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Ant colony system (ACS) is a promising approach which has been widely used inproblems such as Travelling Salesman Problems (TSP), Job shop schedulingproblems (JSP) and Quadratic Assignment problems (QAP). In its originalimplementation, parameters of the algorithm were selected by trial and errorapproach. Over the last few years, novel approaches have been proposed onadapting the parameters of ACS in improving its performance. The aim of thispaper is to use a framework introduced for self-tuning optimization algorithmscombined with the firefly algorithm (FA) to tune the parameters of the ACSsolving symmetric TSP problems. The FA optimizes the problem specificparameters of ACS while the parameters of the FA are tuned by the selectedframework itself. With this approach, the user neither has to work with theparameters of ACS nor the parameters of FA. Using common symmetric TSP problemswe demonstrate that the framework fits well for the ACS. A detailed statisticalanalysis further verifies the goodness of the new ACS over the existing ACS andalso of the other techniques used to tune the parameters of ACS.

A self-tuning Firefly algorithm to tune the parameters of Ant Colony System (ACSFA)