基于BWAS的无线传感器网络静态分簇路由算法

为提高路径搜索效率,避免动态分簇较多的能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)的无线传感器网络静态分簇路由算法.BWAS是对蚁群算法的改进,在路径搜寻过程中评价出最优最差蚂蚁,引入奖惩机制,加快了路径搜索速度.通过无线传感器网络静态分簇、簇内动态选举簇头,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,能减少路径寻优能量消耗,实现均衡能量管理,延长网络寿命,且具有较强的鲁棒性.通过与基于BWAS的动态分簇和基于蚁群算法的动态分簇路由的仿真实验相比较,证实了本算法的有效性.     

基于改进蚁群算法的无线传感器网络路由

为了降低节点能量消耗,延长网络生存的时间,提出了一种改进蚁群算法的无线传感器网络路由机制。首先将无线传感器网络服务质量分为3类,然后利用蚁群算法可以自适应网络状况动态性的优势,构建传感器节点转移函数和信息素更新规则,自适应构建数据路由,最后采用仿真模拟实验对算法性能进行检验。实验结果表明,相对于与现有无线传感器路由算法,本文通过引入蚁群优化机理挖掘传感器节点之间的关联性,数据传输延迟、可靠性和能量开销上具有更好的性能,使整个网络性能保持最优。     

仿血管路径的无线传感器网络故障容错路由算法

为提高无线传感器网络故障容错性和传输稳定性,实现网络负载均衡,提出了一种仿血管路径的无线传感器网络故障容错路由算法.研究了人体血管路径特性及属性关联,对网络节点分区域等级标定并以不同概率值进行静态分簇,运用改进的蚁群算法BWAS(最优最差蚂蚁系统)生成节点路径,以路径信息素值作为传输路径的选择概率建立仿血管拓扑结构路由...     

一种基于蚁群优化的分簇路由算法

在无线传感器网络路由协议的研究中,能量高效是其首要设计目标.传统LEACH协议产生簇头数目比较随机,并且簇头直接与基站通信导致能量消耗过快.在分析传统和改进LEACH路由协议的基础上,提出了一种簇头数目固定的簇头选择机制,解决了簇头分布不均匀的问题.并且将蚁群优化算法应用到无线传感器网络的路径选择中,利用蚁群的动态适应性和寻优能力,在簇头与基站之间形成一条最优路径进行通信.在Matlab平台下对新提出的算法进行仿真测试实验,实验结果表明,相对于LEACH路由协议,该算法降低了平均能量消耗,延长了网络的生命周期.     

无线传感器网络的蚁群自组织算法

探测效能与能量节省的综合性能优化是无线传感器网络研究的一个热点问题.提出了一种分布式、自适应的无线传感器网络蚁群自组织算法,将无线传感器网络节点映射为情绪蚂蚁,通过蚁群间的协同对节点的唤醒概率进行群体智能优化,从而实现无线传感器网络自组织,并以定理的形式给出了性能指标和相关参数的设计方法.仿真表明,算法实现在唤醒较少节点的前提下,对目标保持了较好的探测能力.     

改进遗传算法优化无线传感器网络路由算法

为了有效延长无线传感器网络的生存时间,针对传感器节点能耗不均衡难题,提出一种改进遗传算法优化的无线传感器网络路由算法。首先对LEACH算法不足进行分析,然后构建簇头节点选择的目标函数,并将其作为遗传算法的搜索目标,最后通过遗传算法找到下一时刻簇头的候选节点,并针对遗传算法不足进行相应改进。采用仿真实验对算法的性能进行分析,结果表明,相对于其它无线传感器路由算法,本文算法可以保证无线传感器的节点能量均衡,延长了网络的生存时间。     

多媒体异构Mesh网络体系设计及跨层QoS路由算法研究

 针对无线Mesh网络的异构特性和多媒体业务的QoS要求,研究了一种跨域、跨层、跨节点的无线Mesh网络QoS自适应体系架构.在此基础上,利用双层规划数学模型描述之,并利用改进的蚁群算法来求解该双层规划模型,从而提出了基于双层规划模型的蚁群优化路由算法.仿真结果表明双层规划数学模型充分地考虑并优化了路径的各QoS指标,提出的蚁群优化路由算法能够很好地收敛于双层规划模型的最优解,且具有复杂度低、收敛速度快的特点.     

混合光无线传感网络节能路由算法优化

为解决混合光无线传感网络节点因自身能量耗尽而过早消亡的问题.结合改进的蚁群算法,对一般混合光无线传感网络节能路由算法进行优化.首先给出节能路由算法的假定条件,设置算法具体应用场景,然后构建混合光无线传感器网络通信能耗模型,最后从引入路径度量,改进前向蚂蚁和返回蚂蚁的信息素更新规则着手,提高传统蚁群算法收敛速度,以减少能...     

WMN中基于免疫算法的QoS路由研究

无线Mesh网络的路由技术是影响网络性能的一个关键问题。针对无线Mesh网络的QoS多约束路由算法难于找到最优路径的问题,提出了一种基于免疫算法的路由算法,利用免疫算法的寻优能力,实现了无线Mesh网络QoS多约束条件下的最优路径选择,并与基于遗传算法的路由算法进行了比较。实验结果表明,利用免疫算法获得满足QoS要求的最佳路径时,不容易陷入局部最优,且收敛速度快,性能优越,更符合无线通信实时性的要求。     

基于改进蚁群优化算法的无线传感器网络路由研究

无线传感器网络能够进行传感器的数据收集和传输,为用户提供多元信息,在不同的领域都具有广阔的应用前景,但无线传感网络的路由研究没有动态的拓扑结构以及全地址机制,并且资源有限,因此需要新的无线传感器路由算法进行支持.本文提出了基于改进蚁群优化算法的无线传感器网络路由算法,将改进的蚁群算法的自组织、动态和多路径等特性结合到无线传感网络路由研究中,用仿真实验进行分析表明该算法在网路平均能耗方面的显著改善,并证明其基本满足无线传感器网络的设计目标.     

基于蚁群算法的自动化仓储系统空闲优化

根据目前自动化仓储系统提出了蚁群算法的一种改进型优化算法。通过分析自动化仓储系统的工作特性,建立与之相类似的数学模型,加入特殊的空闲优化模式,结合遗传算法对原有的蚁群算法进行优化。在长时间连续工作的自动化仓储系统中,采用优化后的蚁群算法迭代计算次数更少、计算时间更短、并且最小路径更优化,更适应于现代化仓储系统。     

基于蚁群算法的网络层析故障链路诊断

为了解决网络层析成像中链路故障诊断的NP难问题,提出一种基于蚁群算法的故障链路诊断方法。首先将问题建模成一个组合优化问题,利用蚁群算法在解决组合优化问题中独特的优势进行求解。不同于传统的蚁群算法,求解故障链路时蚁群在初始放置点和可行路径上都受约束。为了加快算法的收敛速度,对蚁群算法的初始信息素浓度进行优化。仿真结果表明,所提出的算法在故障链路检测中具有较好的精度和召回率。     

Multi-point path planning based on the algorithm of colony-particle swarm optimization

The problem of multi-point path planning is a NP-hard problem,which is equivalent to finding the shortest path of a starting point and some specific node.Aiming at the problem of multi-point path planning,a retrospective ant colony-particle swarm optimization algorithm was proposed.This algorithm used Floyd-Warshall to transform the graph and combined ant colony algorithm and particle swarm algorithm to find the shortest path.The experimental results show that this algorithm can find the precise solution under small data,at the same time,under a large amount of data,can be better than the maximum minimum ant colony algorithm and genetic algorithm.     

无线传感器网络自适应蚁群定位算法研究

针对蚁群定位算法可能出现局部最优解而导致定位不准确的问题,提出了无线传感器网络自适应蚁群定位算法。通过将节点估计坐标移动方向离散化,将传感器定位问题转换成离散组合最优问题。定位过程中通过聚度和信息权重对传感器节点估计坐标向各个方向移动的概率进行修正,解决了定位结果收敛于局部最优解的问题。仿真结果表明,自适应蚁群定位算法比传统蚁群定位算法具有更低的定位误差。     

遗传蚁群禁忌融合算法的研究

通过对遗传算法、蚁群算法和禁忌搜索算法三种算法的分析研究,针对其各自优缺点,提出一种融合遗传算法、蚁群算法和禁忌搜索算法的融合算法。融合算法是采用遗传算法生成初始信息素分布,利用蚁群算法快速求精确解,同时将遗传禁忌算子引入到蚁群算法的每轮迭代中,有效解决了蚁群系统初始信息素匮乏、易陷入局部最优和收敛速度慢的缺点,实现优势互补。通过NP-hard30问题仿真实验,结果显示算法具有良好的寻优能力和寻优效率。     

Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm

Aiming at the multi-constraint routing problem,a mathematical model was designed,and an improved immune clonal shuffled frog leaping algorithm (IICSFLA) was proposed,which combined immune operator with traditional SFLA.Under the constraints of bandwidth,delay,packet loss rate,delay jitter and energy cost,total energy cost from the source node to the terminal node was computed.The proposed algorithm was used to find an optimal route with minimum energy cost.In the simulation,the performance of IICSFLA with adaptive genetic algorithm and adaptive ant colony optimization algorithm was compared.Experimental results show that IICSFLA solves the problem of multi-constraints QoS unicast routing optimization.The proposed algorithm avoids local optimum and effectively reduces energy loss of data on the transmission path in comparison with adaptive genetic algorithm and adaptive ant colony optimization algorithm.     

Discovering the backbone network with a novel designed ant colony algorithm

Forthe problem that in interactive network,the illegal and abnormal behaviors were becoming more hidden,moreover,the complex relation in real interactive network heightens the difficulty of detecting anomalous entities,an ant colony model was proposed for extracting the backbone network from the complex interactive network.The novel model simulated the relationships among entities based on the theory of path optimization,reduced the network size after quantifying the significance of each flow of information.Firstly,a strategy of initial location selection was proposed taking advantage of network centrality.Secondly,a novel path transfer mechanism was devised for the ant colony to fit the flow behavior of entities.Finally,an adaptive and dynamic pheromone update mechanism was designed for guiding the optimization of information flows.The experimental results show that the proposed model is superior to the traditional ant colony algorithm in both solving quality and solving performance,and has better coverage and accuracy than the greedy algorithm.     

Routing Technology in Wireless Sensor Network Based on Ant Colony Optimization Algorithm

With rapid development of wireless communication, sensor, micro power system and electronic technology, the research on wireless sensor network has attracted more and more attention. The work proposed routing algorithm in wireless sensor network based on ant colony optimization by analyzing routing protocol and utilizing advanced idea. Ant colony optimization algorithm has advantages in implementing local work, supporting multiple paths and integrating link quality into pheromone formation. In routing selection, the work calculated probability that node is selected as the next hop according to pheromone concentration on the path. With characteristics including self-organization, dynamic and multipath, ant colony optimization algorithm is suitable for routing in wireless sensor network. With low routing cost, good adaptability and multipath, the algorithm balanced energy consumption to prolong network lifetime. In terms of simulation and experiments, ant colony algorithm was proved to be suitable for finding optimal routing in wireless sensor network, thus achieving design goal of routing algorithm.     

A Tabu search based routing algorithm for wireless sensor networks

In this paper, a Tabu search based routing algorithm is proposed to efficiently determine an optimal path from a source to a destination in wireless sensor networks (WSNs). There have been several methods proposed for routing algorithms in wireless sensor networks. In this paper, the Tabu search method is exploited for routing in WSNs from a new point of view. In this algorithm (TSRA), a new move and neighborhood search method is designed to integrate energy consumption and hop counts into routing choice. The proposed algorithm is compared with some of the ant colony optimization based routing algorithms, such as traditional ant colony algorithm, ant colony optimization-based location-aware routing for wireless sensor networks, and energy and path aware ant colony algorithm for routing of wireless sensor networks, in term of routing cost, energy consumption and network lifetime. Simulation results, for various random generated networks, demonstrate that the TSRA, obtains more balanced transmission among the node, reduces the energy consumption and cost of the routing, and extends the network lifetime.     

基于栅格模型机器人路径规划的量子蚁群算法

针对复杂环境中移动机器人路径规划问题,提出了一种基于量子-蚁群算法(QACA)融合的路径规划算法。该算法的核心是在蚁群系统(ACS)中引入量子算法中的量子态矢量和量子旋转门来分别表示和更新信息素,增加位置的多样性,加快算法的收敛速度。通过仿真实验表明,该算法可增加算法的随机性,较传统的蚁群算法具有更好的种群多样性,更快的收敛速度和全局寻优能力,即使在障碍物较复杂的环境下,也能迅速规划出一条最优路径。