Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

中文翻译：

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通过新的进化算法放置数字微流体生物芯片

数字微流控生物芯片 (DMFB) 已成为具有灵活性和可重构性优势的实验室程序自动化和小型化的革命性平台。布局问题是 DMFB 设计自动化中最具挑战性的问题之一。它包含三个交互的 NP-hard 子问题：资源绑定、操作调度和模块放置。此外，在布局优化过程中，必须满足复杂的约束条件以保证可行的解决方案，例如优先约束、存储约束和资源约束。在本文中，基于进化算法提出了一种新的 DMFB 放置方法，该算法具有新颖的基于启发式的解码策略，用于操作调度和模块放置。具体来说，我们没有使用以前的列表调度器和路径调度器来解码操作调度染色体，而是引入了一种新的启发式调度算法（称为顺序调度器），对操作调度解决方案的搜索空间限制较少。此外，还提出了一种结合调度和布局的新型3D布局器，可以灵活记录芯片中微流控阵列随时间的使用情况，能够代表更可行的模块布局解决方案。与最先进的放置方法（T-tree 和 3D-DDM）相比，实验结果证明了基于多个真实世界生物测定基准的所提出方法的优越性。所提出的方法可以在所有测试用例中以最短的检测完成时间找到最佳结果。