引用本文:崔亚妮,任佳,杜文才,SHIKHIN Vladimir.多无人船通信网络拓扑优化控制算法[J].控制理论与应用,2016,33(12):1639~1649.[点击复制]
CUI Ya-ni,REN Jia,DU Wen-cai,SHIKHIN Vladimir.Network topology optimization control algorithm for multiple unmanned surface vehicle[J].Control Theory and Technology,2016,33(12):1639~1649.[点击复制]
多无人船通信网络拓扑优化控制算法
Network topology optimization control algorithm for multiple unmanned surface vehicle
摘要点击 3219  全文点击 2108  投稿时间：2016-06-30  修订日期：2016-12-22
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DOI编号  10.7641/CTA.2016.60473
  2016,33(12):1639-1649
中文关键词  无人船  拓扑优化  电波传播  类电磁机制  粒子群优化
英文关键词  unmanned surface vehicle  topology optimization  radio propagation  electromagnetism-like mechanism  particle swarm optimization
基金项目  国家国际科技合作专项(2015DFR10510), 国家自然科学基金项目(61562018),海南省高等学校科学研究项目(HNKY2014-04)资助.
作者单位E-mail
崔亚妮 海南大学信息科学技术学院 cyn0213@163.com 
任佳* 海南大学信息科学技术学院，海南大学南海海洋资源利用国家重点实验室 renjia@hainu.edu.cn 
杜文才 海南大学信息科学技术学院，海南大学南海海洋资源利用国家重点实验室  
SHIKHIN Vladimir 莫斯科动力学院自动化与计算机工程学院  
中文摘要
      为构建有效、可靠的多无人船网络拓扑结构, 本文提出一种基于改进粒子群优化的多无人船网络拓扑优化 控制算法. 该算法通过综合考虑网络连通度、链路通信质量、网络连接收益和网络连接成本构建多无人船网络拓扑 优化模型. 为确保模型与应用对象的适配性, 重点分析海上无线电波的传播特性, 并在此基础上, 完成链路通信质 量、网络连接收益和网络连接成本的表征. 为获得模型的全局最优解,加快模型的收敛速度, 在粒子群优化算法的 迭代寻优过程中,借鉴电磁场中带电粒子间的相互作用, 利用粒子的电荷量动态自适应调整算法的控制参数, 当粒 子种群多样性小于给定的阈值时, 将粒子种群中适应度值最小的粒子作为扰动粒子,引导粒子向未搜索区域移动, 克服算法的早熟收敛. 仿真结果证明了该算法的有效性.
英文摘要
      In order to construct an ef?cient and reliable network topology, a topology optimization control algorithm based on improved particle swarm optimization is proposed. And the network topology optimization model of multiple USV (unmanned surface vehicle) is constructed by considering the network connectivity, the link communication quality, the earnings and the cost of network connection. In order to ensure the suitability between the model and the application object, maritime radio propagation characteristics are analyzed, and on this basis, the representation of the link commu- nication quality, the earnings and the cost of network connection are obtained. To get the globally optimal solution and accelerate the convergence of the model, in the iterative optimization process of particle swarm optimization algorithm, considering the interaction between charged particles in electromagnetic ?eld, using the charge quantity of the particle dynamically adaptive adjust the control parameters. When the diversity of particle swarm is less than the given threshold, the particle owning the smallest ?tness value in the swarm will be regarded as the disturbing particle to guide the swarm to the region which has not been searched, to overcome the premature convergence of the algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm.