Swarm intelligence on the binary constraint satisfaction problem

We introduce a discrete particle swarm (PS) algorithm for solving binary constraint satisfaction problems (CSPs). It uses information about the conflicts between the variables to compute the velocity of the individual particles. We tune the parameters of the PS algorithm to a quasi-optimal setting and study the behavior of the algorithm under changes to this setting. The PS algorithm is then empirically compared with ant colonies (which also belong to the swarm intelligence class) and genetic algorithms on a whole range of randomly generated binary CSP instances.