一种SDN架构下业务属性相关的多径路由算法

面对当前网络中流量的增长、业务种类的增多,SDN中多数的路由算法只支持一种QoS参数,没有兼顾对系统调度服务公平性的考虑,然而多参数限制的QoS 明显是NP 难问题,该问题用普通的路由算法难以解决,引进蚁群算法,在蚁群算法的基础上,将链路的时延、分组丢失率引入蚁群算法中,作为算法选择路径的依据,提出一种新的路由算法。该算法在对不同业务属性的数据流分类的基础上,根据网络的实时状况,为不同业务属性的数据流选择合适的路径,对网络中的数据流进行多路径传输。仿真实验表明,该算法能有效地降低数据流的时延、分组丢失率。     

自适应蚁群优化的云数据库动态路径查询

蚁群算法对于解决动态最优路径查询问题具有很强的优势,但蚁群算法中的信息素挥发因子的静态设置容易带来收敛速度不稳定和陷入局部最优解的问题,在云数据库中更是明显。融合了蚁群算法和云数据库,并提出了信息素挥发因子自适应的算法,该算法能够在云中快速、合理地找到所需访问的数据库,减少了云数据库数路由的动态负荷,从而很大程度上提高云计算的效率。     

基于蚁群算法的带平衡约束矩形布局问题的启发式求解

以卫星舱布局问题作为研究背景,求解了带平衡约束的矩形布局问题。采用启发式策略设计了分区域分步布局法,该策略将圆形卫星舱承重板分成4个区域,分区域同步进行布局。当所布矩形和区域都确定时,采用最左最底填充策略进行布局。该方法通过不干涉约束,使布局紧凑,通过控制系统质心的位置,使系统保持平衡。在启发式策略的基础上,设计了蚁群算法搜索优化定位次序,从而得到优化的布局。数值仿真结果表明,该布局方法具有优良的计算性能。     

基于蚁群优化算法的神经网络训练的研究

在ACO算法原理及框架的基础上，将蚁群优化算法引入神经网络的训练中．提出了ACO训练神经网络的基本原理和方法步骤。并与遗传算法、模拟退火算法、加动量项的BP算法相比，仿真结果表明，用ACO算法训练的神经网络具有较快的收敛速度．能够达到较小的均方误差值。     

Self-Configuration of Network Services with Biologically Inspired Learning and Adaptation

This paper proposes a self-organizing scheme based on ant metaheuristics to optimize the operation of multiple classes of managed elements on an Operations Support Systems (OSSs) for mobile pervasive communications. Ant metaheuristics are characterized by learning and adaptation capabilities against dynamic environment changes and uncertainties. As an important division of swarm agent intelligence, it distinguishes itself from centralized management schemes due to its features of robustness and scalability. We have successfully applied ant metaheuristics to the network service configuration process, which is simply redefined as: the managed elements represented as graphic nodes, and ants traverse by selecting nodes with the minimum cost constraints until the eligible network elements are located along near-optimal paths—the located elements are those needed for the configuration or activation of a particular product and service. Although the configuration process is non-transparent to end users, the negotiated SLAs between users and providers affect the overall process. This proposed self-organized learning and adaptation scheme using Ant Colony Optimization (ACO) is evaluated by simulation in Java. A performance comparison is also made with a class of Genetic Algorithm known as PBIL. Finally, the simulation results show the scalability and robustness capability of autonomous ant-like agents able to adapt to dynamic networks.     

求解VRP问题的改进蚁群算法

由于基本VRP算法收敛速度慢,易于陷于局部最优等缺点,现对VRP进行了一些改进,在每次循环中所有蚂蚁都是从起点出发结束于终点,同时在原始的蚁群算法上增加了节点信息素更新策略以及对所有节点改进使得每个节点都有记忆功能,提出了一种基于基本蚁群算法的有节点信息素更新和记忆功能的算法模型.仿真结果表明,基于改进的蚁群算法模型在寻找最优解时表现出很高的效率,优于现有的启发式算法的解,是一种有效的算法,该算法也适用于并行计算和应用.     

基于模糊粒子群优化的计算网格工作调度算法

网格计算是利用网络把分散的计算资源组织起来解决复杂问题的计算模式，工作调度是待解决的主要问题之一。本文提出一种基于模糊粒子群优化的网格计算工作调度算法，该算法利用模糊粒子群优化动态地产生网格计算工作调度的优化方案，使现有计算资源完成所有工作的时间最小化。实验结果表明，与基于遗传算法、模拟退火、蚁群算法的工作调度方法相比，所提出的算法在时间和精度上具有一定的优势。     

基于蚁群——遗传混合算法的高级计划与排产系统应用研究

生产管理系统是制造业中最为复杂的系统之一,高级计划与排产(Advanced Planning and Scheduling以下简称APS)是其重点与难点。由于生产的复杂性与不确定性,当前企业的生产计划编制方法虽多但效果不尽人意,特别是中小企业的现行生产计划与排产系统尤是如此。本文以江门某塑胶丝花行业为背景,以生产计划编制为研究对象,以计划编制的优化为主要内容,结合蚁群算法、遗传算法及其混合应用等算法理论与多种数学及智能信息技术方法,对建立一个能有效提高企业生产计划编制水平的APS系统应用进行了研究。     

一种遗传蚁群系统的研究*

在遗传蚁群系统中,为减少蚂蚁构建路径的时间消耗,引入遗传操作,使得当前迭代中蚂蚁构建的路径部分来自于之前迭代获取的优秀巡回路径的遗传;同时为减少由遗传操作产生的算法停滞的影响、提高算法解的质量,对蚁群构建的路径施行2opt变异操作。通过旅行商问题测试算法性能,并与蚁群系统进行比较。实验表明,遗传蚁群系统搜索效率高,而且解的质量优于蚁群系统。     

Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO

This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network(WSN) which aims to balance the node power consumption and prolong the network lifetime as long as possible.Our approach focuses on energy efficient unequal clustering scheme and inter-cluster routing protocol.On the one hand,considering each node’s local information such as energy level,distance to base station and local density,we use fuzzy logic system to determine one node’s chance of becoming cluster head and estimate the corresponding competence radius.On the other hand,adaptive max-min ant colony optimization is used to construct energy-aware inter-cluster routing between cluster heads and base station(BS),which balances the energy consumption of cluster heads and alleviates the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent.The confirmation experiment results have indicated the proposed clustering algorithm has more superior performance than other methods such as low energy adaptive clustering hierarchy(LEACH) and energy efficient unequal clustering(EEUC).