Improving MMAS using parameter control

Tunning parameters values in metaheuristics is a time consuming task. Techniques to control parameters during the execution have been successfully applied into evolutionary algorithms. The key idea is that the algorithm themselves computes its parameters values according to its current state of the search. In this paper, we propose a strategy to include parameters control on ants based algorithms. We have tested our approach to solve hard instances of the travel salesman problem using MMAS. The tests shown that in some cases, it is possible to obtain better results than the reported ones for the same algorithm, by including a parameter control strategy.