改进猎人猎物优化算法在WSN覆盖中的应用  

Application of improved hunter-prey optimization algorithm in WSN coverage

作　　者：杨乐 张达敏 何庆 邓佳欣 左锋琴 YANG Le;ZHANG Damin;HE Qing;DENG Jiaxin;ZUO Fengqin(College of Big Data and Information Engineering,Guizhou University,Guiyang Guizhou 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵阳550025

出　　处：《计算机应用》2024年第8期2506-2513,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62166006);贵州省科学技术基金资助项目(黔科合基础[2020]1Y254)。

摘　　要：针对传统无线传感器网络(WSN)节点部署覆盖盲区大、分布不均等问题,提出一种改进的猎人猎物优化(IHPO)算法优化网络覆盖。首先,在猎物位置更新阶段,引入差分进化(DE)思想并借助动态比例因子进行交叉变异,从而增强种群信息交流;其次,在全局最优位置更新阶段,由α稳定分布提出自适应α变异对全局最优位置进行扰动,从而平衡不同时期算法的性能需求;最后,利用自适应α变异扰动的全局最优位置引导种群完成动态反向学习,从而增加种群的全局搜索能力和多样性。在WSN覆盖问题中,使用IHPO优化的网络节点分布更均匀、覆盖率更高,在传感器感知能力不足时能达到92.56%的覆盖率,对比原始HPO算法优化的节点提高了25.74%,对比改进粒子群优化(IPSO)算法、改进灰狼优化算法(IGWO)优化的节点分别提高了13.98%、16.41%。同时,IHPO算法优化的节点能耗更均衡,在路由测试中的网络工作时间可以延长至2500轮次。An Improved Hunter-Prey Optimization(IHPO)algorithm was proposed to improve network coverage in order to tackle the issues of node deployment coverage blind areas and uneven distribution of conventional Wireless Sensor Network(WSN).Firstly,with the goal to improve population information exchange,Differential Evolution(DE)was introduced,and cross-variation with dynamic proportional factors was implemented during the prey position update stage.Secondly,adaptiveαvariation was proposed on the basis ofαstable distribution in the phase of updating the global optimal location to disturb the location,so as to balance the algorithm’s performance demands over time.Finally,the population was guided to complete dynamic reverse learning by using the global optimal location with adaptiveαvariation perturbation,thereby increasing the population’s variety and capacity for global search.In WSN coverage challenge,the network nodes optimized by IHPO were distributed more uniformly and had a higher coverage rate.When the sensor perception capacity was insufficient,the coverage rate increased to 92.56%,which was 25.74%higher than that of the nodes optimized by the original HPO algorithm,and 13.98%and 16.41%higher than those of the nodes optimized by Improved Particle Swarm Optimization(IPSO)and Improved Grey Wolf Optimizer(IGWO),respectively.At the same time,the energy consumption of the nodes optimized by IHOP was more evenly distributed,and those nodes had the network working duration increased to 2500 cycles in routing test.

关 键 词：猎人猎物优化算法 差分进化 自适应α变异 动态反向学习 无线传感器网络覆盖 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]