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    Area of Science:

    • Biotechnology and Information Technology
    • Computational Science

    Background:

    • DNA computing offers high parallelism, data storage, and low power consumption.
    • It has emerged as a significant research field for solving complex problems.

    Purpose of the Study:

    • To demonstrate the feasibility of the Adleman-Lipton model for DNA computing.
    • To apply DNA computing to solve the Prize Collecting Traveling Salesman Problem (PCTSP).

    Main Methods:

    • Utilized the Adleman-Lipton model for DNA computing.
    • Designed and executed a simulation experiment to solve PCTSP instances.

    Main Results:

    • The DNA computing model successfully solved the tested PCTSP instances.
    • Optimal solutions from DNA computing significantly outperformed the Clustering Search and GSP/VNS algorithms.

    Conclusions:

    • The Adleman-Lipton model is a viable method for solving PCTSP using DNA computing.
    • This research presents a powerful approach for tackling combinatorial optimization problems.