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Real Time Scheduling Optimization
Abstract
This article deals with real time embedded multiprocessor systems scheduling optimization using conventional and quantum inspired genetic algorithms. Real time scheduling problems are known to be NP-hard. In order to resolve it, researchers have resorted to meta-heuristics instead of exact methods. Genetic algorithms seem to be a good choice to solve complex, non-linear, multi-objective and multi-modal problems. However, conventional genetic algorithms may consume much time to find good solutions. For this reason, to minimize the mean response time and the number of tasks missing their deadlines using quantum inspired genetic algorithms for multiprocessors architectures. Our proposed approach takes advantage of both static and dynamic preemptive scheduling. This article has the developed algorithms on a typical example showing a big improvement in research time of good solutions in quantum genetic algorithms with comparison to conventional ones.
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