An Auxiliary Classifier Generative Method for Antenna Design and Optimization

Machine learning (ML) methods are widely employed in antenna design and optimization due to their strong performance in multi-dimensional optimization problems and reduced dependence on engineers’ experience. In this work, we propose an auxiliary classifier generative method inspired by the auxiliary classifier generative adversarial networks (AC-GANs) from the computer vision domain, integrated with an evolutionary strategy to facilitate class-based antenna design. By allocating suitable class labels, multiple design requirements can be efficiently met. This method categorizes antenna designs to enable simultaneous optimization of various antenna types within a unified geometric model. Utilizing a discriminator-generator framework typical of GANs, the discriminator predicts class labels from geometric models, while the generator creates new candidates to satisfy these classifications. An additional filter layer, a random forest classifier, refines the generator’s outputs, proving effective in our experiments. The incorporation of evolutionary criteria significantly enhances the design process, mirroring active learning principles to address the challenges of limited and expensive datasets. The final outcomes include both optimized geometric designs and a robust generator capable of producing these designs in large quantities. We substantiate the efficacy and benefits of our proposed method through an analytical case study and a practical dipole antenna example. The comparison between our method and other optimization methods highlights three distinct advantages of the proposed approach: versatility, scalability, and high efficiency.