A New Improved Particle Swarm Optimization Algorithm for Multiprocessor Job Scheduling
Job Scheduling in a Multiprocessor architecture is an
extremely difficult NP hard problem, because it requires
a large combinatorial search space and also precedence
constraints between the processes. For the effective
utilization of multiprocessor system, efficient assignment
and scheduling of jobs is more important. This paper
proposes a new improved Particle Swarm Optimization
(ImPSO) algorithm for the job scheduling in
multiprocessor architecture in order to reduce the
waiting time and finishing time of the process under
consideration. In the Improved PSO, the movement of a
particle is governed by three behaviors, namely, inertia,
cognitive, and social. The cognitive behavior helps the
particle to remember its previous visited best position.
This paper proposes to split the cognitive behavior into
two sections .This modification helps the particle to
search the target very effectively. The proposed ImPSO
algorithm is discussed in detail and results are shown
considering different number of processes and also the
performance results are compared with the conventional
techniques such as longest processing time, shortest
processing time and Particle Swarm Optimization.
Keywords: Multiprocessor job scheduling, finishing time, waiting time, PSO, Improved PSO (ImPSO)
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