白鲨优化算法是受白鲨捕猎行为的启发设计的一种新元启发式算法。该算法在求解高维优化问题时,易进入早熟状态,寻优结果精度较低。为此,文章提出一种改进的白鲨优化(improved white shake optimizer,IWSO)算法。首先使用Sinusoidal混沌...白鲨优化算法是受白鲨捕猎行为的启发设计的一种新元启发式算法。该算法在求解高维优化问题时,易进入早熟状态,寻优结果精度较低。为此,文章提出一种改进的白鲨优化(improved white shake optimizer,IWSO)算法。首先使用Sinusoidal混沌映射初始化种群,以提高种群多样性及初始解在解空间的分布性;其次,引入鸟群搜索行为,赋予白鲨游动速度自适应动态惯性权重,以提高算法的收敛速度;最后,在位置更新阶段引入精英白鲨余弦变异策略,利用余弦函数的周期性特征,驱使白鲨个体在精英白鲨的有限邻域内进行精细化开发,以提高收敛精度。在23个著名基准函数和CEC2014函数上做了性能对比实验,其结果表明,IWSO算法优于6种对比算法,适合求解函数优化问题。展开更多
针对移动传感器网络(Mobile sensor networks,MSNs)中动态目标(事件源)的监测优化问题,为提高网络覆盖质量,建立基于Voronoi剖分的监测性能(Quality of monitoring,QoM)评价函数,提出基于群集控制的传感器节点部署分布式控制算法.每个...针对移动传感器网络(Mobile sensor networks,MSNs)中动态目标(事件源)的监测优化问题,为提高网络覆盖质量,建立基于Voronoi剖分的监测性能(Quality of monitoring,QoM)评价函数,提出基于群集控制的传感器节点部署分布式控制算法.每个节点在本地结合最小二乘法和一致性算法来估计目标相对位置.相比传统算法,本文算法只需本地和单跳通信(可观测)邻居的信息,从而减小通信时长和能耗.算法在提高以目标为中心的一定区域监测性能的同时,使全体传感器速度趋于一致,从而在尽量保持网络拓扑结构的同时减少了整体移动能耗.在目标匀速或目标加速度信息全网可知的情况下,全体传感器速度渐近收敛至目标速度,且监测性能收敛至局部最优.所采用的目标位置估计滤波算法计算简单、切实可行.展开更多
Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tuna...Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.展开更多
This paper considers a multiple unmanned aerial vehicles （UAV） formation problem and proposes a new method inspired by bird flocking and foraging behavior. A bidirectional communication network, a navigator based on...This paper considers a multiple unmanned aerial vehicles （UAV） formation problem and proposes a new method inspired by bird flocking and foraging behavior. A bidirectional communication network, a navigator based on bird foraging behavior, a controller based on bird interaction and a movement switch are developed for multi-UAV formation. Lyapunov＇s second method and mechanical energy method are adopted for stability analysis. Parameters of the controller are optimized by Levy-flight based pigeon inspired optimization （Levy-PIO）. Patrol missions along a square and an S shaped trajectory are designed to test this formation method. Simula- tions prove that the bird flocking and foraging strategy can accomplish the mission and obtain satisfying performance.展开更多
文摘白鲨优化算法是受白鲨捕猎行为的启发设计的一种新元启发式算法。该算法在求解高维优化问题时,易进入早熟状态,寻优结果精度较低。为此,文章提出一种改进的白鲨优化(improved white shake optimizer,IWSO)算法。首先使用Sinusoidal混沌映射初始化种群,以提高种群多样性及初始解在解空间的分布性;其次,引入鸟群搜索行为,赋予白鲨游动速度自适应动态惯性权重,以提高算法的收敛速度;最后,在位置更新阶段引入精英白鲨余弦变异策略,利用余弦函数的周期性特征,驱使白鲨个体在精英白鲨的有限邻域内进行精细化开发,以提高收敛精度。在23个著名基准函数和CEC2014函数上做了性能对比实验,其结果表明,IWSO算法优于6种对比算法,适合求解函数优化问题。
文摘针对移动传感器网络(Mobile sensor networks,MSNs)中动态目标(事件源)的监测优化问题,为提高网络覆盖质量,建立基于Voronoi剖分的监测性能(Quality of monitoring,QoM)评价函数,提出基于群集控制的传感器节点部署分布式控制算法.每个节点在本地结合最小二乘法和一致性算法来估计目标相对位置.相比传统算法,本文算法只需本地和单跳通信(可观测)邻居的信息,从而减小通信时长和能耗.算法在提高以目标为中心的一定区域监测性能的同时,使全体传感器速度趋于一致,从而在尽量保持网络拓扑结构的同时减少了整体移动能耗.在目标匀速或目标加速度信息全网可知的情况下,全体传感器速度渐近收敛至目标速度,且监测性能收敛至局部最优.所采用的目标位置估计滤波算法计算简单、切实可行.
基金Supported by National Natural Science Foundation of China(61573199)National Natural Science Foundation of Tianjin(14JCYBJC18700)Basic Research Projects of High Education(3122015C025)
基金supported by the Hunan Provincial Natural Science Foundation of China(No.2023JJ40686).
文摘Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.
文摘This paper considers a multiple unmanned aerial vehicles （UAV） formation problem and proposes a new method inspired by bird flocking and foraging behavior. A bidirectional communication network, a navigator based on bird foraging behavior, a controller based on bird interaction and a movement switch are developed for multi-UAV formation. Lyapunov＇s second method and mechanical energy method are adopted for stability analysis. Parameters of the controller are optimized by Levy-flight based pigeon inspired optimization （Levy-PIO）. Patrol missions along a square and an S shaped trajectory are designed to test this formation method. Simula- tions prove that the bird flocking and foraging strategy can accomplish the mission and obtain satisfying performance.
