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Parallelizing Assignment Problem with DNA Strands.

Babak Khorsand1, Abdorreza Savadi1, Mahmoud Naghibzadeh1

  • 1Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

Iranian Journal of Biotechnology
|September 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel DNA computing algorithm to solve the NP-hard assignment problem efficiently. The parallel DNA algorithm achieves a linear time complexity, offering a significant advancement for complex optimization tasks.

Keywords:
Adelman Lipton modelAssignmentDNA algorithmDNA computingMolecular computation

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Area of Science:

  • Biomolecular computing
  • Computational complexity theory
  • Optimization algorithms

Background:

  • Many combinatorial optimization problems are NP-hard, requiring exponential time for exact solutions.
  • Parallel computing offers potential but often requires massive, impractical resources.
  • Existing methods for NP-hard problems face significant computational challenges.

Purpose of the Study:

  • To leverage DNA computing for solving NP-hard problems in polynomial time.
  • To address the assignment problem, a classic NP-hard problem involving optimal job-person allocation.
  • To develop an efficient parallel algorithm for the assignment problem using DNA molecular operations.

Main Methods:

  • Utilized the Adelman-Lipton model for DNA computing.
  • Developed a novel parallel DNA algorithm specifically for the assignment problem.
  • Employed biomolecular operations to execute the computational steps.

Main Results:

  • The proposed DNA algorithm solves the assignment problem in linear time complexity.
  • Requires only O(n^2) initial DNA strands, a substantial reduction compared to other methods (n^n).
  • Demonstrates the potential of DNA computing for practical NP-hard problem solving.

Conclusions:

  • A parallel DNA algorithm has been successfully proposed for the assignment problem.
  • The algorithm achieves a linear time solution, outperforming traditional computational approaches.
  • DNA computing offers a viable and efficient paradigm for tackling complex optimization challenges.