Many-Objective Deployment Optimization for a Drone-Assisted Camera Network

被引：132
作者：
Cao, Bin [1 ,2 ]
Li, Meng [1 ,2 ]
Liu, Xin [3 ]
Zhao, Jianwei [1 ,2 ]
Cao, Wenxi [4 ]
Lv, Zhihan [5 ]
机构：
[1] Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China
[2] Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300401, Peoples R China
[3] Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China
[4] Chinese Acad Sci, South China Sea Inst Oceanol, Guangdong Key Lab Ocean Remote Sensing, Guangzhou 510301, Peoples R China
[5] Qingdao Univ, Sch Data Sci & Software Engn, Qingdao 266071, Peoples R China
来源：
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2021年 / 8卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Cameras; Three-dimensional displays; Optimization; Solid modeling; Visualization; Drones; Gaussian processes; Drone-assisted camera network; Gaussian process regression; many-objective optimization problem; quantization of polynomial mutation operator; ALGORITHM; COVERAGE; MOEA/D;
D O I：
10.1109/TNSE.2021.3057915
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Drone-assisted camera networks can be used in many applications. However, different application requirements lead to different deployment scenarios. In this paper, based on a 3D terrain environment represented by triangular mesh data, a many-objective optimization model for the deployment of multiple onboard cameras is constructed. We propose an improved version of the constrained two-archive evolutionary algorithm. A selection operator based on Gaussian process regression is used for enhancement. Additionally, we quantize the polynomial mutation operator. The improved algorithm is applied to optimize drone-assisted camera deployment, and the experimental results show that the improved algorithm is superior to state-of-the-art algorithms.
引用
收藏
页码：2756 / 2764
页数：9
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