  Swarm intelligence is widely recognized as a powerful paradigm of
self-organized optimization, with numerous examples of successful applications
in distributed artificial intelligence. However, the role of physical
interactions in the organization of traffic flows in ants under crowded
conditions has only been studied very recently. The related results suggest new
ways of congestion control and simple algorithms for optimal resource usage
based on local interactions and, therefore, decentralized control concepts.
Here, we present a mathematical analysis of such a concept for an experiment
with two alternative ways with limited capacities between a food source and the
nest of an ant colony. Moreover, we carry out microscopic computer simulations
for generalized setups, in which ants have more alternatives or the alternative
ways are of different lengths. In this way and by variation of interaction
parameters, we can get a better idea, how powerful congestion control based on
local repulsive interactions may be. Finally, we will discuss potential
applications of this design principle to routing in traffic or data networks
and machine usage in supply systems.
