The effectiveness of dynamic ant colony tuning
Adrian A. de Freitas, Christopher B. Mayer · 2007
We examine the Genetically Modified Ant Colony System (GMACS) algorithm [3], which claims to dynamically tune an Ant Colony Optimization (ACO) algorithm to its near-optimal parameters. While our research indicates that the use of GMACS does result in higher quality solutions over a hand-tuned ACO algorithm, we found that the algorithm is ultimately hindered by its emphasis on randomized ant breeding. Specifically, our investigation shows that tuning ACO parameters on a single colony using a genetic algorithm, as done by GMACS, is not as effective as it may first appear and has several drawbacks.