Abstract
An effective Hopfield neural network (HNN) approach to the multiprocessor job scheduling problem (known to be an NP-hard problem) is proposed in this paper, which is apt to resource and timing (execution time and deadline) constraints. This approach directly formulates the energy function of the HNN according to constraints term by term and derives the HNN model, then embeds simulated annealing into the HNN to prevent local minimum. Simulation results demonstrate that the derived energy function works effectively for this class of problems.
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