G. Venturini, Laboratoire d'Informatique de Tours, Université de Tours, Tours, France
M. Slimane, Laboratoire d'Informatique de Tours, Université de Tours, Tours, France
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.