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

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.