  The performance of the meta-heuristic algorithms often depends on their
parameter settings. Appropriate tuning of the underlying parameters can
drastically improve the performance of a meta-heuristic. The Ant Colony
Optimization (ACO), a population based meta-heuristic algorithm inspired by the
foraging behavior of the ants, is no different. Fundamentally, the ACO depends
on the construction of new solutions, variable by variable basis using Gaussian
sampling of the selected variables from an archive of solutions. A
comprehensive performance analysis of the underlying parameters such as:
selection strategy, distance measure metric and pheromone evaporation rate of
the ACO suggests that the Roulette Wheel Selection strategy enhances the
performance of the ACO due to its ability to provide non-uniformity and
adequate diversity in the selection of a solution. On the other hand, the
Squared Euclidean distance-measure metric offers better performance than other
distance-measure metrics. It is observed from the analysis that the ACO is
sensitive towards the evaporation rate. Experimental analysis between classical
ACO and other meta-heuristic suggested that the performance of the well-tuned
ACO surpasses its counterparts.

Neural and Evolutionary Computing