Optimal design of DNA coding based on multi-strategy JAYA algorithm

DNA computing as a future method of computing, has been widely concerned by many scholars because of its high density, high parallelism and other characteristics, which have led to a significant improvement in the storage capacity and computational speed of DNA computing, and the optimization problem of DNA computing has remained a popular research area until now. This paper proposes an optimisation design for DNA coding based on the improved JAYA algorithm, which incorporates saltation learning, lensing learning and the equalisation of random and Halton sequences to initialise populations, improving the standard JAYA algorithm’s shortcomings of strong randomness, uneven population distribution and insufficient inter-population communication, and cleverly balancing the convergence and population diversity of the algorithm, allowing the JAYA algorithm to be able to find better quality in the process of finding the best. The improved JAYA algorithm is proved to be highly practical through ablation study.