Monitoring Scheduling of Drones for Emission Control Areas: An Ant Colony-Based Approach
Monitoring Scheduling of Drones for Emission Control Areas: An Ant Colony-Based Approach
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DOI：
10.1109/tits.2021.3106305
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发表时间：
2022-08
影响因子：
8.5
通讯作者：
Zhao-Hui Sun;Xiaosong Luo;E. Wu;Tian-Yu Zuo;Z. Tang;Zilong Zhuang
中科院分区：
文献类型：
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作者：
Zhao-Hui Sun;Xiaosong Luo;E. Wu;Tian-Yu Zuo;Z. Tang;Zilong Zhuang
无人机由于其高机动性，已成为提高部分排放控制区域船舶排放监测效率的一种有前途的工具。然而，如何优化无人机的飞行路径，以提高被监测船只的加权和，即，无人机调度问题（DSP）是一个尚未得到充分研究的问题。与文献中经典的优化求解方法不同，本文提出了一种高效的基于蚁群算法的DSP优化求解方法。针对DSP的特点，提出了基于层次的信息素更新策略和基于分区的信息素管理机制，对典型蚁群算法进行了优化。数值实验不仅验证了蚁群算法求解DSP问题的可行性，而且表明在不同问题规模下，该算法的解质量和求解速度均优于其他算法。
The drone has become a promising tool to improve the efficiency of vessel emission monitoring in emission control areas of the part due to its high mobility. However, how to optimize the flight path of drones to improve the weighted sum of monitored vessels, i.e., drone scheduling problem (DSP), is a not yet fully researched problem. In this paper, different from the classic optimization solution method used by the literature, an efficient ant colony-based algorithm is developed to solve DSP. Given the characteristics of DSP, a hierarchical-based pheromone update strategy and partition-based pheromone management mechanism are proposed to optimize the typical ant colony algorithm. Numerical experiments not only illustrate the feasibility of using the ant colony algorithm to solve DSP, but also show that the algorithm we proposed outperforms other compared methods in terms of the solution quality and the solving speed under different problem scales.