An Efficient Transfer Learning Assisted Global Optimization Scheme for Analog/RF Circuits

Online surrogate model-assisted evolution algorithms (SAEAs) are very efficient for analog/RF circuit optimization. To improve modeling accuracy/sizing results, we propose an efficient transfer learning-assisted global optimization (TLAGO) scheme that can transfer useful knowledge between neural networks to improve modeling accuracy in SAEAs. The novelty mainly relies on a novel transfer learning scheme, including a modeling strategy and novel adaptive transfer learning network, for high-accuracy modeling, and greedy strategy for balancing exploration and exploitation. With lower optimization time, TLAGO can have a faster rate of convergence and more than 8% better performances than GASPAD.