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Scheduling multiprocessor job with resource and timing constraints using neural networks

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2 Author(s)
Yueh-Min Huang ; Dept. of Eng. Sci., Nat. Cheng Kung Univ., Tainan, Taiwan ; Ruey-Maw Chen

The Hopfield neural network is extensively applied to obtaining an optimal/feasible solution in many different applications such as the traveling salesman problem (TSP), a typical discrete combinatorial problem. Although providing rapid convergence to the solution, TSP frequently converges to a local minimum. Stochastic simulated annealing is a highly effective means of obtaining an optimal solution capable of preventing the local minimum. This important feature is embedded into a Hopfield neural network to derive a new technique, i.e., mean field annealing. This work applies the Hopfield neural network and the normalized mean field annealing technique, respectively, to resolve a multiprocessor problem (known to be a NP-hard problem) with no process migration, constrained times (execution time and deadline) and limited resources. Simulation results demonstrate that the derived energy function works effectively for this class of problems

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:29 ,  Issue: 4 )