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Anti-aircraft Missile Deployment Optimization Using Hopfield Neural Network

In this paper, we construct a novel HNN energy function, and use the HNN with this energy function to solve anti-aircraft missile deployment, which is a constrained layout problem. A near-optimum solution is obtained when HNN reaches a stable state, i.e, the minimum of the energy function is reached. We have studied the convergence of the network and the relationship between parameters of the network and stability. A group of suitable parameters are obtained by lots of experiences. The simulation results in different scales show that our approach can obtain the near-optimum solution. In addition, our approach can get better solutions in larger scales than other methods such as the divide-and-conquer algorithm which is often used to solve constrained layout problems, and it can also be extended to other constrained layout problems, such as mobile base station planning and integrated circuit layout design.