  The article presents a study of the Particle Swarm optimization method for
scheduling problem. To improve the method's performance a restriction of
particles' velocity and an evolutionary meta-optimization were realized. The
approach proposed uses the Genetic algorithms for selection of the parameters
of Particle Swarm optimization. Experiments were carried out on test tasks of
the job-shop scheduling problem. This research proves the applicability of the
approach and shows the importance of tuning the behavioral parameters of the
swarm intelligence methods to achieve a high performance.
