Literature DB >> 28890344

A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

Zhaocai Wang1, Zuwen Ji2, Xiaoming Wang3, Tunhua Wu4, Wei Huang5.   

Abstract

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(n2) time complexity.
Copyright © 2017. Published by Elsevier B.V.

Keywords:  Adleman-Lipton model; NP-complete problem; Parallel DNA computing; The task scheduling problem

Mesh:

Substances:

Year:  2017        PMID: 28890344     DOI: 10.1016/j.biosystems.2017.09.001

Source DB:  PubMed          Journal:  Biosystems        ISSN: 0303-2647            Impact factor:   1.973


  3 in total

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2.  A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model.

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Journal:  PLoS One       Date:  2020-12-02       Impact factor: 3.240

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  3 in total

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