DNA Encoding Optimisation Based on Thermodynamics

Due to their exceptional programmability, DNA molecules are widely employed in the design of molecular circuits for applications such as DNA computing, DNA storage and cancer diagnosis and treatment. The quality of DNA sequences directly determines the reliability of these molecular circuits. However, existing DNA encoding algorithms suffer from limitations such as reliance on Hamming distance and conflicts among multiple objectives, resulting in insufficient stability of the generated sequences. To address these issues, this paper proposes a thermodynamics‐based multi‐objective evolutionary optimisation algorithm (TEMOA). The core innovations of the proposed algorithm are as follows: First, a thermodynamics‐based DNA encoding modelling strategy (TDEMS) is introduced, which simplifies the encoding process and significantly improves the sequence quality by incorporating thermodynamic stability constraints. Second, two diversity optimisation strategies—the diversity assessment strategy (DAS) and the front equalisation nondominated sorting (FENS) strategy—are designed to enhance the algorithm's global search capability. Finally, a flexible fitness function design is incorporated to accommodate diverse user requirements. Experimental results demonstrate that TEMOA is more effective than state‐of‐the‐art methods on challenging multi‐objective optimisation problems, whereas the DNA sequences generated by TEMOA exhibit greater reliability compared to those produced by traditional DNA encoding algorithms.