基于云自适应遗传算法的NoC路径分配研究

路径分配是NoC设计流程中的两个关键步骤之一;路径分配的结果对NoC系统的性能尤其是通讯延时有着很重要的影响;多约束条件下的NoC路径分配问题是NP完全问题,要求出其最优解比较困难,目前常用的方法是利用启发式算法求得其较优解;文中提出一种基于云自适应遗传算法的NoC路径分配解决方案,该算法利用云模型对传统遗传算法加以改进,采取新的方法自动调整遗传算法过程中的交叉概率pc和变异概率pm,将适应度与云模型的3个参数Ex、En、He相互结合,从而达到优化遗传算法的目的;将此算法应用于2D-Mesh拓扑结构的NoC中,以平衡链路负载和联合优化为实验目标,以优化静态通讯分配结果;实验证明,文章所采取的算法在平衡链路负载和联合优化方面均取得了良好的效果。     

Routing with multiple quality-of-services constraints: An approximation perspective

Finding a path that satisfies multiple Quality-of-Service (QoS) constraints is vital to the deployment of current emerged services. However, existing algorithms are not very efficient and effective at finding such a path. Moreover, few works focus on three or more QoS constraints. In this paper, we present an enhanced version of fully polynomial time approximation scheme (EFPTAS) for multiconstrainted path optimal (MCOP) problem. Specifically, we make four major contributions. We first allow the proposed algorithm to construct an auxiliary graph, through which the QoS parameters on each of the finding path can be guaranteed not to exceed the given constraints. Then we adopt a concept, called nonlinear definition of path constraints in EFPTAS for reducing both time and space complexity. Also, we enable EFPTAS to run iteratively to facilitate a progressive refinement of the finding result. In addition to these, we identify some “deployment” issues for proposed algorithm, the essential steps that how and when the EFPTAS takes place are presented. By analyzing the proposed algorithm theoretically, we find that the presented EFPTAS can find a (1+ε)-approximation path in the network with time complexity O(|E||V|/ε) (where |E| is the number of edges and |V| is the number of nodes), which outperforms the previous best-known algorithm for MCOP. We conduct an extensive comparison between the algorithm presented in this paper and previous best-known study experimentally, our results indicate that EFPTAS can find a path with low complexity and preferable quality.     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

A robust critical path in an environment with hybrid uncertainty

In this article, we consider the project critical path problem in an environment with hybrid uncertainty. In this environment, the duration of activities are considered as random fuzzy variables that have probability and fuzzy natures, simultaneously. To obtain a robust critical path with this kind of uncertainty a chance constraints programming model is used. This model is converted to a deterministic model in two stages. In the first stage, the uncertain model is converted to a model with interval parameters by alpha-cut method and distribution function concepts. In the second stage, the interval model is converted to a deterministic model by robust optimization and min-max regret criterion and ultimately a genetic algorithm with a proposed exact algorithm are applied to solve the final model. Finally, some numerical examples are given to show the efficiency of the solution procedure.     

机器人三维路径规划问题的一种改进蚁群算法

本文研究移动机器人三维空间路径规划问题,针对三维空间的复杂地形特点,提出了一种基于改进蚁群算法的路径规划算法。文中首先描述了一种简单有效的环境建模方法,然后给出了算法在信息素呈现、路径点选取、信息素更新以及启发式函数设计等方面的改进方法。仿真结果证明了算法的可行性和可靠性。     

新工科背景下安全工程专业创新型人才培养路径研究 #br#

为实现新工科背景下安全工程专业创新人才培养目标,基于对创新能力内涵的充分理解,从创新精神、创新意识和创新思维三重角度出发,充分利用“校企协同”育人优势,确定安全工程专业创新型人才培养路径,包括创新型课程体系建设、创新型多维实践平台建设、创新型师资队伍建设及多学科交叉创业活动4条路径。将创新能力培养贯穿于安全工程专业人才培养的全过程,为高校实现创新型人才培养提出建议和参考。     

新时代家风建设的思想基础与实践路径探析 #br#

党的十八大以来,以习近平同志为核心的党中央高度重视家风建设在治国理政中的独特作用,深刻体现了家与国辩证统一的战略思维。优秀传统家训文化、马克思、恩格斯家庭观以及红色家风文化是新时代家风建设的思想基础。创造性转化和创新性发展优秀传统家训文化,继承和弘扬红色家风文化,运用好道德和制度两个杠杆,发挥好女性在家风建设中的重要作用是新时代家风建设的重要实践路径。     

高温混合障碍空间中的移动机器人路径规划

为了解决高温场景中移动机器人全局路径规划所面临的安全与效率问题,提出高温热源虚拟障碍的定义,建立混合障碍空间模型,将高温场景中的路径规划问题转化为高温混合障碍空间中考虑路径温度代价和长度代价的多目标优化问题. 改进NSGA-Ⅱ算法,通过选取优秀非可行解扩展种群,提高了种群多样性和进化效率,提出新的交叉和变异概率计算方法. 根据种群进化进程和个体代价函数值调整概率,实现了种群前期搜索能力和后期收敛性的平衡. 仿真所得的最优路径结果表明,该改进算法的路径长度代价虽然比原算法和其他改进算法略有增加,但温度代价大幅降低,更有效地避免了陷入局部最优.     

基于行车轨迹通信算法

随着城市中汽车数量的大增,汽车与汽车之间的通信系统显得尤为重要。因此互联网在行车过程中起到了重要的作用,其中的车载网便扮演着物物通信的功能。本文提出行车过程中复杂特性的分发算法,首先对多个城市中的汽车进行汽车行动路线的统计与分析,其中包含了通信车辆之间的距离、所连车辆的数量分布以及聚集指标,从结论分析得到车与车之间联系的数量呈现一定的指数分布;并且在小范围内,聚集指标比较高。根据上述特性分,推算出一种车辆行进轨迹数据的分发算法,以便于进行车辆与车辆的实时通信,这样就能大大提高车辆行驶的有效性和降低车祸之间的发生。     

基于改进遗传算法的机器人路径规划方法

针对基本遗传算法在机器人路径规划中存在收敛速度慢、易陷入局部最优解的问题,提出一种改进的遗传算法. 在适应度函数中增加带有惩罚项的平滑度函数; 引入精英保留机制,保留每一代最优个体; 自适应调整交叉概率和变异概率,使交叉概率和变异概率随进化次数变化而变化. 利用MATLAB在两种障碍物地图中与其他两种算法进行仿真对比分析,实验结果表明,改进后的算法在路径规划的应用中有效减少了机器人的转弯次数,提高了逃离局部最优路径的能力,寻优能力更强.