The canonical particle swarm optimization (PSO) is broken down into its essential components, and recombined them in another ways which builds a fast and easy binary discrete PSO. In this algorithm, the probability of a certain particle element assuming a value of 0 or 1 is in positive proportion to value 0 or 1 of this element in the current position of the particle, the historic best position it experienced, and the best point found by any member of its topological neighborhood; but in negative proportion to value of the former position of it. This algorithm doesn’t involve the meaning of velocity which is usually hard to be defined in the discrete PSO. A queen informant is also introduced. It doesn’t increase the number of function evaluations; however, it appears it greatly speeds up the convergence.