Optimization Design of Microwave Filters Based on Deep Learning and Metaheuristic Algorithms

To address the efficiency bottlenecks of traditional full-wave simulation methods in the high-performance design and rapid optimization of microwave filters, this study proposes an efficient design method based on an improved surrogate model and a hybrid optimization algorithm. A one-dimensional dense convolutional autoencoder (1D-DenseCAE) model is constructed to enhance the model’s ability to extract key features and improve convergence speed. Additionally, the Ivy–Hiking optimization algorithm (IHOA) is introduced, combining the advantages of global search and local fine-tuning. Experiments demonstrate that this method achieves approximately a 25% improvement in convergence speed over the standard one-dimensional convolutional autoencoder (1D-CAE) in cavity filter design, and enables efficient optimization in complex structures such as interdigital filters and seventh-order cross-coupled cavity filters, meeting design requirements of return loss below −20 dB and in-band ripple under 0.5 dB. This method provides an effective technical pathway for the intelligent design of microwave filters.