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.
The feasibility of using agent's type in solving a constrained optimization problem for a multi-agent hierarchical system is analyzed. The existence of a threshold for agent's type, as well as certain conditions of its incorporation into system's goal function are established.
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