Application of improved hunter-prey optimization algorithm in WSN coverage

doi:10.11772/j.issn.1001-9081.2023081208

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (8): 2506-2513.DOI: 10.11772/j.issn.1001-9081.2023081208

• Network and communications • Previous Articles Next Articles

Application of improved hunter-prey optimization algorithm in WSN coverage

Le YANG, Damin ZHANG(), Qing HE, Jiaxin DENG, Fengqin ZUO

College of Big Data and Information Engineering，Guizhou University，Guiyang Guizhou 550025，China

Received:2023-09-06 Revised:2023-10-19 Accepted:2023-11-03 Online:2024-08-22 Published:2024-08-10
Contact: Damin ZHANG
About author:YANG Le， born in 2001， M. S. candidate. His research interests include wireless sensor network， swarm intelligence algorithm， machine learning.
HE Qing， born in 1982， Ph. D.， professor. His research interests include application of big data technology， machine learning， natural language processing.
DENG Jiaxin， born in 1998， M. S. candidate. Her research interests include cognitive radio， swarm intelligence algorithm.
ZUO Fengqin， born in 1998， M. S. candidate. Her research interests include cognitive radio， swarm intelligence algorithm.
Supported by:
National Natural Science Foundation of China(62166006);Science and Technology Foundation of Guizhou Province （Qiankehe Basic ［2020］1Y254）

改进猎人猎物优化算法在WSN覆盖中的应用

杨乐, 张达敏(), 何庆, 邓佳欣, 左锋琴

贵州大学大数据与信息工程学院，贵阳 550025

通讯作者: 张达敏
作者简介:杨乐（2001—），男，陕西咸阳人，硕士研究生，主要研究方向：无线传感器网络、群智能算法、机器学习
张达敏（1967—），男，贵州贵阳人，教授，博士，主要研究方向：认知无线电、群智能算法、信号与信息处理 1203813362@qq.com
何庆（1982—），男，贵州黔南州人，教授，博士，主要研究方向：大数据技术应用、机器学习、自然语言处理
邓佳欣（1998—），女，贵州毕节人，硕士研究生，主要研究方向：认知无线电、群智能算法
左锋琴（1998—），女，贵州织金人，硕士研究生，主要研究方向：认知无线电、群智能算法。
基金资助:
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)

Abstract

Abstract:

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， adaptive α variation was proposed on the basis of α stable 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 adaptive α variation 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 2 500 cycles in routing test.

Key words: Hunter-Prey Optimization (HPO) algorithm, differential evolution, adaptive α variation, dynamic reverse learning, Wireless Sensor Network (WSN) coverage

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2 500轮次。

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

CLC Number:

TP183

Le YANG, Damin ZHANG, Qing HE, Jiaxin DENG, Fengqin ZUO. Application of improved hunter-prey optimization algorithm in WSN coverage[J]. Journal of Computer Applications, 2024, 44(8): 2506-2513.

杨乐, 张达敏, 何庆, 邓佳欣, 左锋琴. 改进猎人猎物优化算法在WSN覆盖中的应用[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2506-2513.

Figures/Tables 15

Fig. 1 Flowchart of HPO algorithm

Fig. 2 Curve of change in α

Fig. 3 Probability distribution function corresponding to different α

Fig. 4 Schematic diagram of population individual coding

Tab. 1 Coverage rates of different algorithms in scenario one

算法	网络覆盖率/%
算法	最优值	最差值	平均值	标准差
IHPO	96.78	92.94	94.81	0.008 3
HPO	81.86	74.60	79.59	0.018 8
PSO	85.44	81.41	82.56	0.018 0
IPSO	93.46	85.15	90.35	0.016 9
GWO	93.20	75.28	90.02	0.038 8
IGWO	95.39	90.83	92.69	0.020 5

Fig. 5 Iteration curves of different algorithms in scenario one

Fig. 6 Influence of node number change on coverage rate in scenario one

Tab. 2 Coverage rates of different algorithms in scenario two

算法	网络覆盖率/%
算法	最优值	最差值	平均值	标准差
IHPO	93.98	91.22	92.56	0.018 5
HPO	75.36	71.52	73.61	0.007 9
PSO	72.03	69.38	71.19	0.008 2
IPSO	90.58	73.56	81.21	0.032 2
GWO	72.23	69.38	70.38	0.007 4
IGWO	89.14	72.68	79.52	0.042 0

Fig. 7 Iteration curves of different algorithms in scenario two

Fig. 8 Deployment results of different algorithms in scenario two

Fig. 9 Influence of node number on coverage rate in scenario two

Fig. 10 IHPO deployment with 100 nodes in scenario two

Tab. 3 Parameters of LEACH protocol

参数名称	值
基站坐标/（m，m）	（50，50）
节点初始能量/J	1
自由空间能耗系/（pJ·bit·m^-2）	10
多路衰减能耗系数/（pJ·bit·m^-4）	0.001 3
处理单位数据能耗/（nJ·bit^-1）	50
数据包大小/bit	4 000
控制信息大小/bit	200
簇头概率	0.1
最大运行轮次	2 500

Fig. 11 Rounds where the first sensor node died and all nodes died for different algorithms

Fig. 12 Comparison of energy consumption curves of different algorithms

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Application of improved hunter-prey optimization algorithm in WSN coverage

改进猎人猎物优化算法在WSN覆盖中的应用

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