聚类分析
计算机科学
模拟退火
余弦相似度
任务(项目管理)
数据挖掘
相似性(几何)
人工智能
机器学习
工程类
图像(数学)
系统工程
作者
Weijian Pang,Hui Li,Peng Li,Hailin Zhang
标识
DOI:10.1109/icus52573.2021.9641293
摘要
The unmanned aerial vehicle (UAV) has huge application potential in disaster rescue, natural detection, and military reconnaissance. In these scenarios, task assignment is essential and facing the problem of NP-hard, and as the scale of the Multi-UAV system increase, it is harder to solve the problem. In this paper, a CSCM-SA (Cosine Similarity Clustering Method- Simulated Annealing) is proposed. Firstly, the cosine similarity clustering method is used for pre-clustering of large-scale targets; Secondly, simulated annealing is employed to optimize routers for each UAV; Finally, to cope with the instability of CSCM results, two strategies of employing genetic algorithm are studied. Simulation results show that the CSCM-SA algorithm is effective to conduct task assignments for the large-scale multi-UAV systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI