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融合多策略学习型麻雀搜索算法的UWSN覆盖优化

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水下无线传感器网络(UWSN)是三维无线传感器网络的一种应用场景.为解决UWSN覆盖率低的问题,提出一种多策略学习型麻雀搜索算法(MSLSSA).首先引入双重反冲运动策略,对麻雀个体中的发现者和加入者进行位置更新,其次引入互利学习策略进行麻雀个体中的警戒者互利信息共享,进一步提高搜索能力,增大寻优解搜索空间;然后采用改进的折射反向学习策略作为边界处理方法对越界个体进行处理.最后将该算法应用于UWSN覆盖优化,仿真实验分析表明,与五种相似算法进行比较,MSLSSA覆盖率达到96.61％,能够有效提升UWSN覆盖率,优化节点分布.
UWSN Coverage Optimization based on Multi-Strategy Learning Sparrow Search Algorithm
Underwater wireless sensor network( UWSN) is an application scenario of 3D WSN.To solve the problem of low coverage of UWSN,a multi-strategy learning sparrow search algorithm(MSLSSA)is proposed.Firstly,the dual recoil motion strategy is introduced to update the positions of discoverers and scroungers.Secondly,the mutually beneficial learning strategy is introduced to share mutually beneficial information among the sparrows who are responsible for detecting danger to further improve the search ability and increase the search space for optimal solutions.Then,the improved refraction reverse learning strategy is used as the boundary processing method to deal with the sparrows crossing the boundary.Finally,MSLSSA is applied to the coverage optimization of UWSN.Simulation analysis shows that,compared with five other similar algorithms,the coverage rate of MSLSSA reaches 96.61％,which can effectively improve the coverage rate of UWSN and optimize the distribution of nodes.

underwater wireless sensor networksparrow search algorithmdouble recoil motionmutually beneficial learningrefraction reverse learning

王振东、王建兰、王俊岭、李大海

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江西理工大学信息工程学院,江西 赣州341000

水下无线传感器网络 麻雀搜索算法 双重反冲运动 互利学习 折射反向学习

国家自然科学基金项目国家自然科学基金项目江西省自然科学基金江西省自然科学基金

620620376156203720212BAB20201420171BAB202026

2024

10.3969/j.issn.1004-1699.2024.08.017

传感技术学报
东南大学 中国微米纳米技术学会

传感技术学报

CSTPCD北大核心
影响因子：1.276
ISSN：1004-1699
年,卷(期)：2024.37(8)