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基于FW-PSO算法优化无线传感网络拓扑结构的方法

张颖 杨广媛

张颖, 杨广媛. 基于FW-PSO算法优化无线传感网络拓扑结构的方法[J]. 电子与信息学报, 2021, 43(2): 396-403. doi: 10.11999/JEIT191039
引用本文: 张颖, 杨广媛. 基于FW-PSO算法优化无线传感网络拓扑结构的方法[J]. 电子与信息学报, 2021, 43(2): 396-403. doi: 10.11999/JEIT191039
Ying ZHANG, Guangyuan YANG. The Optimization of Wireless Sensor Network Topology Based on FW-PSO Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(2): 396-403. doi: 10.11999/JEIT191039
Citation: Ying ZHANG, Guangyuan YANG. The Optimization of Wireless Sensor Network Topology Based on FW-PSO Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(2): 396-403. doi: 10.11999/JEIT191039

基于FW-PSO算法优化无线传感网络拓扑结构的方法

doi: 10.11999/JEIT191039
基金项目: 国家自然科学基金(61673259)
详细信息
    作者简介:

    张颖:男,1968年生,博士,教授,博士生导师,研究方向为海洋物联网、海事无线通信、无线自组织网络

    杨广媛:女,1994年生,硕士生,研究方向为无线传感器网络拓扑控制、抗毁性及系统自愈控制

    通讯作者:

    张颖 yingzhang@shmtu.edu.cn

  • 中图分类号: TP393

The Optimization of Wireless Sensor Network Topology Based on FW-PSO Algorithm

Funds: The National Natural Science Foundation of China (61673259)
  • 摘要:

    无线传感网络(WSN)具有无标度网络的特征,通常工作在无人值守的开放性环境中,极易遭受到各种蓄意攻击。攻击使得网络发生故障,甚至会导致整个网络瘫痪。该文基于复杂网络领域的无标度网络,构建具有无标度特性的无线传感网络模型。利用烟花算法及粒子群算法(PSO)寻优过程中的搜索能力、种群多样性等优点,提出了一种FW-PSO算法,该算法在全局搜索能力和收敛速度上具有较好的性能。针对具有无标度特性的网络模型,用FW-PSO算法对网络拓扑进行优化,在不同的攻击策略下分别从动态抗毁性和静态抗毁性分析优化前后网络的性能。仿真实验表明,与其他同类算法相比,经过该文所提算法优化后的无线传感网络的动态和静态抗毁性能都有明显提升。

  • 图  1  自然连通度随迭代次数变化情况

    图  2  $\alpha $不同时,4种方法的抗毁性对比

    图  3  随机攻击时网络连通性对比

    表  1  FW-PSO算法的伪代码

       FW-PSO算法:
     1. -- fpbest: 个体的最佳适应度值
     2. -- fgbest: 群体的最佳适应度值
     3. 输入:目标函数$f\left( x \right)$,邻接矩阵${{A}}\left( G \right)$;
     4. 参数初始化: gronum, $n,{c_1},{c_2},{w_{\max } },{w_{\min } },{ { {{\rm{gen}}} }_{\max } }, A,M, a,b$,
       maxgen
     5. Set ${\rm{fpbes}}{{\rm{t}}_i} \leftarrow {x_i}\left( { {x_i} \in 1,2, \cdots ,{\rm{gronum}}} \right)$, ${ { {{\rm{gen}}} }_{\max } } \leftarrow 1$
     6. While ${\rm{gen}} < { { {{\rm{gen}}} }_{\max } }$
     7.   for ${\rm{pgen}} \leftarrow 1$ to maxgen
     8.    for $i \leftarrow 1$ to gronum
     9.     更新粒子的${v_i},{x_i}$ by式(6)、式(7)
     10.     计算$f\left( {{x_i}} \right)$
     11.     if $f\left( { {x_i} } \right) > {\rm{fpbest}}\left( { {x_i} } \right)$
     12.      then ${\rm{fpbest}}\left( { {x_i} } \right) \leftarrow f\left( { {x_i} } \right)$
     13.     end if
     14.     if $f\left( { {x_i} } \right) < {\rm{fpbest}}\left( { {x_i} } \right)$
     15.      then $f\left( { {x_i} } \right) \leftarrow {\rm{fpbest}}\left( { {x_i} } \right)$
     16.     end if
     17.    end for
     18.    ${\rm{pgen}} \leftarrow {\rm{pgen}} + 1$
     19.   end for
     20.  将粒子组按降序排序,选出适合度较好的n个粒子,并根
        据3.2节FW-PSO算法选出n个最优粒子
     21. 计算新种群的fpbest,fgbest
     22. ${\rm{gen}} \leftarrow {\rm{gen}} + 1$
     23. end while
     24. 输出:fgbest
    下载: 导出CSV

    表  2  仿真参数设置

    参数仿真参数值
    网络节点数gronum100
    网络边数W191
    加速因子${c_1},{c_2}$1.49445
    最小惯性权重${w_{\min }}$0.4
    最大惯性权重${w_{\max }}$0.9
    PSO算法保留的最优粒子数n40
    爆炸数目调节因子A5
    爆炸数目调节因子M6
    爆炸数目限制因子a0.3
    爆炸数目限制因子b0.6
    机器精度$\varepsilon $$2.2204 \times {10^{ - 16}}$
    变异火花数10
    总迭代次数${ { {{\rm{gen}}} }_{\max } }$100
    PSO迭代次数maxgen300
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-25
  • 修回日期:  2020-07-26
  • 网络出版日期:  2020-08-21
  • 刊出日期:  2021-02-23

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