The efficiency of DNA computation is closely related to the design of DNA coding sequences. For the purpose of obtaining superior DNA coding sequences, it is necessary to choose suitable DNA constraints to prevent potential conflicting interactions in different DNA sequences and to ensure the reliability of DNA sequences. An improved matrix particle swarm optimization algorithm, referred to as IMPSO, is proposed in this paper to optimize DNA sequence design. In addition, this paper incorporates centroid opposition-based learning to fully preserve population diversity and develops and adapts a dynamic update on the basis of signal-to-noise ratio distance to search for high-quality solutions in a sufficiently intelligent manner. The results show that the proposal of this paper achieves satisfactory results and can obtain higher computational efficiency.
In DNA computing, the design of DNA coding sequences is an important factor affecting the reliability of DNA computing. In different DNA sequence designs, suitable constraints should be selected and the sequence design should be rationalized according to these constraints. In this paper, an improved particle swarm optimization algorithm based on elastic collision strategy (EC-PSO) is used to optimize the design of DNA sequences by using an adaptation function that satisfies multiple constraints. EC-PSO uses the idea of elastic collision to improve the optimal and worst positions within the population, introduces the flight means of the sparrow search algorithm (SSA) to enhance the search capability of the algorithm and increase the diversity of the population; then introduces the harmony search algorithm to the population is then fine-tuned to improve the quality of the solution. The effectiveness of the algorithm was verified by comparing it with the other six algorithms in eight test functions. Finally, the sequence designed was more reasonable in the DNA optimal design experiment.
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