<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.