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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.
