蚁群-遗传算法的无线传感器网络路径优化

为了提高无线传感器网络路径优化效率,快速找到最优路径,提出基于蚁群-遗传算法的传感器路径优化方法.利用遗传算法的快速全局搜索能力和蚁群算法的正反馈机制,实现了两种算法的融合.仿真结果表明,蚁群-遗传算法在时间和性能上都优于单独的蚁群算法和遗传算法,能快速找到无线传感器网络最优路径,有效延长了网络的生命周期.     

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。     

Solution to the problem of ant being stuck by ant colony routing algorithm

Many ant colony routing (ACR) algorithms have been presented in recent years, but few have studied the problem that ants will get stuck with probability in any terminal host when they are searching paths to route packets around a network. The problem has to be faced when designing and implementing the ACR algorithm. This article analyzes in detail the differences between the ACR and the ant colony optimization (ACO). Besides, particular restrictions on the ACR are pointed out and the three causes of ant being-stuck problem are obtained. Furthermore, this article proposes a new ant searching mechanism through dual path-checking and online routing loop removing by every intermediate node an ant visited and the destination host respectively, to solve the problem of ant being stuck and routing loop simultaneously. The result of numerical simulation is abstracted from one real network. Compared with existing two typical ACR algorithms, it shows that the proposed algorithm can settle the problem of ant being stuck and achieve more effective searching outcome for optimization path.     

蚁群优化和能量有效的Ad Hoc路由协议

为了延长Ad Hoc网络的生存周期,提出了一种基于蚁群优化和能量有效的Ad Hoc网络多路径动态路由算法ACOERA。该算法根据路径的有效能量率进行路由选择,路径建立后通过蚁群优化算法动态收集路径信息,并对路由表进行更新。仿真结果表明,该算法能有效延长网络生存时间,增强通信网络的自适应能力。     

Path planning algorithm for WCE with joint energy replenishment and data collection based on multi-objective optimization

Considering limited energy of the wireless charging equipment (WCE) in wireless rechargeable sensor network,an energy replenishment strategy and a data collection strategy are designed.On the basis of these,a path planning model for WCE with functions of joint energy replenishment and data collection based on multi-objective optimization is constructed with two optimization objectives,maximizing the total energy utility of WCE and minimizing the average delay of data transmission of all the sensor nodes in the network.To deal with it,a multi-objective ant colony optimization algorithm based on elitist strategy was proposed,where the state transition strategy and the pheromone updating strategy were improved.Then,the Pareto set was obtained in terms of this multi-objective optimization problem.The parameter setting of ant colony algorithm’s effects on the proposed algorithm were analyzed under 20 sensor nodes.50 groups of contrastive experiments show that the average number of energy utilization obtained by ES-MOAC algorithm is 4.53% higher than that of NSGA-II algorithm.The average number of average delay of all node data transmission obtained by ES-MOAC algorithm is 5.12% lower than that of NSGA-II algorithm.     

基于蚁群优化的高性能拥塞控制路由机制

为了有效地解决偏远地理区域通信网络存在的网络拥塞严重、数据成功传输率低、数据冗余率高以及网络整体性能不佳等问题,通过考虑网络节点运动区域性特点,基于蚁群优化机制,设计出一种新型的容延容断网络 (DTN) 拥塞控制路由优化算法。该算法结合蚁群优化机制中的信息素因子,在同一对源、目的网络节点之间进行多次数据信息传输操作。在数据信息传输方向上,获取各个网络节点的中转跳数平均值,评估各个网络节点的中转价值;参考蚁群优化机制中的启发值因子,将网络节点的中转价值与剩余存储容量相关联,构成网络节点作为中转节点的评定参数,选取评定参数最大的网络节点完成其中转任务。实验表明：该算法有效控制了网络拥塞,提高了数据成功传输率,降低了数据信息冗余率,使网络整体性能得到进一步优化。     

蚁群算法收敛性验证系统的研究与实现

蚁群算法是一种新型的仿生优化算法,具有较强的鲁棒性、优良的分布式机制、并行性以及正反馈等特点。目前蚁群算法已涉及众多应用领域,在解决复杂优化问题上具有较多优越性。文中深入研究了蚁群算法的性能及机制,分析了参数对算法性能的影响。在理论研究的基础上,实现了蚁群算法的仿真实验;通过Java绘图界面形象完整地展现出整个收敛的过程,验证了蚁群算法的收敛性;通过对参数的调试、组合,得到了最佳的收敛效果。该系统的实现对今后收敛性的研究打下了基础。     

复杂路网条件下的物流配送方案研究

为了有效提高在复杂路网条件下物流配送路径优化效率,文中提出了采用蚂蚁算法复杂路网条件下物流配送路径优化算法.算法运用Repast Simphony仿真平台和GIS技术,对复杂路网条件下的城市物流配送进行仿真实验.实验结果表明,该物流配送方案可以较好地解决了复杂路网条件下的车辆路径问题(VRP)难题,同时仿真实验模型简单,具有非常优越的可扩展性能.     

融合QPSO思想的多行为蚁群算法在QoS路由问题上的应用

在解决QoS(quality of service)单播路由问题上,针对蚁群算法缺点,提出了一种融合量子粒子群算法(QP-SO)思想的多行为蚁群算法.该算法采用QPSO作为前期搜索,根据各粒子历史最优值来初始化路径信息素浓度,后期利用多行为蚁群算法来优化路径.仿真结果表明:该算法寻优能力强,可靠性高,是解决QoS路由问题的有效方法.     

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.     

An improved distance‐based ant colony optimization routing for vehicular ad hoc networks

Vehicular ad hoc network (VANET) has earned tremendous attraction in the recent period due to its usage in a wireless intelligent transportation system. VANET is a unique form of mobile ad hoc network (MANET). Routing issues such as high mobility of nodes, frequent path breaks, the blind broadcasting of messages, and bandwidth constraints in VANET increase communication cost, frequent path failure, and overhead and decrease efficiency in routing, and shortest path in routing provides solutions to overcome all these problems. Finding the shortest path between source and destination in the VANET road scenario is a challenging task. Long path increases network overhead, communication cost, and frequent path failure and decreases routing efficiency. To increase efficiency in routing a novel, improved distance‐based ant colony optimization routing (IDBACOR) is proposed. The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO). The modified ACO method is a metaheuristic approach, motivated by the natural behavior of ants. The simulation result indicates that the overall performance of our proposed scheme is better than ant colony optimization (ACO), opposition‐based ant colony optimization (OACO), and greedy routing with ant colony optimization (GRACO) in terms of throughput, average communication cost, average propagation delay, average routing overhead, and average packet delivery ratio.     

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.     

Research on mapping algorithm of virtual network oriented to SDN

In order to solve the problem of virtual network mapping,a mapping method based on ant colony hybrid genetic algorithm was put forward under SDN environment,which established a linear programming model for virtual network mapping,and divided the mapping process into node mapping and link mapping.Firstly,the fusion algorithm was adopted,in which virtual nodes were mapped to physical nodes.Then the shortest path algorithm was used to map the virtual link to a physical link.On this basis,the acceptance ratio of virtual network requests can be improved.Simulation experiment results show that acceptance rate of virtual network requests can be increased by 10% efficiently using the ant colony hybrid genetic algorithm,compared with existing mapping algorithms D-ViNE,RW-BFS and R-ViNE.Further more,proposed method can greatly improve the average utilization rate of nodes and links and the ratio of the mapping income to cost.     

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。     

基于改进蚁群算法的可信服务发现

针对非结构化P2P网络中的服务发现问题,提出了一种基于改进蚁群算法的可信服务发现方法。该方法在传统蚁群算法基础上应用若干新的策略控制蚁群的行为,如动态蚁群策略、子蚂蚁策略、服务节点的信誉评估策略以及蚂蚁的恶意节点惩罚策略等, 高信誉服务节点在蚂蚁的推荐下进行邻居更换和自组织。 这些策略和方法为蚁群算法在分布式环境下服务发现的应用研究提供了一种新思路。实验结果表明, 本算法在各种常见复杂网络环境下仍可保证较高的可信服务查准率和更高的综合服务发现效率, 通过对实验数据的分析和解释得到了若干有益的结论,为进一步的研究工作奠定了基础。     

基于Openflow网络的高可靠性虚拟网络映射算法

该文基于Openflow网络提出了具有容错能力的虚拟网络映射模型,并且采用蚁群算法对其进行求解。针对虚拟网络的故障恢复机制,提出了区分用户优先级的故障恢复算法(Priority_Diff),该算法为用户提供不同的网络可靠性级别,对高级用户采用提前映射的备份路径替代故障链路,对低级用户重新映射故障链路;设计了故障备份链路重映射(BLRM)算法,将故障链路中的备份资源迁移到相邻链路,增强了备份链路的可用性。最后,通过仿真实验,从虚拟网络故障修复率、虚拟网络成功运行率和工作链路资源利用率3个方面验证了所提算法的优越性。     

基于蚁群的无线电源管理网络路由算法

蚁群算法(Ant Colony Algorithm,ACA)是一种新型的基于群体的仿生算法。采用蚁群算法实现了对无线电源管理网络的路由优化,为远程设备管理提供了新的解决思路。对原始蚁群算法进行了改进,提出了一种多蚁群的优化算法。基于蚁群算法及其并行搜索最优的特征,通过蚁群聚类和动态调整网络优化参数的方法,可作为一种面向无线电源管理网络的路由算法,经过仿真验证了该算法的有效性。     

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.     

面向城市交通网络的一种新型动态路径寻优方法

 针对静态路径寻优方法中没有考虑到交通流随时间变化的问题,本文提出一种基于交通流量预测的路径寻优方法.首先,从实际交通路网本身的特点和人类对路网的认识出发,构建以"道路"为基本元素的新型路网模型;其次,采用基于单变量时间序列的预测方法对路网上各路段未来一段时间内的道路交通流量进行预测,并根据预测结果估计出行者在未来时段各路段上的交通代价;再次,通过估计的交通代价把车辆从起点到该路段时间内的道路交通变化状况融入改进A^*算法的路径搜索过程,从而建立一种高效的动态改进A^*路径搜索算法;最后,实例表明预测交通流量与实际交通流量具有很好的拟合度,路径搜索结果能够对出行者起到诱导作用,提高出行者行驶效率.     

车载自组织网中基于蚁群算法的延迟感知路由协议

针对城市道路环境下车载自组织网( VANETs)中通信性能下降以及数据传输失败的问题,提出了一种基于蚁群算法的延迟感知路由( ACDR)协议。首先,建立双向车道的数学延迟模型;然后,根据提出的端点十字路口( EI)的概念,ACDR利用蚁群优化( ACO)寻找最佳路线,其中前向蚂蚁根据本地路段延迟以及当前十字路口与目的节点的端点十字路口之间的全局时延来选择路径,后向蚂蚁则负责在返回路径时更新信息素,同时,相邻十字路口之间利用贪婪转发算法进行数据包的传递。最后仿真比较了ACDR协议与连通性感知路由( CAR)协议的性能,结果表明提出的ACDR协议的数据包的传输延迟小,丢包率低,通信性能好。