Path Planning Based on Clustering and Improved ACO in UAV-assisted Wireless Sensor Network

Yibing Li, Xianzhen Meng, Fang Ye, T. Jiang, Yingsong Li
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引用次数: 4

Abstract

UAVs as mobile nodes have been introduced into wireless sensor network (WSN) to assist information transmission and reduce the burden of communication. To minimize the path cost of UAVs for information transmission, this paper focuses on the path planning of UAVs. A multi-UAVs path planning combined K-means clustering algorithm and improved MAXMIN ant system (MMAS) is proposed. This algorithm apply the K-means clustering algorithm to reduce the problem size and improve the search efficiency of subsequent path planning. By modifying the node search rules and proposing two optimal solution detection rules of the MMAS, the algorithm searching stagnation and failing into local optimal solution are effectively avoided.
基于聚类和改进蚁群算法的无人机辅助无线传感器网络路径规划
无人机作为移动节点被引入到无线传感器网络中,以辅助信息传输,减轻通信负担。为了使无人机的路径成本最小化,本文重点研究了无人机的路径规划问题。提出了一种结合k均值聚类算法和改进的MAXMIN系统(MMAS)的多无人机路径规划方法。该算法采用K-means聚类算法,减小了问题规模,提高了后续路径规划的搜索效率。通过修改节点搜索规则,提出MMAS的两种最优解检测规则,有效地避免了算法搜索停滞和陷入局部最优解的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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