@inproceedings{39f58bc283b645809579488cf3ac8173,
title = "Research on Adaptive Grouping Method Under Multi-constraints Swarm Confrontation",
abstract = "With the development of intelligent swarm has overturned the combat concept of the traditional air defense system, decomposing large-scale swarm confrontation into small-scale groups confrontation has become a research hotspot in recent years. In this paper, we analyze the grouping constraints of the enemy (Blue Side) and our (Red Side) swarms. In order to obtain the optimal grouping scheme of the enemy swarms, we use k-means algorithm to group the enemy swarms which compared with ISODATA algorithm. Based on the above, we build a consistency function model and propose an improved adaptive genetic algorithm, which improves the rapidity and effectiveness for grouping our swarms. Finally, the grouping algorithm simulation is carried out where the feasibility and reliability of the method is verified. The adaptive grouping method can provide a basis for target assignment.",
keywords = "Clustering algorithm, Genetic algorithm, Grouping method, Swarm confrontation",
author = "Hao Yin and Heng Su and Tianyu Huang and Yue Wang and Dongguang Li",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Autonomous Unmanned Systems, ICAUS 2021 ; Conference date: 24-09-2021 Through 26-09-2021",
year = "2022",
doi = "10.1007/978-981-16-9492-9_277",
language = "English",
isbn = "9789811694912",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "2823--2833",
editor = "Meiping Wu and Yifeng Niu and Mancang Gu and Jin Cheng",
booktitle = "Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021",
address = "Germany",
}