摘要
针对标准流向算法易陷入局部最优和收敛精度低等问题,提出一种融合莱维(Levy)飞行和入侵杂草策略的改进流向算法.首先,该算法在选择水流流向时引入Levy飞行机制,使水流沿最优水流位置方向做Levy飞行运动,避免陷入局部最优;其次,利用入侵杂草策略,对每一代水流进行繁殖、空间扩散和竞争操作,增加水流的多样性,扩大搜索范围,提高全局寻优能力.最后,将改进流向算法应用于无线传感器网络覆盖优化中,并与标准流向算法及其他改进算法进行实验对比.仿真结果表明,相比标准流向算法及其他改进算法,所提改进流向算法的覆盖性能有大幅提升,覆盖率可达98.52%,可实现更均匀的节点分布和更低的部署成本.
Addressing issues of local optima and low convergence accuracy existed in the standard flow direction algorithm,we propose an improved flow direction algorithm by incorporating Levy flight and weed invasion strategy.Firstly,in the selection of flow direction,the newly-designed algorithm introduces Levy flight mechanism to guide the flow along optimal flow positions and prevent it getting stuck in local optima.Secondly,breed,spread and compete operations are conducted to each generation of water flow by using invading weed strategy to increase the diversity of water flow,expand the search scope and further improve the overall optimization capabilities.Finally,the improved flow direction algorithm is implemented for the coverage optimization of wireless sensor networks,and its performance is compared with that of the standard flow direction algorithm and other improved algorithms.Simulation results show that the improved flow direction algorithm achieves a coverage rate of 98.52%,surpassing both the standard flow direction algorithm and other enhanced algorithms.This improvement leads to a more uniform node distribution and lower deployment costs.
作者
陈伟
杨盘隆
CHEN Wei;YANG Panlong(Department of Computer Information,Suzhou Vocational and Technical College,Suzhou 234099,Anhui Province,P.R.China;College of Computer Science and Technology,University of Science and Technology of China,Hefei 230027,Anhui Province,P.R.China)
出处
《深圳大学学报（理工版）》
CAS
CSCD
北大核心
2024年第2期241-247,共7页
Journal of Shenzhen University(Science and Engineering)
基金
安徽省高校优秀青年骨干教师国内外访学研修资助项目(gxgnfx2020152)
安徽省教育厅自然科学重点资助项目(2022AH052763)。
关键词
人工智能
无线传感器网络
流向算法
莱维飞行
入侵杂草算法
节点分布
覆盖优化
artificial intelligence
wireless sensor networks
flow direction algorithm
Levy flight
invasive weed optimization
node distribution
coverage optimization
