A New Swarm Intelligence Coordination Model Inspired by Collective Prey Retrieval and Its Application to Image Alignment
Swarm Intelligence is the emergent collective intelligence of groups of simple agents acting almost independently. Algorithms following this paradigm have many desirable properties: flexibility, decentralized control, robustness, and fault tolerance. This paper presents a novel agent coordination model inspired by the way ants collectively transport large preys. In our model a swarm of agents, each having a different destination to reach, moves with no centralized control in the direction indicated by the majority of agents keeping its initial shape. The model is used to build an algorithm for the problems of image alignment and image matching. The novelty of the approach and its effectiveness are discussed.
KeywordsParticle Swarm Optimization Prefer Destination Swarm Intelligence Large Prey Speckle Noise
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- 1.Brooks, R.A.: Intelligence without representation. Dedham, Endicott House (1987)Google Scholar
- 2.Birattari, M., Caro, G.D., Dorigo, M.: Toward the formal foundation of ant programming. In: Ant Algorithms, pp. 188–201 (2002)Google Scholar
- 3.Leonardi, L., Mamei, M., Zambonelli, F.: Co-fields: Towards a unifying model for swarm intelligence (2002)Google Scholar
- 6.Kennedy, J., Eberhart, R.C.: Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
- 7.Ramos, V., Almeida, F.: Artificial ant colonies in digital image habitats - a mass behaviour effect study on pattern recognition (2000)Google Scholar
- 8.Bourjot, C., Chevrier, V., Thomas, V.: Web Intelligence and Agent System. In: A new swarm mechanism based on social spiders colonies: from web weaving to region detection, vol. 1, pp. 47–64 (2003)Google Scholar
- 9.Lumer, E., Faieta, B.: Exploratory database analysis via self-organization (1995) (unpubished manuscript)Google Scholar
- 10.Kondacs, A.: Biologically-inspired self-assembly of two-dimensional shapes using global-to-local compilation. In: IJCAI, pp. 633–638 (2003)Google Scholar
- 15.Meshoul, S., Batouche, M.: Ant colony system with extremal dynamics for point matching and pose estimation. In: ICPR (3), pp. 823–826 (2002)Google Scholar