  Ant species such as Temnothorax albipennis select a new nest site in a
distributed fashion that, if modeled correctly, can serve as useful information
for site selection algorithms for robotic swarms and other applications.
Studying and replicating the ants' house hunting behavior will also illuminate
useful distributed strategies that have evolved in nature. Many of the existing
models of househunting behaviour for T. albipennis make the assumption that all
candidate nest sites are equally distant from the ants' home nest, or that an
ant has an equal probability of finding each candidate nest site. However,
realistically this is not the case, as nests that are further away from the
home nest and nests that are difficult to access are less likely to be found,
even if they are of higher quality. We extend previous house-hunting models to
account for a pairwise distance metric between nests, compare our results to
those of real colonies, and use our results to examine the effects of house
hunting in nests of different spatial orientations. Our incorporation of
distances in the ant model appear to match empirical data in situations where a
distance-quality tradeoff between nests is relevant. Furthermore, the model
continues to be on par with previous house-hunting models in experiments where
all candidate nests are equidistant from the home nest, as is typically
assumed.

Multiagent Systems