Swarm Intelligence Algorithm-Based Optimal Design of Microwave Microfluidic Sensors

Microwave microfluidic sensors have been employed for dielectric characterization of different liquids. Intuitively, the microfluidic channel plays a vital role in determining the sensor performance. In this article, for the first time, numerical optimization design of microfluidic channel route is carried out with the aim of improving the sensor sensitivity. Two swarm intelligence algorithms, i.e., particle-ant colony optimization algorithm and wolf colony algorithm, are implemented for the route optimization. Through the developed optimization procedure, the sensor sensitivity of the original design can be increased significantly. Several prototypes of optimized sensors are fabricated and tested, and they exhibit good capability in retrieving the liquid properties. In comparison with original complementary split-ring resonator-based sensor with a sensitivity of 0.308% for water measurement, the optimized sensor achieves a high sensitivity value of 0.55%, i.e., the sensor sensitivity is increased by 78.6% after optimization. The developed methodology can also be used in other designs, such as series LC-based sensor, whose sensitivity can be improved by about 50%. It is demonstrated that the developed methodology possesses good automatic optimization ability and universality for the optimal design of microwave microfluidic sensors.

Automated summary

This article introduces a numerical optimization design of microfluidic channel route to improve sensor sensitivity, using particle-ant colony optimization algorithm and wolf colony algorithm. The developed methodology significantly increases the sensor sensitivity and shows good universality and automatic optimization ability for microwave microfluidic sensor design.