Continuously Adapting Random Sampling (CARS) for Power Electronics Parameter Design
This addresses parameter design for power electronics engineers, offering a continuous and parallelizable alternative to existing methods, though it appears incremental in nature.
The paper tackles power electronics parameter design by proposing Continuously Adapting Random Sampling (CARS), a method that balances fast simulations with focused sampling on promising ranges, resulting in competitive performance compared to genetic algorithms across three use-cases.
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.