Multiprocessor scheduling using particle swarm optimization

• Autores: S. N. Sivanandam, P. Visalakshi, A. Bhuvaneswari
• Localización: International journal of the computer, the internet and management, ISSN 0858-7027, Vol. 17, Nº. 3 (SEP-DIC), 2009, págs. 11-24
• Idioma: inglés
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• Resumen

  • 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 and also a parallel version of Particle Swarm Optimization to solve the task assignment problem. 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 parallel version of Particle Swarm Optimization involves data parallelism. The experimental results manifest that among the two proposed methods, the parallel version is effective and efficient in finding near optimal solutions. Keywords: Task assignment problem, Distributed systems, Hybrid strategy, Parallel Strategy, Particle swarm optimization, Simulated Annealing.