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Title:
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GENETIC ALGORITHM FOR MULTIPROCESSOR TASK SCHEDULING
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Author:
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Ritu Verma , Sunita Dhingra
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Keywords:
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Genetic Algorithm (GA), crossover, mutation, Multiprocessor task scheduling (MPTS), permutation flow shop scheduling
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Abstract:
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Multiprocessor task scheduling (MPTS) is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running real-time applications especially due to limitation of uni-processor system for not having sufficient enough capability to execute all the tasks. This paper describes multiprocessor task scheduling in the form of permutation flow shop scheduling, which has an objective function for minimizing the makespan. Here, we will conclude how the performance of genetic algorithms (value of the makespan of the schedule) varies with the variation of Genetic Algorithm (GA) control parameters (population size, crossover probability and mutation probability).
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