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

摘要/Abstract

摘要：

针对无人机编队网络管理问题, 提出了一种基于自适应果蝇优化算法的加权分簇算法, 利用分簇结构进行网络优化。该算法使用了基于离差标准化的数据归一化方法对各性能指标进行处理, 并根据整体能耗改变权值分配规则, 共同提高了簇头选举的客观性; 分析了未定节点调整准则, 提出了应用自适应果蝇优化算法进行簇的规模优化, 消除了孤立节点和小规模簇; 引入了剩余能量阈值和安全距离阈值约束维护条件，并分析了阈值的最优取值, 减少了簇的维护次数。仿真结果表明, 所提算法能够有效提高无人机编队各方面的性能, 与现有算法相比, 能够获得更好的网络管理效果。

关键词: 加权分簇算法, 离差标准化, 自适应果蝇优化算法, 剩余能量阈值, 安全距离阈值

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

中图分类号:

TN929.5

王翔宇, 张艳语, 李龙, 菅春晓, 崔维嘉. 基于自适应果蝇优化算法的加权分簇算法[J]. 系统工程与电子技术, 2023, 45(7): 2259-2268.

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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参考文献 34

1	ARUNA O, SHARMA A. An adaptive and opportunistic based routing protocol in flying ad hoc networks (fanets): a survey[C]//Proc. of the International Conference on Computer Networks, Big Data and IoT, 2019: 119-127.
2	SUN Z , WANG P , VURAN M C , et al. Border-sense: border patrol through advanced wireless sensor networks[J]. Ad Hoc Networks, 2011, 9 (3): 468- 477. doi: 10.1016/j.adhoc.2010.09.008
3	BARRADO C , MESSEGUER R , LOPEZ J , et al. Wildfire monitoring using a mixed air-ground mobile network[J]. IEEE Pervasive Computing, 2010, 9 (4): 24- 32. doi: 10.1109/MPRV.2010.54
4	XIANG H T , TIAN L . Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)[J]. Bio-Systems Engineering, 2011, 108 (2): 174- 190.
5	YANG J , YOU X H , WU G X , et al. Application of reinforcement learning in UAV cluster task scheduling[J]. Future Gene-ration Computer Systems, 2019, 95, 140- 148. doi: 10.1016/j.future.2018.11.014
6	YASSEIN M B , ALHUDA N . Flying ad-hoc networks: routing protocols, mobility models, issues[J]. International Journal of Advanced Computer Science and Applications (IJACSA), 2016, 7 (6): 162- 168.
7	张鸿运, 王磊, 张旭, 等. 考虑子系统执行能力的多无人机协同任务规划[J]. 系统工程与电子技术, 2023, 45 (1): 127- 138.
	ZHANG H Y , WANG L , ZHANG X , et al. Multi-UAV coope-rative mission planning considering subsystem execution capability[J]. Sustems Engioneering and Electronics, 2023, 45 (1): 127- 138.
8	BEKMEZCI I , SAHINGOZ O K , TEMELŞ . Flying ad-hoc networks (FANETs): a survey[J]. Ad Hoc Networks, 2013, 11 (3): 1254- 1270. doi: 10.1016/j.adhoc.2012.12.004
9	OUBBATI O S , ATIQUZZAMAN M , LORENZ P , et al. Routing in flying ad hoc networks: survey, constraints, and future challenge perspectives[J]. IEEE Access, 2019, 7, 81057- 81105. doi: 10.1109/ACCESS.2019.2923840
10	BEKMEZCI I , ŞENTURK E , TURKER T . Security issues in flying ad-hoc networks (FANETS)[J]. Journal of Aeronautics and Space Technologies, 2016, 9 (2): 13- 21.
11	CHLAMTAC I , CONTI M , LIU J J N . Mobile ad hoc networking: imperatives and challenges[J]. Ad Hoc Networks, 2003, 1 (1): 13- 64. doi: 10.1016/S1570-8705(03)00013-1
12	洪洁. 高动态飞行器自组织网络关键技术研究[D]. 北京: 中国科学院大学(中国科学院国家空间科学中心), 2019.
	HONG J. Research on key technologies of highly dynamic flying ad hoc networks[D]. Beijing: University of Chinese Academy of Sciences (National Space Science Center of Chinese Academy of Sciences), 2019.
13	李磊, 王彤, 蒋琪. 从美军2042年无人系统路线图看无人系统关键技术发展动向[J]. 无人系统技术, 2018, (4): 79- 84.
	LI L , WANG T , JIANG Q . Key technology develop trends of unmanned systems viewed from unmanned systems integrated roadmap 2017-2042[J]. Unmanned Systems Technology, 2018, (4): 79- 84.
14	WU J , ZOU L K , ZHAO L , et al. A multi-UAV clustering strategy for reducing insecure communication range[J]. Computer Networks, 2019, 158, 132- 142. doi: 10.1016/j.comnet.2019.04.028
15	ROSATI S, KRUZELECKI K, TRAYNARD L, et al. Speed-aware routing for UAV ad-hoc networks[C]//Proc. of the IEEE Globecom Workshops, 2013: 1367-1373.
16	BENASHER Y , FELDMAN S , GURFIL P , et al. Distributed decision and control for cooperative UAVs using ad hoc communication[J]. IEEE Trans.on Control Systems Technology, 2008, 16 (3): 511- 516. doi: 10.1109/TCST.2007.906314
17	LIN C R, GERLA M. A distributed architecture for multimedia in dynamic wireless networks[C]//Proc. of the IEEE GLOBECOM, 1995: 1468-1472.
18	EPHREMIDES A , WIESELTHIER J E , BAKER D J . A design concept for reliable mobile radio networks with frequency hopping signaling[J]. Proceedings of the IEEE, 1987, 75 (1): 56- 73. doi: 10.1109/PROC.1987.13705
19	GERLA M , TZU-CHIEH TSAI J . Multicluster, mobile, multimedia radio network[J]. Wireless Networks, 1995, 1 (3): 255- 265. doi: 10.1007/BF01200845
20	LUO J , HUBAUX J P . A survey of research in inter-vehicle communications[J]. Embedded Security in Cars, 2006, 111- 122.
21	CHATTERJEE M, DAS S K, TURGUT D. An on-demand weighted clustering algorithm (WCA) for ad hoc networks[C]//Proc. of the IEEE Global Telecommunications Conference, 2000: 1697-1701.
22	张卫刚, 何庆, 陈浩亮, 等. 一种基于权重的动态分簇算法[J]. 暨南大学学报(自然科学与医学版), 2009, 30 (1): 35- 39. doi: 10.3969/j.issn.1000-9965.2009.01.009
	ZHANG W G , HE Q , CHEN H L , et al. A weighted dynamic clustering algorithm[J]. Journal of Jinan University(Natural Science & Medicine Edition), 2009, 30 (1): 35- 39. doi: 10.3969/j.issn.1000-9965.2009.01.009
23	王沁飞, 南建国, 黄金科, 等. 基于加权的无人机集群组网分簇算法[J]. 计算机应用研究, 2019, 36 (5): 1500-1503, 1514
	WANG Q F , NAN J G , HUANG J K , et al. Weighting based clustering algorithm for FANET[J]. Application Research of Computers, 2019, 36 (5): 1500-1503, 1514
24	严磊, 雷磊, 蔡圣所, 等. 基于路径规划的无人机加权高效分簇方法[J]. 计算机工程, 2018, 44 (11): 276- 281.
	YAN L , LEI L , CAI S S , et al. UAV weighted efficient clustering method based on path-planning[J]. Computer Engineering, 2018, 44 (11): 276- 281.
25	PANDEY A , SHUKLA P K , AGRAWAL R . An adaptive flying ad-hoc network (FANET) for disaster response operations to improve quality of service (QoS)[J]. Modern Physics Letters B, 2020, 34 (10): 2050010. doi: 10.1142/S0217984920500104
26	彭辉, 沈林成, 卜彦龙, 等. 一种Ad Hoc网络群组移动模型[J]. 软件学报, 2008, 19 (11): 2999- 3010.
	PENG H , SHEN L C , BU Y L , et al. Group mobility model for Ad Hoc networks[J]. Journal of Software, 2008, 19 (11): 2999- 3010.
27	KHAN A , AFTAB F , ZHANG Z . Self-organization based clustering scheme for FANETs using glowworm swarm optimization[J]. Physical Communication, 2019, 36, 100769. doi: 10.1016/j.phycom.2019.100769
28	ARAUJO J N R , BATISTA L S , MONTEIRO C C . Improving proactive routing with a mult-icriteria and adaptive framework in adhoc wireless networks[J]. Wireless Networks, 2020, 26 (6): 4595- 4614. doi: 10.1007/s11276-020-02366-4
29	VAIDYA O S , KUMAR S . Analytic hierarchy process: an overview of applications[J]. European Journal of operational Research, 2006, 169 (1): 1- 29. doi: 10.1016/j.ejor.2004.04.028
30	SHARIFI S A , BABAMIR S M . Evaluation of clustering algorithms in ad hoc mobile networks[J]. Wireless Personal Communications, 2019, 109 (4): 2147- 2186.
31	YUAN X F , DAI X S , ZHAO J Y , et al. On a novel multi-swarm fruit fly optimization algorithm and its application[J]. Applied Mathematics and Computation, 2014, 233, 260- 271.
32	李良光, 朱丽, 邢丽坤. 基于混合策略改进的果蝇优化算法[J]. 计算机工程与设计, 2020, 41 (1): 139- 144.
	LI L G , ZHU L , XING L K . Mixed strategy based improved fruit fly optimization algorithm[J]. Computer Engineering and Design, 2020, 41 (1): 139- 144.
33	夏征农. 大辞海, 军事卷[M]. 上海: 上海辞书出版社, 2007.
	XIA Z N . Big dictionary, military volume[M]. Shanghai: Shanghai Lexicographical Publishing House, 2007.
34	KHAN A , KHAN S , FAZAL A S , et al. Intelligent cluster routing scheme for flying ad hoc networks[J]. Science China Information Sciences, 2021, 64, 182305.

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

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