To date, power electronics parameter design tasks are usually tackled using detailed optimization approaches with detailed simulations or using brute force grid search grid search with very fast simulations. A new method, named "Continuously Adapting Random Sampling" (CARS) is proposed, which provides a continuous method in between. This allows for very fast, and / or large amounts of simulations, but increasingly focuses on the most promising parameter ranges. Inspirations are drawn from multi-armed bandit research and lead to prioritized sampling of sub-domains in one high-dimensional parameter tensor. Performance has been evaluated on three exemplary power electronic use-cases, where resulting designs appear competitive to genetic algorithms, but additionally allow for highly parallelizable simulation, as well as continuous progression between explorative and exploitative settings.

翻译：目前，电力电子参数设计任务通常采用基于详细仿真的精细优化方法，或基于极快仿真的暴力网格搜索。本文提出一种名为"连续自适应随机采样"（CARS）的新方法，作为介于两者之间的连续化解决方案。该方法既能实现极快速和/或大规模仿真，又能逐步聚焦于最具潜力的参数区间。其灵感源于多臂老虎机研究领域，由此实现对高维参数张量子域的优先采样。基于三个典型电力电子应用案例的性能评估表明，该方法所得设计方案可与遗传算法相媲美，同时具备高度可并行化仿真能力，并能在探索性与利用性设置之间实现连续渐进过渡。