Abstract:
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 optimisation
algorithms combined with the firefly algorithm (FA) to tune the parameters of
the ACS solving symmetric TSP problems. The FA optimises 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.
