A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems

In a distributed system, a number of application tasks may need to be assigned to different processors such that the system cost is minimized and the constraints with limited resource are satisfied. Most of the existing formulations for this problem have been found to be NP-complete, and thus finding the exact solutions is computationally intractable for large-scaled problems. This paper presents a hybrid particle swarm optimization algorithm for finding the near optimal task assignment with reasonable time. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also, our method converges at a fast rate and is suited to large-scaled task assignment problems.