Solving the task scheduling problem using a parallel genetic algorithm implemented with GRADE
AbstractIn this paper, we present a task-scheduling heuristic, based on parallel genetic algorithm (PGA). The algorithm schedules parallel programs, represented as directed acyclic graphs (DAGs), onto multi-processor systems with dynamic interconnection networks (DINs) taking into account inter-processor communication cost, link contention and changes of inter-processor connections. The proposed solution combines two methods: list scheduling which is used for constructing schedules and genetic algorithms which drives exploration of the search space for the list algorithm. The parallel genetic algorithm has been implemented on a cluster of workstations in the GRADE environment.
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How to Cite
Kalinowski, T. (2012). Solving the task scheduling problem using a parallel genetic algorithm implemented with GRADE. COMPUTING AND INFORMATICS, 17(5), 495–506. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/625