An Evolutionary Approach for Job Scheduling in a Multiprocessor Architecture

This paper proposes a genetic algorithm optimization approach to solve job scheduling problems in a multiprocessor architecture. Job scheduling problem (JSP) is an extremely difficult hard problem because it requires a large combinatorial search space and also precedence constraints between the processes considered. Efficient assignment and scheduling of tasks is of high importance in the effective utilization of multiprocessor systems. With regard to this kind of JSP problem, a genetic algorithm based approach is utilized. In this approach, the chromosomes are encoded and the population size is devised for the optimized goal of scheduling problem. Then the fitness function is formulated. Next, utilizing the genetic algorithm operators to prevent the premature convergence, a best or satisfactory scheduling path can be determined. The execution results using the devised algorithms in certain multiprocessor architecture are given in the paper.