引用本文:滕志军,李哲,王幸幸,杜春秋,李梦.无线传感器网络中基于μ律爆炸算子的烟花虚拟力混合覆盖策略[J].控制理论与应用,2023,40(5):817~824.[点击复制]
TENG Zhi-jun,LI Zhe,WANG Xing-xing,DU Chun-qiu,LI Meng.Fireworks virtual force mixture covering strategy based on μ-law explosion operator in wireless sensor networks[J].Control Theory and Technology,2023,40(5):817~824.[点击复制]
无线传感器网络中基于μ律爆炸算子的烟花虚拟力混合覆盖策略
Fireworks virtual force mixture covering strategy based on μ-law explosion operator in wireless sensor networks
摘要点击 1520  全文点击 392  投稿时间:2021-05-17  修订日期:2023-03-12
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DOI编号  10.7641/CTA.2021.10415
  2023,40(5):817-824
中文关键词  无线传感器网络  μ律爆炸算子  烟花算法  虚拟力  覆盖率
英文关键词  wireless sensor networks  μ-law explosion operator  fireworks algorithm  virtual force  coverage rate
基金项目  国家自然科学基金青年科学基金项目(61501107), 吉林省教育厅“十三五”科学研究规划项目(JJKH20180439KJ)
作者单位E-mail
滕志军 东北电力大学 tengzhijun@163.com 
李哲* 东北电力大学 1761669036@qq.com 
王幸幸 东北电力大学 1471385830@qq.com 
杜春秋 东北电力大学 2912289944@qq.com 
李梦 东北电力大学 15765594470@163.com 
中文摘要
      针对烟花算法在无线传感器网络节点部署过程中易陷入局部最优导致节点分布不均匀、后期收敛速度慢 等问题, 本文提出一种基于μ律爆炸算子的烟花虚拟力混合算法(μFW–VFA). 首先, 采用μ律特性曲线重新定义爆炸 算子, 增强烟花间的差异性, 通过动态调整μ值使烟花爆炸的数目和幅度随迭代次数动态调整, 以平衡烟花局部和 全局的寻优能力. 其次, 引入虚拟力调节停滞烟花内传感器节点的位置信息, 加速烟花种群进化, 增强算法跳出局部最优的能力, 提高算法收敛速度. 仿真实验表明, 经μFW–VFA部署后, 网络的重叠区域和监测盲区显著减少, 有效提升了网络覆盖率并压缩节点移动距离.
英文摘要
      The firework algorithm is easy to fall into a local optimum in the application of node coverage for wireless sensor networks. In order to solve the problems of uneven distribution of nodes and slow convergence, a novel hybrid firework-virtual force algorithm based on the μ-law is proposed. Firstly, redefine the explosion operator with the aid of μ-law so that the number and amplitude of fireworks explosions will be dynamically adjusted by resizing the value of μ in different iterations, which means that fireworks are more diverse. Secondly, the virtual force is introduced to adjust the position information of sensor nodes in the stagnant fireworks, which can accelerate the evolution of the fireworks population. Through the above approaches, the improved algorithm can jump from the local optimal solution and accelerate convergence. Accordingly, the algorithm can avoid an uneven distribution of nodes in the application of node coverage. Simulation experiments demonstrate that the algorithm can significantly reduce the overlapping areas and monitoring blind areas of the network, and meanwhile, the algorithm effectively increases the node coverage and compresses the node moving distance.