D. 2005 "An idea based on honey bee swarm for numerical optimization', Technical report, Erciyes University, Engineering Faculty, Computer Engineering Department.
  7. Kennedy, J. , and Eberhart, R. C. 1995 "Particle swarm optimization", Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ. , pp. 1942-1948.
  8. Passino, K. 2002 "Biomimicry of bacterial foraging for distributed optimization and control", IEEE Control Systems Magazine, pp. 52–67.
  9. Yang, X. S. 2009 "Firefly algorithms for multimodal optimization", SAGA 2009, Lecture Notes in Computer Science, 5792, pp. 169-178.
  10. Yang, X. 2010 "A new metaheuristic bat-inspired algorithm", Nature Inspired Cooerative Strategies for Optimization (NISCO 2010), Studies in Computational Intelligence, Vol. 284, pp. 65–74, Springer Berlin.
  11. Agrawal, R. , Imielinski, T. , and Swami, A. 1993 "Mining association rules between sets of items in large databases", Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 207-216.
  12. Deneubourg, J. -L. , Aron, S. , Goss, S. , & Pasteels, J. M. 1990 "The Self –Organizing Exploratory Pattern of the Argentine Ant:, Journal of Insect Behaviour, Vol. 3, pp. 159-168.
  13. Eusuff, M. M. and Lansey, K. E. 2003 "Optimization of water distribution network design using the shuffled frog leaping algorithm", Journal of Water Resource Planning Management, , 210 – 225. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), pp. 1289-1305.
  14. Dao-I Lin, Zvi M Kedem. 1997 "Pincer Search: A New Algorithm for Discovering the Maximum Frequent Item Set", New York University. J. Han, M. Kamber. 2001 "Data Mining: Concepts and Techniques" Morgan Kaufmann Publishers.
Index Terms

Computer Science
Information Sciences

Keywords

Swarm Intelligence Ant Colony Optimization Association Rule Mining