数字微流控生物芯片中高效管脚约束液滴路径技术的多目标优化算法

Soumyajit Chatterjee, H. Rahaman, T. Samanta
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引用次数: 5

摘要

数字微流控生物芯片的设计自动化包括许多组合优化问题,这些问题本质上是np完全的。迄今为止,还没有有效的优化算法来解决这些问题。在本文中,我们提出了一种多目标优化算法,该算法可以在数字微流控生物芯片的生物分析操作中同时最小化几种资源。我们将渐进式液滴路径设计为一个约束多目标优化问题,考虑三个目标函数进行优化,分别为(i)电极使用量,(ii)路径完成时间或最后到达时间,以及(iii)控制引脚分配。复合目标函数由前两个目标函数的加权和构成。这个复合函数被最小化到第三个目标函数的上界,即控制引脚分配。一个分数常数权重因子(λ)被选择赋予必要的权重对两个因素所涉及的复合目标函数的精确优化过程。我们对几个现有的基准进行了实验,实验结果非常令人鼓舞。
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Multi-objective optimization algorithm for efficient pin-constrained droplet routing technique in digital microfluidic biochip
Design automation for digital microfluidic biochip comprises of many combinatorial optimization problems, which are NP-complete in nature. Efficient optimization algorithms to solve them is in dearth till date. In this paper, we propose a multi-objective optimization algorithm that simultaneously minimizes several resources during bioassay operations in a digital microfluidic biochip. We design the progressive droplet routing as a constrained multi-objective optimization problem considering three objective functions to be optimized, named (i) electrode usages, (ii) routing completion time, or latest arrival time, and (iii) control pin allocation. A composite objective function is constructed by a weighted sum of the first two objective functions. This composite function is minimized pertaining to an upper bound on the third objective function, control pin allocation. A fractional constant weight factor (λ) is chosen to confer upon the necessary weightage on the two factors involved in the composite objective function for accurate optimization procedure. We perform experimentations with several existing benchmarks, and experimental results are quite encouraging.
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