Wang Zhaocai, Ji Zuwen, Wang Xiaoming, Wu Tunhua, Huang Wei
State Key Laboratory of Simulation and Regulation of River Basin Water Cycle, China Institute of Water Resources and Hydropower Research, Beijing 100048, PR China; College of Information, Shanghai Ocean University, Shanghai 201306, PR China; Department of Computer Science, Peking University, Beijing 100871, PR China.
State Key Laboratory of Simulation and Regulation of River Basin Water Cycle, China Institute of Water Resources and Hydropower Research, Beijing 100048, PR China.
Biosystems. 2017 Dec;162:59-65. doi: 10.1016/j.biosystems.2017.09.001. Epub 2017 Sep 7.
As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n) time complexity.
作为解决计算难题的一种有前途的方法,基于DNA计算的方法是一个新兴的研究领域,涉及数学、计算机科学和分子生物学。任务调度问题是一个著名的NP完全问题,它将n个任务分配给m个人,并找到最后完成任务的人的最短执行时间。在本文中,我们使用一种受生物启发的计算模型,并描述一种新的并行算法,通过基本的DNA分子操作来解决任务调度问题。相应地,我们巧妙地设计了可变长度的DNA链来表示分配矩阵的元素,进行适当的生物实验操作,并在小于O(n)的时间复杂度下,在适当的长度范围内得到任务调度问题的解。