  Ant colony system (ACS) is a promising approach which has been widely used in
problems such as Travelling Salesman Problems (TSP), Job shop scheduling
problems (JSP) and Quadratic Assignment problems (QAP). In its original
implementation, parameters of the algorithm were selected by trial and error
approach. Over the last few years, novel approaches have been proposed on
adapting the parameters of ACS in improving its performance. The aim of this
paper is to use a framework introduced for self-tuning optimization algorithms
combined with the firefly algorithm (FA) to tune the parameters of the ACS
solving symmetric TSP problems. The FA optimizes the problem specific
parameters of ACS while the parameters of the FA are tuned by the selected
framework itself. With this approach, the user neither has to work with the
parameters of ACS nor the parameters of FA. Using common symmetric TSP problems
we demonstrate that the framework fits well for the ACS. A detailed statistical
analysis further verifies the goodness of the new ACS over the existing ACS and
also of the other techniques used to tune the parameters of ACS.
