引用本文:王凯生,黄炎焱,孙鹏耀,翟文杰.面向能耗均衡的海战场感知网络部署规划[J].控制理论与应用,2026,43(2):355~365.[点击复制]
WANG Kai-sheng,HUANG Yan-yan,SUN Peng-yao,ZHAI Wen-jie.Deployment planning of naval battlefield perception network for energy balance[J].Control Theory & Applications,2026,43(2):355~365.[点击复制]
面向能耗均衡的海战场感知网络部署规划
Deployment planning of naval battlefield perception network for energy balance
摘要点击 141  全文点击 20  投稿时间：2024-08-02  修订日期：2025-09-22
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DOI编号  10.7641/CTA.2025.40418
  2026,43(2):355-365
中文关键词  战场感知  网络结构  最优化  能耗均衡  人工蜂鸟算法
英文关键词  battlefield perception  network architecture  optimization  balanced energy consumption  artificial hummingbird algorithm
基金项目  装备预研共用技术项目(50901020202), 中船创新基金项目(KJB2023012)资助.
作者单位E-mail
王凯生 南京理工大学自动化学院 2295694094@qq.com 
黄炎焱* 南京理工大学自动化学院 2295694094@qq.com 
孙鹏耀 南京理工大学自动化学院  
翟文杰 南京理工大学自动化学院  
中文摘要
      为实现对海战场态势的有效监测, 针对传统感知网络部署模型连通性差、寿命较短、感知量低等问题, 本 文构建了一套基于能耗均衡策略的网络部署模型, 将网络连通性加入约束项, 提出均衡网络节点的能量消耗策略, 以提高网络的生存周期与感知总量. 提出改进人工蜂鸟算法(IAHA)对模型进行求解, 引入差分变异策略与反向映 射策略提高算法的全局寻优能力, 引入自适应觅食策略提高算法的收敛速度. 实验结果表明, 文章所提模型生成的 网络连通性强, 且相比其他部署策略, 基于能耗均衡策略生成的网络有更长的生存周期与更高的感知总量; 相比于 对比算法, IAHA的全局寻优能力更强、收敛速度更快.
英文摘要
      In order to realize effective monitoring of naval battlefield situation, this paper constructs a set of network deployment model based on energy balancing strategy, aiming at the problems of poor connectivity, short life and low perception of traditional perception network deployment model, and adds network connectivity to the constraint term, and proposes an energy consumption balancing strategy for network nodes to improve the life cycle and total perception of the network. An improved artificial hummingbird algorithm (IAHA) is proposed to solve the model. Differential variation strategy and reverse mapping strategy are introduced to improve the global optimization ability of the algorithm, and adaptive foraging strategy is introduced to improve the convergence speed of the algorithm. The experimental results show that the network generated by the proposed model has strong connectivity, and compared with other deployment strategies, the network generated based on energy balancing strategy has a longer lifetime and a higher perception total. Compared with the comparison algorithm, IAHA has stronger global optimization ability and faster convergence speed.