一种求解多处理器作业调度的Hopfield神经网络方法

Scheduling Mutiprocessor Job Using Hopfield Neural Network

多处理器作业调度是一类非常复杂的组合优化问题 ,而Hopfield神经网络通常被广泛用于求解各种组合优化问题。针对具有时间约束 (执行时间和最后执行期限 )和若干资源约束的多处理器作业调度问题 (已知是NP难解的 ) ,提出了一种基于离散的Hopfield神经网络的求解新方法。该方法直接把问题的各种约束表示为Hopfield神经网络的能量函数项 ,进而导出神经网络模型。实验仿真结果表明了该方法的有效性。

Multiprocessor job scheduling is a complicated combinatorial optimization problem, and the Hopfield neural network is extensively applied to solve various combinatorial optimization problems. An effective Hopfield neural network (HNN) approach to multiprocessor job scheduling problem (known to be a NP hard problem)is proposed, which is apt to resource and timing (execution time and deadline) constraints. This approach directly formulates the energy function of HNN according to constraints term by term and derives HNN model. Simulation results demonstrate that the derived energy function works effectively for this class of problems.