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Many-Objective Deployment Optimization for a Drone-Assisted Camera Network
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DOI：10.1109/TNSE.2021.3057915.png)
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Abstract
En 中文
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.
Keywords:
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
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