Evolutionary Neural Network Based on Quantum Arithmetic Optimization Algorithm for Adaptive Anti-jamming Decision

Due to the broadcasting characteristics of wireless channels, jamming poses a serious threat to wireless communications. In order to prevent jamming from hindering communication, this paper considers the use of evolutionary neural network based on quantum arithmetic optimization algorithm to make adaptive anti-jamming decision. In this paper, we proposed a new adaptive anti-jamming decision-making method based on quantum arithmetic optimization algorithm (QAOA) evolutionary neural network. In order to solve the hyperparameter setting problem of DNN, we designed a quantum arithmetic optimization algorithm (QAOA) based on arithmetic optimization algorithm (AOA) and quantum computing mechanism to optimize the hyperparameters of DNN so that the designed DNN have better convergence and results than the DNN obtained by trial by error under the same number of training. The experimental results show that the QAOA-DNN we designed can quickly select the optimal anti-jamming communication scheme for different communication environments. Compared with other optimization algorithms, the QAOA designed in this paper has faster convergence speed and higher convergence accuracy.