一种新网络体系结构下的流量分配路由机制

针对CABO(Concurrent Architectures are Better Than One)网络体系结构下虚拟网内部路由协议可定制的特点设计了流量分配路由算法,对于运营带宽敏感业务的虚拟网络采用路由算法解决流量分配问题,提出一种新的指定路由机制,利用改进的多商品流问题作为流量分配路由算法,以流量均衡、收益最大为分配目标;仿真实验结果表明,采用基于指定路由的流量分配路由机制较传统负载均衡路由算法获得的链路利用率更高、网络性能(丢包率、延迟)更好,并可接受更多的业务请求接入.     

一种求解网络最大流问题的算法

随着网络应用的不断深入，人们对网络传输容量和服务质量的要求和期望也越来越高，设计高性能网络成为一项迫切的工作。缓存的配置直接影响网络的时延和丢失率，网络缓存和网络传输容量的合理匹配，能很好提高网络性能。文章简述了网络最大流问题的现状，提出了一种求解网络最大流问题的算法。算法基于MPLS流量工程技术，在实现网络最大流的情况下，同时对M争分支（链路）重新分配流量，达到合理分配网络流量和利用网络资源的目的。仿真结果表明算法是有效的。     

随机流量网络中流量分配控制的多目标优化研究

现实世界的网络比如：物流网络、通信网络、交通网络,电网等可以被抽象成一个随机流量网络。以传输成功率和整个传输所花费的成本为目标,对随机流量网络上流量的分配控制的多目标优化问题进行了研究。采用MPs的概念对问题建模,大大简化了模型的复杂程度。最后提出一个多目标遗传算法,通过实例验证,该算法较好地解决了随机流量网络上的流量分配控制问题。     

基于多Agent系统的分布式群体决策支持系统中问题分配的研究

本文利用分布式人工智能技术提出基于多Agent系统(MAS：Multi—Agent System)的分布式群体决策支持系统框架体系结构。在这个系统中，每个Agent都是一个独立的、具有各自利益的自治体，问题不能被强行地分配给各个Agent，因此本文在研究多Agent系统协调的基础上，给出了问题分配的一种解决方案。     

基于延迟的MPLS网络流级多径负载平衡

提出一种流级的多径负载平衡模型。该模型考虑到实际网络中的背景业务，将每个LSP模拟为M／G／1处理器共享队列，通过成本函数最小化原则，实现多径负载均衡。根据对模型的分析，提出一种应用于MPLS网络中的稳定的负载平衡机制。核心LSR并不参与执行流量工程，根据测量到的延迟按流实现负载分配。仿真结果证明该算法是有效的，能够在多径之间平衡地分配流量，吞吐率得到很大提高，网络资源得到有效利用。     

基于模糊逻辑控制器的通信端口流量控制仿真

5G移动通信输出端口的有效任务量取决于端口输入流量的速率和流量分配缓冲区大小，通信多端口并行流量过大会导致网络压力过大，流量偏小不利于数据的高效传输。因此，需要对多端口的并行流量实现合理控制。提出一种5G移动通信多端口并行流量控制方法。对5G移动通信多端口性能建模，根据性能优势对多端口各条链路流量完成并行分配。利用建立的模糊逻辑控制器获取每条链路的网络拥堵反馈情况，并通过模糊逻辑控制器判断是否需要丢弃或标记输入的违约流量信元，以此保证每条链路网络流量的畅通性，实现并行流量的控制。实验结果表明，研究方法下并行流量达到2500Mbs时，CPU占用率低于50%。当链路数不断增加时，研究方法的总端口平均带宽可保持在500M,以下，端口带宽最大利用率可达95%以上，平均时延低于20ms。     

飞机液压能源系统流量分配分析及优化

针对多液压泵并联的飞机液压能源系统,主、辅助液压泵的关键参数未充分分析和优化时辅助液压泵输出流量常大于主液压泵输出的问题,提出了优化方法,建立了某型飞机液压能源系统多泵并联流量分配模型,对优化方法进行了仿真分析,并在"铁鸟"试验台上进行了试验验证,结果表明优化方法有效,最后给出了影响主液压泵和辅助液压泵流量分配关系的主要因素和参数优化方法.     

遗传算法在计算机通信网容量分配和流量控制中的应用

遗传算法是一种模仿自然选择和遗传机制的优化算法。本文从网络总费用的角度来分析通信网中的容量分配和流量控制问题，采用遗传算法对计算机通信网进行容量分配和流量控制，并解释了对于实现这一算法中一些问题的考虑，最后给出了算法的实验结果，实验结果表明，我们的方法是一种十分有效的方法。     

浙江师大选课系统负载平衡研究

通过分析选课系统的数据流量可知，造成系统运行不稳定的主要因素是硬件不能快速响应SQL请求和网络数据流量过高。解决这两个问题而设计的具体方案是增加CPu、内存及网络适配器以提高数据库管理软件的处理速度；通过ASP将数据流量分配到两个不同的网络适配器上以均衡网络流量，实现负载平衡。文章着重阐述利用ASP程序实现负载平衡、提高系统性能的解决方案。     

求解人力资源分配问题的多目标和声搜索算法

人力资源分配问题是将若干个人力资源合理分配给若干个工作任务,从而达到人力生产效率最大化与人力生产成本最小化。文中提出一种改进的多目标和声搜索(MOIHS)算法来求解人力资源分配问题。MOIHS算法是通过改变记忆考虑的选择机制与微调概率来改进基本的和声算法提高算法收敛稳定性,并采用快速非支配排序方法与建立动态拥挤的距离来获得一个分布良好的Pareto解集。在求解人力资源分配问题时,同时优化人力生产成本最小化与效率最大化两个目标,最后通过一个实例可以得到在解决该问题上多目标改进和声搜索算法优于多目标遗传算法,求出的解集也具有良好的分布性。     

A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before.     

变分布的量子行为粒子群优化算法求解工程约束优化问题

针对工程形状设计领域中带有多个约束条件的非线性设计优化问题,提出了一种自适应的基于高斯分布的量子行为粒子群优化(AG-QPSO)算法。通过自适应地调整高斯分布,AG-QPSO算法能够在搜索的初始阶段有很强的全局搜索能力,随着搜索过程的进行,算法的局部搜索能力逐渐增强,从而满足了算法在搜索过程不同阶段的需要。为了验证算法的有效性,在压力容器和张弦设计问题这两个工程约束优化问题上进行50轮独立实验。实验结果表明,在满足所有约束条件的情况下,AG-QPSO算法在压力容器设计问题上取得了5890.9315的平均解和5885.3328的最优解,在张弦设计问题上取得了0.01096的平均解和0.01096的最优解,远优于标准粒子群优化(PSO)算法、具有量子行为的粒子群优化(QPSO)算法和高斯量子行为粒子群(G-QPSO)算法等现有的算法的结果,同时AG-QPSO算法取得的结果的方差较小,说明该算法具有很好的鲁棒性。     

A new iterated fast local search heuristic for solving QAP formulation in facility layout design

In facility layout design, the problem of locating facilities with material flow between them was formulated as a quadratic assignment problem (QAP), so that the total cost to move the required material between the facilities is minimized, where the cost is defined by a quadratic function. In this paper, we propose a modification to iterated fast local search algorithm (IFLS) with a new recombination crossover operator and the modified IFLS is addressed as NIFLS. The ideas we incorporate in the NIFLS are iterated self-improvement with evolutionary based perturbation tool, which includes (i) recombination crossover as perturbation tool and (ii) self-improvement in mutation operation followed by a local search. Three schemes of NIFLS are proposed and the obtained solution qualities by the three schemes are compared. We test our algorithm on all the benchmark instances of QAPLIB, a well-known library of QAP instances. The performance of proposed recombination crossover with sliding mutation (RCSM) scheme of NIFLS is well superior to the other two schemes of NIFLS.     

Combinatorial Algorithms for the Unsplittable Flow Problem

We provide combinatorial algorithms for the unsplittable flow problem (UFP) that either match or improve the previously best results. In the UFP we are given a (possibly directed) capacitated graph with n vertices and m edges, and a set of terminal pairs each with its own demand and profit. The objective is to connect a subset of the terminal pairs each by a single flow path subject to the capacity constraints such that the total profit of the connected pairs is maximized.We consider three variants of the problem. First is the classical UFP in which the maximum demand is at most the minimum edge capacity. It was previously known to have an O(√m) approximation algorithm; the algorithm is based on the randomized rounding technique and its analysis makes use of the Chernoff bound and the FKG inequality.We provide a combinatorial algorithm that achieves the same approximation ratio and whose analysis is considerably simpler. Second is the extended UFP in which some demands might be higher than edge capacities. Our algorithm for this case improves the best known approximation ratio. We also give a lower bound that shows that the extended UFP is provably harder than the classical UFP. Finally, we consider the bounded UFP in which the maximum demand is at most 1/K times the minimum edge capacity for some K > 1. Here we provide combinatorial algorithms that match the currently best known algorithms. All of our algorithms are strongly polynomial and some can even be used in the online setting.     

带有时间和费用双重限制的网络容量扩充问题

该文将网络容量定义为最大s-t流的流量,建立了带有时间和费用双重限制下的网络容量扩充问题的一般模型。通过网络变换,将带有时间限制的容量扩充问题转化为线性最小费用流问题,并给出了具体证明和求解容量扩充问题的算法。该模型和算法不仅适用于各种情形的容量扩充问题,而且还可应用于网络流规划。最后通过具体例子的求解,说明了模型和算法的正确性和有效性。     

基于绝对贪心和预期效率的0-1背包问题优化

With analysis and research the traditional theory of solving knapsack problem, and then to optimize enigmatical knapsack problems, this paper proposed a new algorithm based on the absolute greedy and expected efficiency strategy. Through the three sets of simulation experiments, it shows that the algorithm can solve a class of knapsack problems and it is superior to greedy algorithm, backtracking algorithm, dynamic programming algorithm, branch and bound algorithm. The convergence speed is ten times as the artificial glowworm swam algorithm by comparing with these two algorithms. Finally, it analyzed discrete degree of data and determined an adaptive scope of the algorithm.     

Interior point methods for power flow optimization with security constraints

This paper deals with power flow optimization with security constraints, focusing on the problem of short‐term hydroelectric scheduling, called predispatch. Since the energy demand varies throughout the day, the generation must satisfy daily targets, established by long‐term scheduling models. This study considers that the hydroelectric plants and transmission systems must provide an optimal flow of energy under security constraints that allow meeting energy demands for normal operating conditions and when disturbances happen. Algebraic techniques are used to exploit the sparse structure of the problem, targeting the design of an interior point algorithm, efficient in terms of robustness and computational time. Case studies compare the proposed approach with a general purpose optimization solver for quadratic problems and an algorithm for the predispatch problem that does not consider security constraints. The results show the benefits of using the method proposed in the paper, obtaining optimal power flow that is suitable to consider contingencies, with numerical stability and appropriate computational time.     

分布式问题求解方案的模糊综合评价模型及其算法实现

为解决现阶段大型复杂工程方案评价缺少实用理论分析工具的问题,文章应用“主因素突出型”和“因素加权型”两种多阶段模糊评判模型,实现了一种适合大型工程分布式协同设计的方案模糊综合评价算法。文中首先分析了大型工程分布式问题求解过程,提出了一种面向工程的分布式问题求解逻辑结构;其次详细分析了多阶段方案模糊评价模型以及模糊综合评价算法的流程框图及其实现过程。     

基于遗传算法求解TSP问题的一种算法

主要研究了用遗传算法求解TSP问题。阐述了简单遗传算法的设计方法、基本原理和基本步骤。描述了简单遗传算法在TSP问题中的应用现状。根据种群个体的多样性和分布情况,提出了判定遗传算法的截止代数。简单遗传算法具有易于陷入局部最优解、收敛速度慢的特点,针对这些特点,通过改进交叉算子,加入初始化启发信息,提高了遗传算法解的精度和收敛性。     

SID-GA: An evolutionary approach for improving observability and redundancy analysis in structural instrumentation design

In this paper the core of a genetic algorithm designed to define a sensor network for instrumentation design (ID) is presented. The tool has been incorporated into a decision support system (DSS) that assists the engineer during the ID process. The algorithm satisfactorily deals with non-linear mathematical models, and considers four design objectives, namely observability, cost, reliability and redundancy, exhibiting properties that were either never addressed by existing techniques or partially dealt with in the literature. Its performance was tested by carrying out the ID of an ammonia synthesis industrial plant. Results were statistically analysed. A face validity study on the fitness function’s soundness was also assessed by a chemical engineer with insight and expertise in this problem. The technique performed satisfactorily from the point of view of the expert in ID, and therefore it constitutes a significant upgrading for the DSS.