基于混合策略麻雀搜索算法的WSN覆盖优化  

WSN coverage optimization based on hybrid strategy sparrow search algorithm

作　　者：陈立万[1] 赵尚飞 曾蝶 欧俊 崔浩 Chen Liwan;Zhao Shangfei;Zeng Die;Ou Jun;Cui Hao(Teacher School of Education,Chongqing Three Gorges University,Chongqing 404120,China;School of Electronic and Information Engineering,Chongqing Three Gorges University,Chongqing 404120,China)

机构地区：[1]重庆三峡学院教师教育学院,重庆404120 [2]重庆三峡学院电子与信息工程学院,重庆404120

出　　处：《电子测量技术》2022年第23期174-180,共7页Electronic Measurement Technology

基　　金：重庆市教委科学技术研究项目(KJQN202101233,KJQN202001229);重庆市人工智能+智慧农业学科群开放基金(ZNNYKFB201901);重庆市三峡库区地质环境监测与灾害预警重点实验室开放基金(MP2020B0202)项目资助。

摘　　要：为了有效提高无线传感器网络的节点覆盖率,提出了一种基于混合策略麻雀搜索算法的WSN优化算法。利用Tent混沌映射初始化麻雀种群,增加种群的多样性;再用反向学习策略生成反向解扩大搜索范围,提高算法全局的搜索能力;加入惯性因子选择对预警麻雀个体进行Levy策略更新,提高算法局部搜索能力;对最优麻雀位置进行随机游走扰动进一步提高局部的搜索能力。仿真结果显示,HSSSA算法使节点分布更加均匀,覆盖率有明显提高。In order to effectively improve the node coverage of wireless sensor networks, a network coverage optimization algorithm based on hybrid strategy sparrow search algorithm is proposed. Firstly, the Tent chaotic mapping is used to improve the initialization sparrow population and increase the diversity of the population;Reverse learning strategy is used to generate inverse solutions to expand the search range and improve the global search capability;Then the inertia factor is added to select Levy strategy and update the sparrow position to improve the local search ability of the algorithm;Finally the optimal sparrow position is perturbed by random walk strategy to further improve the local search capability. The simulation results show that HSSSA algorithm resulted in a more uniform distribution of nodes and a significant improvement in coverage rate.

关 键 词：无线传感器网络 麻雀搜索算法 优化策略 覆盖率 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]