基于自适应果蝇优化算法的加权分簇算法

doi:10.12305/j.issn.1001-506X.2023.07.37

Abstract

Abstract:

To improve the network management of unmanned aerial vehicle formation, the weighted clustering algorithm based on the adaptive fruit fly optimization algorithm is proposed to optimize the network through cluster structure. The proposed algorithm employs a data normalization method based on min-max standardization for each performance index and changes the weight assignment rules according to the overall energy consumption, which jointly improves the objectivity of cluster head election. This paper analyzes the adjustment criterion of undefined nodes, and proposes an adaptive fruit fly optimization algorithm for cluster scale optimization, and then eliminats isolated nodes and small-scale clusters and introduces a residual energy threshold and a safe distance threshold to constrain the maintenance conditions and analyzes the optimal values of the thresholds to reduce the number of cluster maintenance. Simulation results show that the proposed algorithm in this paper can effectively improve the performance of all aspects of the unmanned aerial vehicle formation and obtain better network management results comparing with the existing algorithms.

Key words: weighted clustering algorithm, min-max standardization, adaptive fruit fly optimization algorithm, residual energy threshold, safe distance threshold

CLC Number:

TN929.5

Xiangyu WANG, Yanyu ZHANG, Long LI, Chunxiao JIAN, Weijia CUI. Weighted clustering algorithm based on adaptive fruit fly optimization algorithm[J]. Systems Engineering and Electronics, 2023, 45(7): 2259-2268.

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References 34

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参数名	参数值
无人机数/架	80~200
编队起飞范围/km²	1×1
无人机初始速度/(m/s)	10~12
无人机初始能量	0.95~1.00
无人机一跳通信范围/m	200

Weighted clustering algorithm based on adaptive fruit fly optimization algorithm

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