An Efficient Optimization Method for Helix Slow Wave Structure Based on Surrogate Models

The helix slow wave structure (SWS) is the most popular SWS in traveling wave tubes (TWT) for its high interaction impedance and wide bandwidth. The cold-test performance of the helix significantly influences the overall efficiency of the TWT. Currently, the optimization of SWSs primarily relies on human expertise and electromagnetic simulations, a process that is often labor-intensive and challenging to achieve a global optimum. To address this issue, this paper introduces a multi-objective optimization model based on an adaptive surrogate model. The proposed approach employs support vector regression (SVR) as the surrogate model and the fast non-dominated sorting differential evolution algorithm as the optimization strategy. It simultaneously optimizes the average interaction impedance, phase velocity, and dispersion characteristics of the helix, offering a time-efficient and high-accuracy alternative to full-wave electromagnetic simulations. To improve accuracy, the average interaction impedance in the electron beam region is used as the optimization objective instead of the central interaction impedance. The effectiveness of a specific helix SWS is investigated. The results clearly validate the effectiveness and advantages of the proposed approach.