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Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks.

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    Controlling virus spread on large networks with limited resources is difficult. A new algorithm, NCD-CEA, uses community detection and coevolution to efficiently solve resource allocation problems.

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    Area of Science:

    • Complex Systems Science
    • Computational Biology
    • Network Science

    Background:

    • Controlling virus spreading over complex networks with limited budgets presents significant challenges.
    • Increasing network sizes necessitate more efficient computational methods for resource allocation problems (RAPs).

    Purpose of the Study:

    • To address combinatorial, discrete resource allocation problems (RAPs) in virus spreading control.
    • To develop an efficient algorithm for large-scale networks.

    Main Methods:

    • Proposed a novel coevolutionary algorithm with network-community-based decomposition (NCD-CEA).
    • Employed a modified community detection method to decompose networks into communities, reducing time complexity.
    • Utilized a cooperative coevolutionary approach with subproblem/subswarm decomposition and alternative evolutionary strategies.

    Main Results:

    • NCD-CEA demonstrated competitive performance in solving resource allocation problems.
    • The algorithm effectively handles increasing network scales and improves solving efficiency.
    • Extensive experiments validated the algorithm's effectiveness across different network types.

    Conclusions:

    • NCD-CEA offers an effective approach for controlling virus spreading over large-scale networks.
    • The proposed method advances the field of network-based virus control strategies.