Ant colonies are good at solving constraint satisfaction problems

We define an ant algorithm for solving random binary constraint satisfaction problems (CSPs). We empirically investigate the behavior of the algorithm on this type of problems and establish the parameter settings under which the ant algorithm performs best for a specific class of CSPs. The ant algorithm is compared to six other state-of-the-art stochastic algorithms from the field of evolutionary computing. It turns out that the ant algorithm outperforms all other algorithms and that bivariate distribution algorithms perform worse than the univariate ones, the latter largely due to the fact that they cannot model the randomly generated instances.