We describe in this paper Ant-solver, a generic constraint solver based on the Ant Colony Optimization meta-heuristic. This approach takes inspiration on the observation of real ants collective foraging behaviour. The idea is to model, in a generic way, any constraint satisfaction problem as the search of a best set of vertices in a graph. Artificial ants walk trough this graph, in a stochastic and incomplete way, searching for good sets of vertices. Artificial ants communicate in a local and indirect way, by laying a pheromone trail on the edges of the graph.

Ant-solver has been designed to solve a general class of combinatorial problems, i.e., constraint satisfaction problems, the goal of which is to find an assignment of values to variables that satisfies constraints. Ant-solver is illustrated on two classical problems~: the n-queens and SAT. We study on SAT the influence of the parameters values
over the resolution process.