Handling shape optimization of superconducting cavities with DNMOGA

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 multiobjective 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.