Normalization of Aco Algorithm Parameters

Due to the fact that Swarm Systems algorithms have been determined to be efficient in solving discrete optimization problems with proven applicability into practical world, the computer scientists are continuously and increasingly discovering swarm-inspired algorithms or improving existing ones. The scope of this paper is to present such an improvement brought to the Elitist Ant System (EAS) algorithm through the normalization of its parameters’ values. It is important to mention that Normalized EAS (N-EAS) behaves exactly as EAS in terms of solution length and computational time since the algorithm behind is the same and the only difference is the normalization of the parameters. The advantage of N-EAS is that it spares computational time for otherwise running empirical test-runs for determining a good set of parameter values, like in the case of EAS.