  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.
