Self-Regulated Artificial Ant Colonies on Digital Image Habitats

Artificial life models, swarm intelligent and evolutionary computation algorithms are usually built on fixed size populations. Some studies indicate however that varying the population size can increase the adaptability of these systems and their capability to react to changing environments. In this...

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Bibliographic Details
Published in:arXiv.org 2005-12
Main Authors: Fernandes, Carlos, Ramos, Vitorino, Rosa, Agostinho C
Format: Article
Language:English
Subjects:
Changing environments
Cognitive maps
Digital imaging
Evolutionary algorithms
Foraging habitats
Image processing
Image segmentation
Mathematical analysis
Mathematical models
Mathematical morphology
Morphology
Object recognition
Pattern recognition
Swarm intelligence
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Summary:Artificial life models, swarm intelligent and evolutionary computation algorithms are usually built on fixed size populations. Some studies indicate however that varying the population size can increase the adaptability of these systems and their capability to react to changing environments. In this paper we present an extended model of an artificial ant colony system designed to evolve on digital image habitats. We will show that the present swarm can adapt the size of the population according to the type of image on which it is evolving and reacting faster to changing images, thus converging more rapidly to the new desired regions, regulating the number of his image foraging agents. Finally, we will show evidences that the model can be associated with the Mathematical Morphology Watershed algorithm to improve the segmentation of digital grey-scale images. KEYWORDS: Swarm Intelligence, Perception and Image Processing, Pattern Recognition, Mathematical Morphology, Social Cognitive Maps, Social Foraging, Self-Organization, Distributed Search.
ISSN:2331-8422