基于改进蜣螂优化算法的海洋牧场三维UWSN覆盖方法  

3D UWSN coverage method for marine ranching based on improved Dung beetle optimization algorithm

作　　者：付雷 王骥 FU Lei;WANG Ji(College of Electronics and Information Engineering,Guangdong Ocean University,Zhanjiang 524088,China;Research Center of Guangdong Smart Oceans Sensor Networks and Equipment Engineering,Zhanjiang 524088,China)

机构地区：[1]广东海洋大学电子与信息工程学院,广东湛江524088 [2]广东省智慧海洋传感网及其装备工程技术研究中心,广东湛江524088

出　　处：《中山大学学报（自然科学版）（中英文）》2024年第2期115-122,共8页Acta Scientiarum Naturalium Universitatis Sunyatseni

基　　金：广东省普通高校重点领域新一代信息技术专项(2020ZDZX3008);广东省人工智能领域重点专项(2019KZDZX1046)。

摘　　要：针对海洋牧场三维环境监测,提出了一种基于改进蜣螂优化算法(IDBO,improved Dung beetle opti‐mizer)的UWSN(underwater wireless sensor networks)覆盖方法。首先,在蜣螂优化算法(DBO)种群初始化时加入Chebyshev混沌映射,使得种群资源在搜索空间的分配方面更加均衡。其次,通过自适应权重因子和Levy飞行改进觅食小蜣螂的位置更新方式,提升了位置搜索能力和DBO算法的收敛能力。将IDBO算法应用在海洋牧场UWSN覆盖优化中,仿真结果表明:在不同参数环境下,IDBO算法的覆盖率高于随机部署和其他智能优化算法,并且能以较低的节点能耗获得更高的覆盖率,节点分布也更加合理。For the environmental monitoring of marine ranching,a 3D underwater wireless sensor networks coverage method based on improved Dung beetle optimizer(IDBO)is proposed.Firstly,Chebyshev chaotic mapping was added to the DBO population initialization to make population resources more balanced in the allocation of search space.Secondly,adaptive weight factor and Levy flight were used to improve the position update mode of Dung beetles,which improved the position search ability and the convergence ability of DBO algorithm.The IDBO algorithm was applied to the UWSN coverage optimization of marine ranching,the simulation results show that the coverage rate of IDBO algorithm is higher than that of random deployment and other intelligent optimization algorithms under different parameter environments,and it achieves higher coverage rate with lower node energy consumption,and the distribution of nodes is more reasonable.

关 键 词：海洋牧场 水下无线传感器网络 Chebyshev混沌映射 自适应权重因子 Levy飞行 

分 类 号：TB393[一般工业技术—材料科学与工程]