The Construction of DNA Coding Sets by an Intelligent Optimization Algorithm: TMOL-TSO

DNA computing has a natural advantage in solving NP-complete problems due to its high concurrency and low energy consumption. With the development of DNA computing, the preliminary formation of DNA logic circuit architectures further illustrates the potential of this field. Additionally, DNA holds great potential for storage due to its high density, large capacity, and long-term stability. It is suitable for database construction and data storage. However, non-specific hybridization of DNA molecules may cause unexpected outcomes during computation or storage processes. Therefore, it is crucial to apply pressure constraints to DNA coding to ensure stability. Designing a DNA coding set that satisfies constraints is a primary challenge. In this paper, we propose a Tuna Swarm Optimization (TSO) algorithm that employs random opposition-based learning strategy and Two-Swarm Merge strategy. This algorithm has stronger global exploration capabilities. Experimental results demonstrate that this algorithm can find a better coding set in some cases.