基于多策略改进蝴蝶优化算法的无线传感网络节点覆盖优化  

Node coverage optimization of wireless sensor network based on multi-strategy improved butterfly optimization algorithm

作　　者：韦修喜 彭茂松 黄华娟[1] WEI Xiuxi;PENG Maosong;HUANG Huajuan(College of Artificial Intelligence,Guangxi Minzu University,Nanning Guangxi 530006,China;College of Electronic Information,Guangxi Minzu University,Nanning Guangxi 530006,China)

机构地区：[1]广西民族大学人工智能学院,南宁530006 [2]广西民族大学电子信息学院,南宁530006

出　　处：《计算机应用》2024年第4期1009-1017,共9页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62266007);广西自然科学基金资助项目(2021GXNSFAA220068)。

摘　　要：针对无线传感网络(WSN)的节点覆盖存在着覆盖率低、节点分布不均匀的问题,提出一种基于多策略改进的蝴蝶优化算法(MIBOA)的节点覆盖优化策略。首先,将基础的蝴蝶优化算法(BOA)与麻雀搜索算法(SSA)结合改进搜索过程;其次,引入自适应权重系数提高寻优精度和收敛速度;最后,对当前最优个体进行柯西变异扰动,提高算法鲁棒性。基准测试函数的寻优实验结果说明,MIBOA基本可在3 s内求解测试函数最优值,且收敛平均值精度较BOA提高了97.96%。将MIBOA应用于WSN节点覆盖优化问题,与BOA和SSA相比,节点覆盖率至少提高了3.63个百分点;与改进灰狼优化算法(IGWO)相比,部署时间缩短了145.82 s;与改进鲸群优化算法(IWOA)相比,节点覆盖率提高了0.20个百分点且时间缩短了1112.61 s。综上,MIBOA可较好提高节点覆盖率并降低冗余覆盖率,有效延长WSN的生存时间。Aiming at the problems of low coverage rate and uneven distribution of nodes in Wireless Sensor Network(WSN),a node coverage optimization strategy based on Multi-strategy Improved Butterfly Optimization Algorithm(MIBOA)was proposed.Firstly,the basic Butterfly Optimization Algorithm(BOA)was combined with Sparrow Search Algorithm(SSA)to improve the search process.Secondly,the adaptive weight coefficient was introduced to improve the optimization accuracy and convergence speed.Finally,the current best individual was perturbed by Cauchy mutation to improve the robustness of the algorithm.The optimization experiment results on benchmark functions show that,MIBOA can basically solve the optimal value of the test function within 3 seconds,and the average accuracy of convergence is improved by 97.96%compared with BOA.MIBOA was applied to the WSN node coverage optimization problem.Compared with optimization results of BOA and SSA,the node coverage rate was improved by 3.63 percentage points at least.Compared with the Improved Grey Wolf Optimization algorithm(IGWO),the deployment time was shortened by 145.82 seconds.Compared with the Improved Whale Optimization Algorithm(IWOA),the node coverage rate was increased by 0.20 percentage points and the time was shortened by 1112.61 seconds.In conclusion,MIBOA can improve the node coverage rate and reduce the redundant coverage rate,and effectively prolong the lifetime of WSN.

关 键 词：蝴蝶优化算法 麻雀搜索算法 自适应权重系数 无线传感网络 节点覆盖率 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]