Multiprocessor Scheduling Using Hybrid Particle Swarm Optimization with Dynamically Varying Inertia

The problem of task assignment in heterogeneous computing systems has been studied for many years with many variations. We have developed a new hybrid approximation algorithm. The proposed hybrid heuristic model involves Particle Swarm Optimization (PSO) Algorithm and Simulated Annealing (SA) algorithm. This PSO/SA performs static allocation of tasks in a heterogeneous distributed computing system in a manner that is designed to minimize the cost. Particle Swarm Optimization with dynamically reducing inertia is implemented which yields better result than fixed inertia. The experimental results manifest that the proposed hybrid method is effective and efficient in finding near optimal solutions.

• Type: Journal
• Year: 2007
• Where: IJCSA
• Authors: S. N. Sivanandam, P. Visalakshi, A. Bhuvaneswari
• Keywords: Heterogeneous Computing Systems, Hybrid Approximation Algorithm, IJCSA 2007, Particle Swarm Optimization