Tasks Scheduling Through Hybrid Genetic Algorithm in Real-Time System on Heterogeneous Environment

The tasks scheduling issue on parallel processors in real-time system is part of the NP-hard issues. This manuscript developed a model for solving the tasks scheduling issue in heterogeneous multiprocessor environment. In this model, we developed two hybrid genetic algorithms; first algorithm named as HHCGA is the union of hierarchical clustering and genetic algorithm, used for making the tasks clusters of to decrease inter-tasks communication cost; second algorithm named as HHAGA is the union of heuristic approach and genetic algorithm, used for scheduling the tasks clusters onto processors to decrease system cost. The developed model has multiple objectives such as minimize the response time, system cost and maximize system reliability simultaneously. The efficacy of the developed model has been shown via simulation studies. The results of the developed model are likened than that of some other models in simulation studies. This model is appropriate for both types fuzzy cost or crisp cost and it also worked very well for random number of processors and tasks.