On Performance Analysis of Hybrid Intelligent Algorithms (Improved PSO with SA and Improved PSO with AIS) with GA, PSO for Multiprocessor Job Scheduling

Many heuristic-based approaches have been applied to finding schedules that minimize the execution time of computing tasks on parallel processors. Particle Swarm Optimization is currently employed in several optimization and search problems due its ease and ability to find solutions successfully. A variant of PSO, called as Improved PSO has been developed in this paper and is hybridized with the AIS to achieve better solutions. This approach distinguishes itself from many existing approaches in two aspects In the Particle Swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-scheduling problem. In the Artificial Immune System (AIS), the models of vaccination and receptor editing are designed to improve the immune performance. The proposed hybrid algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The hybrid technique has been employed, inorder to improve the performance of improved PSO. This paper shows the application of hybrid improved PSO in Scheduling multiprocessor tasks. A comparative performance study is discussed for the intelligent hybrid algorithms (ImPSO with SA and ImPSO with AIS). It is observed that the proposed hybrid approach using ImPSO with AIS gives better results than intelligent hybrid algorithm using ImPSO with SA in solving multiprocessor job scheduling. Key-Words: PSO, Improved PSO, Simulated Annealing, Hybrid Improved PSO, Artificial Immune System ( AIS), Job Scheduling, Finishing time, waiting time