<b>Data and additional materials for the paper "</b><b>A Geese-Inspired Energy-Aware Harmonization Algorithm for UAV Swarms</b><b>"</b>

<b>Purpose. </b>This study develops and evaluates a geese-inspired energy harmonization and leader rotation algorithm for UAV swarms, optimizing energy consumption and enhancing swarm coordination to extend mission endurance and improve operational efficiency. <b>Design/methodology/approach. </b>The research methodology involves a quantitative approach with controlled indoor and outdoor experiments to test the proposed algorithm, employing pre-test and post-test analysis, parallel form reliability testing, and statistical evaluations to assess battery consumption, energy harmonization effectiveness, leader rotation performance, and mission success. <b>Findings. </b>The study findings demonstrated that the proposed algorithm effectively balanced energy consumption across UAV swarms, improved leader-follower rotation efficiency, and extended mission endurance, with the algorithm showing significant performance improvements in both indoor and outdoor environments. <b>Originality. </b>This study introduces a geese-inspired energy harmonization and leader rotation algorithm that dynamically rotates leadership roles based on real-time battery levels, enhancing swarm coordination and endurance. <b>Research limitations/implications. </b>The research limitations are the dependence on controlled indoor and outdoor environments, which may not fully capture the complexities and challenges of real-world dynamic conditions, and the need for further validation with larger UAV swarms and extended operational scenarios. <b>Practical implications. </b>The practical implications of this research include the potential for enhancing the efficiency and operational range of UAV swarms in mission-critical applications such as search and rescue, environmental monitoring, and surveillance, by optimizing energy consumption and improving swarm coordination through the proposed algorithm.**研究目的**：本研究开发并评估了一种受大雁启发的无人机（Unmanned Aerial Vehicle, UAV）集群能量协调与领导者轮换算法，旨在优化无人机集群能耗、提升集群协同能力，以延长任务续航时长并改善作业效率。 **研究设计与方法**：本研究采用定量研究方法，通过受控室内与室外实验对所提出的算法进行测试，并运用前测-后测分析、平行形式信度检验以及统计评估手段，分别从电池能耗、能量协调效果、领导者轮换性能以及任务成功率四个维度进行评估。 **研究发现**：本研究结果表明，所提算法可有效平衡无人机集群的能耗分布，提升领导者-跟随者轮换效率并延长任务续航时长，且该算法在室内与室外环境中均展现出显著的性能提升。 **研究创新性**：本研究提出了一种受大雁启发的能量协调与领导者轮换算法，该算法可基于实时电池电量动态轮换领导角色，从而强化集群协同能力并提升续航时长。 **研究局限与启示**：本研究存在以下局限：一是实验依赖受控的室内与室外环境，无法完全复刻真实世界动态场景中的复杂性与挑战；二是需针对更大规模的无人机集群与更长时长的作业场景开展进一步验证。 **实践启示**：本研究的实践价值在于，通过所提算法优化能耗并提升集群协同能力，可在搜救、环境监测、监视等任务关键型应用场景中，有效提升无人机集群的作业效率与作战航程。