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DNA Sequence Optimization Design of Arithmetic Optimization Algorithm Based on Billiard Hitting Strategy.

Linpeng Xie1, Siwei Wang1, Donglin Zhu1

  • 1College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China.

Interdisciplinary Sciences, Computational Life Sciences
|March 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved billiard algorithm to design high-quality DNA sequences for efficient DNA computing. The optimized sequences demonstrate superior performance compared to existing advanced methods.

Keywords:
Billiard hitting improves arithmetic optimizationDNA calculationDNA encodingStochastic lens learningSweet spot set

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Algorithm Optimization

Background:

  • DNA computing offers high computational efficiency but requires precisely designed DNA sequences.
  • Designing DNA sequences that satisfy multiple complex, conflicting constraints is a significant challenge.

Purpose of the Study:

  • To develop an optimized DNA sequence design method for DNA computing.
  • To enhance the efficiency and quality of DNA sequence design using an improved optimization algorithm.

Main Methods:

  • An improved arithmetic optimization algorithm based on the billiard algorithm was developed.
  • Key enhancements include good point set initialization, a billiard hitting strategy, stochastic lens opposition learning, and harmonic search algorithm integration.

Main Results:

  • The proposed algorithm effectively optimizes DNA sequences, meeting stringent design constraints.
  • Comparative experiments using 12 benchmark functions and six other algorithms validated the method's effectiveness.
  • Designed DNA sequences exhibited higher quality than those from other advanced algorithms.

Conclusions:

  • The enhanced billiard algorithm significantly improves DNA sequence design for DNA computing.
  • This approach addresses the challenge of multi-constraint optimization in DNA sequence design.
  • The method offers a promising solution for advancing DNA computing applications.