Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Information Sharing in Swarm Intelligence Techniques: A Perspective Application for Natural Terrain Feature Elicitation

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 1
Year of Publication: 2011
Lavika Goel
Daya Gupta
V. K. Panchal

Lavika Goel, Daya Gupta and V K Panchal. Article: Information Sharing in Swarm Intelligence Techniques: A Perspective Application for Natural Terrain Feature Elicitation. International Journal of Computer Applications 32(2):34-40, October 2011. Full text available. BibTeX

	author = {Lavika Goel and Daya Gupta and V. K. Panchal},
	title = {Article: Information Sharing in Swarm Intelligence Techniques: A Perspective Application for Natural Terrain Feature Elicitation},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {32},
	number = {2},
	pages = {34-40},
	month = {October},
	note = {Full text available}


Swarm intelligence (SI) is an Artificial Intelligence technique based on the study of collective behaviour in decentralized, self-organizing systems. It enables relatively simple agents to collectively perform complex tasks, which could not be performed by individual agents separately. Particles can interact either directly or indirectly (through the environment). The key to maintain global, self-organized behaviour is social interaction i.e. information sharing between the system's individuals. Hence, information sharing is essential in swarm intelligence. In this paper, we highlight how the concept of information sharing in various swarm-based approaches can be utilised as a perspective application towards the elicitation of natural terrain features. The paper provides a mathematical formulation of the concept of information sharing in each of the swarm intelligence techniques of Biogeography based optimization (BBO), Ant Colony Optimization (ACO), Particle Swarm optimization (PSO) and Bee Colony Optimization (BCO) which are the major constituents of the SI techniques that have been used till date for classifying topographical facets over natural terrain.


  • Dan Simon, 2008. Biogeography Based Optimization, IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6.
  • D. Karaboga, 2005. An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report-TR06.
  • Dervis Karaboga and Bahriye Basturk, 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony algorithm, Springer Science+Business Media B.V.
  • Dervis Karaboga and Bahriye Akay, 2009. A comparative study of Artificial Bee Colony algorithm, Applied Mathematics and Computation 214, pp. 108–132.
  • Dorigo M., V. Maniezzo & A. Colorni, 1996. Ant System: Optimization by a Colony of Cooperating Agents", IEEE Transactions on Systems, Man, and Cybernetics–Part B, 26(1): pp. 29–41.
  • Dušan TEODOROVIĆ1,2, Mauro DELL’ ORCO, 2000. Bee Colony Optimization – A Cooperative Learning Approach To Complex Transportation Problems, Advanced OR and AI methods in Transportation.
  • Haiping Ma, 2010. An analysis of the equilibrium of migration models for biogeography-based optimization, Information Sciences 180, pp. 3444–3464.
  • Haiping Ma, Suhong Ni, and Man Sun, 2009. Equilibrium Species Counts and Migration Model Tradeoffs for Biogeography Based Based Optimization, Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai.
  • Kennedy, J.; Eberhart, R., 1995. Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks(IV), pp. 1942–1948.
  • Lavika Goel, Daya Gupta, V.K. Panchal, 2010. Hybrid ACO-BBO Approach for predicting the deployment strategies of enemy troops in a military terrain application, Second International Conference on Intelligent Systems and Nanotechnology.
  • Lavika Goel, V.K. Panchal, Daya Gupta, 2010. Embedding Expert knowledge to Hybrid Bio-Inspired Techniques- An Adaptive Strategy Towards Focused Land Cover Feature Extraction, International Journal of Computer Science & Information Security), ISSN: 1947-5500, Vol. 8 No. 2, pp. 244-253.
  • Navdeep Kaur Johal, Samandeep Singh and Harish Kundra, , 2010. A hybrid FPAB/BBO Algorithm for Satellite Image Classification, International Journal of Computer Applications (0975 – 8887),Volume 6– No.5.
  • Shelly Bansal, Daya Gupta, V.K. Panchal ,Shashi Kumar, 2009. Remote Sensing Image Classification by Improved Swarm Inspired Techniques in International Conference on Artificial Intelligence and Pattern Recognition (AIPR-09).
  • Tomas Piatrik and Ebroul Izquierdo , 2006. Image Classification Using an Ant Colony Optimization Approach, LNCS 4306, pp. 159 – 168.
  • WangDong, Wu Xiang-Bin, 2008. Particle Swarm Intelligence Classification Algorithm for Remote Sensing Images, IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.