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    This study introduces a DNA computing algorithm to solve the Family Traveling Salesperson Problem (FTSP), an NP-complete problem. The DNA approach offers a powerful parallel processing solution for complex big data challenges.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Computer Science

    Background:

    • The Family Traveling Salesperson Problem (FTSP) is a complex variant of the Traveling Salesperson Problem (TSP).
    • FTSP is classified as a Non-deterministic Polynomial Complete (NP-complete) problem, making it computationally intensive for traditional digital computers.
    • DNA computing offers a powerful parallel processing capability advantageous for NP-complete problems.

    Purpose of the Study:

    • To propose a DNA algorithm for solving the Family Traveling Salesperson Problem (FTSP).
    • To leverage the parallel processing power of DNA computers for NP-complete problems.
    • To demonstrate the potential of DNA computing for addressing complex big data issues.

    Main Methods:

    • Development of a DNA algorithm based on the Adleman-Lipton model.
    • Utilizing basic biological manipulations on DNA molecules to find the minimal loop overhead solution for FTSP.
    • Algorithm complexity analyzed as O(N^2), where N is the number of vertices.

    Main Results:

    • Simulation experiments on benchmark instances showed superior performance of the parallel DNA algorithm compared to traditional computing methods.
    • The detailed deduction of the algorithm process verified its effectiveness.
    • The study confirmed the potential of DNA computing for solving complex big data problems.

    Conclusions:

    • The proposed DNA algorithm effectively solves the Family Traveling Salesperson Problem.
    • DNA computing demonstrates significant potential as a parallel computing method for tackling complex big data challenges.
    • This research validates the efficacy of DNA computing for NP-complete problems.