立方混沌非线性哈里斯鹰优化算法在无线传感器节点部署分析研究  

Analysis of wireless sensor node deployment based on nonlinear CCHHO algorithm

作　　者：洪丽啦 莫愿斌[1,2] 鲍冬雪 HONG Lila;MO Yuanbin;BAO Dongxue(School of Artificial Intelligence,Guangxi Minzu University,Nanning 530006,China;Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis,Guangxi Minzu University,Nanning 530006,China)

机构地区：[1]广西民族大学人工智能学院,广西南宁530006 [2]广西民族大学广西混杂计算与集成电路设计分析重点实验室,广西南宁530006

出　　处：《现代电子技术》2023年第6期161-168,共8页Modern Electronics Technique

基　　金：国家自然科学基金项目(21466008);广西自然科学基金项目(2019GXNSFAA185017);广西民族大学科研项目(2021MDKJ004)。

摘　　要：为提高无线传感器网络(WSN)节点部署的覆盖率,文中提出一种立方混沌非线性哈里斯鹰优化算法(CCHHO)的无线传感器节点部署优化方法。在初始化时期,引入立方混沌映射初始化种群,使种群在解空间分布更加均匀,提高种群多样性;其次,为更好地平衡探索和开发,将逃跑能量因子由线性变成非线性;最后,在开发阶段引入纵横交叉策略增强局部探索能力,增强个体之间的信息交流,避免算法陷入局部最优,提高算法的求解精度。6个基准测试函数的测试结果表明,CCHHO算法具有较快的收敛速度和较高的求解精度。将CCHHO算法应用在WSN节点部署优化,实验结果表明,相较于改进正余弦算法(ESCA)、自适应混沌量子粒子群算法(DACQPSO)、外推人工蜂群算法(EABC),CCHHO算法覆盖率分别提升0.31%,4.16%,8.02%。A wireless sensor node deployment optimization method based on nonlinear cubic chaotic Harris Hawks optimization(CCHHO)algorithm is proposed to improve the coverage rate of wireless sensor network(WSN)node deployment.In the initialization period,the cubic chaotic map is introduced to initialize the population,which makes the distribution of the population more uniform in the solution space and improves the diversity of the population.In order to better balance exploration and exploitation,the escape energy factor is changed from linear to nonlinear.In the exploitation stage,the vertical and horizontal crossing strategy is introduced to enhance the local exploration ability,enhance the information exchange between individuals,avoid the algorithm falling into local optimization,and improve the accuracy of the algorithm.The testes of 6 benchmark functions show that CCHHO algorithm has faster convergence speed and higher solution accuracy.The CCHHO algorithm is applied to WSN node deployment optimization.The experimental results show that in comparison with the improved exponential sine cosine algorithm(ESCA),the dynamic self⁃adaptive chaotic quantum particle swarm optimization algorithm(DACQPSO)and extrapolated artificial bee colony(EABC)algorithm,the coverage of CCHHO algorithm is improved by 0.31%,4.16%and 8.02%respectively.

关 键 词：无线传感器网络 节点部署 哈里斯鹰优化算法 种群多样性 混沌映射 实验测试 

分 类 号：TN915-34[电子电信—通信与信息系统]