聚类分析
蚁群优化算法
计算机科学
粒子群优化
群体行为
灵敏度(控制系统)
元启发式
多群优化
钥匙(锁)
模块化设计
数学优化
数据挖掘
工程类
算法
机器学习
人工智能
数学
计算机安全
电子工程
操作系统
作者
Yongzhao Yan,Zhenqian Sun,Yueqi Hou,Boyang Zhang,Ziwei Yuan,Guoxin Zhang,Bo Wang,Xiaoping Ma
出处
期刊:Applied sciences
[Multidisciplinary Digital Publishing Institute]
日期:2023-11-17
卷期号:13 (22): 12438-12438
被引量:7
摘要
Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.
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