Optimization of High-Frequency Transmission Line Reflection Wave Compensation and Impedance Matching Based on a DQN-GA Hybrid Algorithm

In high-frequency circuit design, parameters such as the characteristic impedance and propagation constant of transmission lines directly affect key performance metrics, including signal integrity and power transmission efficiency. To address the challenge of optimizing impedance matching for high-frequency PCB transmission lines, this study applies a hybrid deep Q-network—genetic algorithm (DQN-GA) that integrates deep reinforcement learning with a genetic algorithm (GA). Unlike existing methods that primarily focus on predictive modeling or single-algorithm optimization, the proposed approach introduces a bidirectional interaction mechanism for algorithm fusion: transmission line structures learned by the deep Q-network (DQN) are encoded as chromosomes to enhance the diversity of the genetic algorithm population; simultaneously, high-fitness individuals from the genetic algorithm are decoded and stored in the experience replay pool of the DQN to accelerate its convergence. Simulation results demonstrate that the DQN-GA algorithm significantly outperforms both unoptimized structures and standalone GA methods, achieving substantial improvements in fitness scores and S11 transmission coefficients. This algorithm effectively overcomes the limitations of conventional approaches in addressing complex reflected wave compensation problems in high-frequency applications, providing a robust solution for signal integrity optimization in high-speed circuit design. This study not only advances the field of intelligent circuit optimization but also establishes a valuable framework for the application of hybrid algorithms to complex engineering challenges.