Digital Microfluidic Biochips Test Path Planning Based on Swarm Intelligence Optimization and Internet of Things Technology

Digital microfluidic biochips (DMFBs), by precisely controlling and manipulating minute fluids, have realized the integration, automation, and cost-effectiveness of biochemical experiments and are applied in various fields such as medical diagnostics, drug development, and environmental monitoring. However, due to the composition of microelectronic component arrays, DMFBs are prone to electrode faults, leading to erroneous biochemical operations and, consequently, inaccurate experimental results. In this paper, a test path optimization algorithm combining an improved grey wolf algorithm and priority strategy is proposed to solve the problem of an extended test droplet path when DMFB tests faulty electrodes. By encoding the priority of the electrodes of the DMFB and the paths between the electrodes, the test droplet routing is performed according to the priority order. The priority coefficients are dynamically adjusted using the improved grey wolf algorithm to shorten the testing path length. Experimental results demonstrate that the proposed path optimization algorithm reduces the path length by 0.45% to 2.08% compared to the Eulerian circuit method in offline testing, achieving the theoretical optimum value, and by 4.90% to 10.53% compared to the ant colony algorithm in online testing. This provides an essential foundation for accelerating the safe and reliable development of DMFBs in healthcare.