IEEE Trans Nanobioscience. 2024 Apr;23(2):252-261. doi: 10.1109/TNB.2023.3316431. Epub 2024 Mar 28.
DNA computing is a new computing method that has high efficiency in solving large-scale nonlinear and Non-deterministic Polynomial complete problems. The design of DNA sequences is an important step in DNA computing, and the quality of the DNA sequences directly affects the accuracy of DNA computing results. Efficiently designing high-quality DNA sequences is currently a significant challenge. In order to improve the efficiency of DNA sequence design, a sparrow evolutionary search algorithm (SESA) is proposed by us. It inherits the fast convergence of the sparrow search algorithm and avoids the situation that the sparrow search algorithm is prone to fall into a local optimum, which greatly improves the search performance of the algorithm on discrete numerical problems. In order to improve the quality of DNA sequence, a new constraint, multiple GC constraint, has been proposed in this paper. Simulated experiments in NUPACK show that this constraint can greatly improve the quality of the DNA sequences designed by us. Compared with previous results, our DNA sequences have better stability.
DNA 计算是一种新的计算方法，在解决大规模非线性和非确定性多项式完全问题方面具有高效率。DNA 序列的设计是 DNA 计算中的一个重要步骤，DNA 序列的质量直接影响 DNA 计算结果的准确性。高效设计高质量的 DNA 序列是目前的一个重大挑战。为了提高 DNA 序列设计的效率，我们提出了一种麻雀进化搜索算法（SESA）。它继承了麻雀搜索算法的快速收敛性，避免了麻雀搜索算法容易陷入局部最优的情况，极大地提高了算法在离散数值问题上的搜索性能。为了提高 DNA 序列的质量，本文提出了一种新的约束条件，即多个 GC 约束条件。在 NUPACK 中的模拟实验表明，该约束条件可以大大提高我们设计的 DNA 序列的质量。与之前的结果相比，我们的 DNA 序列具有更好的稳定性。
