Multi-strategy Collaborative Artificial Gorilla Troops Optimizer for DNA Coding Design

DNA storage, an innovative technology to data preservation, has garnered significant attention due to its remarkable data density, longevity, and energy efficiency. Errors are common during the sequencing and synthesis of DNA stores, for which combinatorial constraints such as storage Hamming distance, GC content, and no-travel length are proposed. Metaheuristics, known for their rapid convergence to the optimal solutions, are particularly well-suited for addressing such intricate combinatorial optimization problems. Artificial gorilla troop optimizer (GTO) is prone to local optimum and slow convergence, to overcome this limitation, this paper employs random opposition-based learning, Lévy flights and elite group genetic strategies to enhance it, and proposes an enhanced GTO (LOGGTO), which is validated by CEC2017 functions. Then LOGGTO is applied to construct a collection of DNA encodings that adhere to the combinatorial constraints. Experimental outcomes reveal that the refined algorithm delivers more stable and reliable DNA coding sequences compared to those obtained in previous investigations.