G. Venturini, Laboratoire d'Informatique de Tours, Université de Tours, Tours, France
M. Slimane, Laboratoire d'Informatique de Tours, Université de Tours, Tours, France
Email: slimane@univ-tours.fr
We present in this paper a new model of artificial ants foraging
behavior based on a population of primitive ants (Pachycondyla
apicalis) and its application to the general problem of
optimization. These ants are characterized by a relatively simple but
efficient strategy of prey search where individuals hunt alone and try
to cover uniformly a given area around their nest. This is performed
by parallel local searches on hunting sites with a sensitivity to
successful sites. Also, the nest is moved periodically. That
corresponds to a restart operator of the parallel searches where the
central point is moved. Furthermore, these ants are able to perform
some form of recruitment called ``tandem-running'' where one leading
ant is followed by another one to a given interesting site. We have
applied this algorithmic model, called API, to combinatorial and
numerical optimization problems.