Radiofrequency (RF) cavities hold immense importance in various accelerator applications, but their optimization poses significant challenges due to complex situations involved. In this study, a recently proposed multi-objective optimization algorithm is utilized to optimize the 325 MHz double spoke cavity, which is characterized by 38 geometric parameters and is one of the most complex cavities commonly used in accelerators. The algorithm utilized combines neural network dynamically to speed up convergence of MOGAs, and it is called DNMOGA. Remarkably, when comparing to two manually optimized cavities (MOCs) respectively, DNMOGA consistently produces some cavities that outperform the MOC in all indicators concerned. This result announces the robust generalization capability exhibited by DNMOGA, and further shows the possibility of designing cavities employing the state-of-art optimization algorithms instead of manual optimization processes completely.

中文翻译：

---

使用 DNMOGA 处理超导腔的形状优化

射频 (RF) 腔在各种加速器应用中非常重要，但由于涉及的复杂情况，它们的优化带来了重大挑战。在这项研究中，利用最近提出的多目标优化算法来优化 325 MHz 双辐腔，该腔具有 38 个几何参数，是加速器中常用的最复杂的腔之一。该算法采用动态结合神经网络来加速MOGA的收敛速度，称为DNMOGA。值得注意的是，当分别与两个手动优化型腔 (MOC) 进行比较时，DNMOGA 始终产生一些在所有相关指标上都优于 MOC 的型腔。这一结果宣告了 DNMOGA 所展现出的强大泛化能力，并进一步表明了使用最先进的优化算法而不是完全手动优化过程来设计型腔的可能性。