DNA computing is a very efficient way to calculate, but it relies on high-quality DNA sequences, but it is difficult to design high-quality DNA sequences. The sequence it is looking for must meet multiple conflicting constraints at the same time to meet the requirements of DNA calculation. Therefore, we propose an improved arithmetic optimization algorithm of billiard algorithm to optimize the DNA sequence. This paper contributes as follows. The introduction to the good point set initialization to obtain high-quality solutions improves the optimization efficiency. The billiard hitting strategy was used to change the position of the population to enhance the global search scope. The use of a stochastic lens opposites learning mechanism can increase the capacity of the algorithm to get rid of locally optimal. The harmonic search algorithm is introduced to clarify some unqualified secondary structures and improve the quality of the solution. 12 benchmark functions and six other algorithms are used for comparison and ablation experiments to ensure the effectiveness of the algorithms. Finally, the DNA sequences we designed are of higher quality compared to other advanced algorithms.
Keywords: Billiard hitting improves arithmetic optimization; DNA calculation; DNA encoding; Stochastic lens learning; Sweet spot set.
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