Radio frequency (RF) biosensors, in particular those based on inter-digitated
capacitors (IDCs), are pivotal in areas like biomedical diagnosis, remote
sensing, and wireless communication. Despite their advantages of low cost and
easy fabrication, their sensitivity can be hindered by design imperfections,
environmental factors, and circuit noise. This paper investigates enhancing the
sensitivity of IDC-based RF sensors using novel reinforcement learning based
Binary Particle Swarm Optimization (RLBPSO), and it is compared to Ant Colony
Optimization (ACO), and other state-of-the-art methods. By focusing on
optimizing design parameters like electrode design and finger width, the
proposed study found notable improvements in sensor sensitivity. The proposed
RLBPSO method shows best optimized design for various frequency ranges when
compared to current state-of-the-art methods.

通过增强电极设计和指间电容式无线射频传感器的指标宽度等参数的优化，本研究采用了一种新的基于强化学习的二进制粒子群优化方法（RLBPSO），并与蚁群优化（ACO）和其他最先进的方法进行了对比。结果显示，所提出的 RLBPSO 方法在不同频率范围内显示出最佳的优化设计。