  Ant Colony System (ACS) is a distributed (agent- based) algorithm which has
been widely studied on the Symmetric Travelling Salesman Problem (TSP). The
optimum parameters for this algorithm have to be found by trial and error. We
use a Particle Swarm Optimization algorithm (PSO) to optimize the ACS
parameters working in a designed subset of TSP instances. First goal is to
perform the hybrid PSO-ACS algorithm on a single instance to find the optimum
parameters and optimum solutions for the instance. Second goal is to analyze
those sets of optimum parameters, in relation to instance characteristics.
Computational results have shown good quality solutions for single instances
though with high computational times, and that there may be sets of parameters
that work optimally for a majority of instances.
