dna storage, an innovative technology to data preservation, has garnered significant attention due to its remarkable data density, longevity, and energy efficiency. Errors are common during the sequencing and synthesi...
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ISBN:
(纸本)9789819756919;9789819756926
dna storage, an innovative technology to data preservation, has garnered significant attention due to its remarkable data density, longevity, and energy efficiency. Errors are common during the sequencing and synthesis of dna stores, for which combinatorial constraints such as storage Hamming distance, GC content, and no-travel length are proposed. Metaheuristics, known for their rapid convergence to the optimal solutions, are particularly well-suited for addressing such intricate combinatorial optimization problems. Artificial gorilla troop optimizer (GTO) is prone to local optimum and slow convergence, to overcome this limitation, this paper employs random opposition-based learning, Levy flights and elite group genetic strategies to enhance it, and proposes an enhanced GTO (LOGGTO), which is validated by CEC2017 functions. Then LOGGTO is applied to construct a collection of dna encodings that adhere to the combinatorial constraints. Experimental outcomes reveal that the refined algorithm delivers more stable and reliable dnacoding sequences compared to those obtained in previous investigations.
dna computing is an emerging computational model that has garnered significant attention due to its distinctive advantages at the molecular biological level. Since it was introduced by Adelman in 1994, this field has ...
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dna computing is an emerging computational model that has garnered significant attention due to its distinctive advantages at the molecular biological level. Since it was introduced by Adelman in 1994, this field has made remarkable progress in solving NP-complete problems, enhancing information security, encrypting images, controlling diseases, and advancing nanotechnology. A key challenge in dna computing is the design of dnacoding, which aims to minimize nonspecific hybridization and enhance computational reliability. The dna coding design is a classical combinatorial optimization problem focused on generating high-quality dna sequences that meet specific constraints, including distance, thermodynamics, secondary structure, and sequence requirements. This paper comprehensively examines the advances in dna coding design, highlighting mathematical models, counting theory, and commonly used dnacoding methods. These methods include the template method, multi-objective evolutionary methods, and implicit enumeration techniques.
With the development of information technology, huge amounts of data are produced at the same time. How to store data efficiently and at low cost has become an urgent problem. dna is a high-density and persistent medi...
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With the development of information technology, huge amounts of data are produced at the same time. How to store data efficiently and at low cost has become an urgent problem. dna is a high-density and persistent medium, making dna storage a viable solution. In a dna data storage system, the first consideration is how to encode the data effectively into code words. However, dna strands are prone to non-specific hybridization during the hybridization reaction process and are prone to errors during synthesis and sequencing. In order to reduce the error rate, a thermodynamic minimum free energy (MFE) constraint is proposed and applied to the construction of coding sets for dna storage. The Brownian multi-verse optimizer (BMVO) algorithm, based on the Multi-verse optimizer (MVO) algorithm, incorporates the idea of Brownian motion and Nelder-Mead method, and it is used to design a better dna storage coding set. In addition, compared with previous works, the coding set has been increasing by 4%-50% in size and has better thermodynamic properties. With the improvement of the quality of the dnacoding set, the accuracy of reading and writing and the robustness of the dna storage system are also enhanced.
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