Advanced Cognition-Driven EM Optimization Incorporating Transfer Function-Based Feature Surrogate for Microwave Filters
Jing Jin, Feng Feng, Weicong Na, Jianan Zhang, Wei Zhang, Zhao Zhi-hao, Qi‐Jun Zhang
Abstract
This article proposes an advanced cognition-driven electromagnetic (EM) optimization incorporating transfer function-based feature surrogate for EM optimization of microwave filters. The proposed optimization technique addresses the situations where the response of the starting point for design optimization is far away from the design specifications. This article proposes to extract transfer function-based feature parameters for optimization to address the challenge that the features cannot be clearly and explicitly identified from the filter response. Multiple transfer function-based feature parameters are extracted and used to develop the feature surrogate model for the proposed cognition-driven optimization. Furthermore, we derive new objective functions for the cognition-driven optimization directly in the feature space. The proposed cognition-driven optimization incorporating transfer function-based feature surrogate can achieve faster convergence than the existing feature-assisted EM optimization methods. Two examples of EM optimizations of microwave filters are used to demonstrate the proposed technique.