In order to construct an exact neural network model for the microstrip line filter, we propose a hybrid algorithm which combines the adaptive genetic algorithm and the improved particle swarm optimization (PSO). In the adaptive genetic algorithm, a quadratic form selection strategy is constructed to improve the replication probability of excellent individuals, which can accelerate the process of convergence to the initial global optimal solution. Taking the advantage of the good local search ability of the PSO, a Gaussian perturbation term is introduced into the position iteration of the standard PSO, which can overcome the shortcomings of slow convergence and premature convergence, and improve the possibility of searching for the global optimal solution. Simulations on the testing functions verify the feasibility of the proposed hybrid algorithm. Finally, the hybrid algorithm is used to optimize the parameters of the neural network, and a parallel coupled microstrip line filter model is established. The results show that the root mean square error of the filter parameters
S21 and S11 are reduced at least by 18.22% and 12.68% respectively, and the modeling accuracy of the microstrip filter is improved, which verifies the validity and reliability of the proposed algorithm.