The data transmission resource scheduling problem (DTRSP) is pivotal for effectively utilizing remotely sensed Big Data. However, existing literature tends to address this challenge by separately considering ground-based or space-based antenna resources. This article introduces a paradigm, known as the space-ground integrated DTRSP (SGI-DTRSP), wherein both ground-based and space-based antennas are collaboratively scheduled for efficient data transmission back to the ground. For the first time, a mathematical integer programming model for addressing SGI-DTRSP is proposed. To tackle this problem efficiently, we present a distributed discrete Jaya (DD-Jaya) algorithm. In DD-Jaya, the entire population is divided into three subpopulations, each evolving independently through different global search strategies'—best-Jaya or random-Jaya. Additionally, an adaptive local search strategy is incorporated into DD-Jaya to enhance exploitation capacity. The quality of individuals is further improved by sharing superior evolutionary information among subpopulations. Finally, extensive simulation experiments demonstrate that DD-Jaya outperforms the existing algorithms, especially when dealing with large-scale SGI-DTRSP.


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    Titel :

    A Distributed Discrete Jaya Algorithm for Space-Ground Integrated Data Transmission Resource Scheduling


    Beteiligte:
    Ren, Sida (Autor:in) / Feng, Yanxiang (Autor:in) / Li, Xiaoling (Autor:in) / Xing, Keyi (Autor:in) / Yang, Yikang (Autor:in)


    Erscheinungsdatum :

    01.04.2025


    Format / Umfang :

    4124411 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch







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