Digital Microfluidic Droplet Path Planning Based on Improved Genetic Algorithm

In practical applications of droplet actuation using digital microfluidic (DMF) systems based on electrowetting-on-dielectric (EWOD), various electrode failures can still arise due to diverse operational conditions. To improve droplet transport efficiency, this study proposes a heuristic-elite genetic algorithm (HEGA) for droplet path planning. We introduce a heuristic method and a bidirectional elite fragment recombination method to address the challenge of poor initialization quality in genetic algorithms, particularly in complex environments. These approaches aim to enhance the global search capability and accelerate the algorithm’s convergence. Simulations were performed using MATLAB, and the results indicate that compared to the basic ant colony algorithm, the proposed method reduces the average number of turning points by approximately 17.23% and the average search time by about 92.60%. In multi-droplet transport applications, the algorithm generates optimal paths for test droplets while maintaining fast convergence. Additionally, it effectively prevents droplets from accidentally contacting or merging in non-synthesis areas, ensuring improved testing outcomes for the chip.