计算机网络服务质量优化方法探析

对当前计算机网络服务的质量进行提高,优化方法在其中的地位非常关键,是提高计算机网络服务质量保证机制的理论依据,传统的启发式网络设计方法存在的一些缺陷,在优化方法中都得到了有效解决。以优化方法为基础的机制设计以及性能评价属于现在网络服务领域中研究中的前沿。该文还介绍了计算机网络模型优化算法的通用表达形式,还将不同的类型进行了分类,最后对不同优化算法的实施方案进行了分析对比,总结出它们之间的联系与区别。     

网络编码中的优化问题研究

简要回顾了网络编码的理论研究,阐述了网络编码优化问题研究的重要意义.在介绍网络信息流模型的基础上,针对优化问题的陈述、特点和解法,结合最新的研究成果进行了综述.根据优化目标的不同,优化问题可分成4类:最小花费组播,无向网络的最大吞吐率,最小编码节点、编码边,基于网络编码的网络拓扑设计.归纳了问题的求解性质,对其中的(线性或凸)规划问题总结了求解的一般方法,对NP完全问题讨论了最新的启发式算法及其设计难点.同时,展望了未来的发展方向.     

复杂网络能控性鲁棒性研究进展

研究复杂网络能控性鲁棒性对包括社会网络、生物和技术网络等在内的复杂系统的控制和应用具有重要价值. 复杂网络的能控性是指: 可通过若干控制节点和适当的输入, 在有限时间内将系统状态驱动至任意目标状态. 能控性鲁棒性则是指在受到攻击的情况下, 复杂网络依然维持能控性的能力. 设计具有优异能控性鲁棒性的复杂网络模型和优化实际网络的能控性鲁棒性一直是复杂网络领域的重要研究内容. 本文首先比较了常用的能控性鲁棒性定义及度量, 接着从攻击策略的角度分析了3类攻击的特点及效果, 包括随机攻击、基于特征的蓄意攻击和启发式攻击. 然后比较了常见模型网络的能控性鲁棒性. 介绍了常用优化策略, 包括模型设计和重新连边等. 目前的研究在攻击策略和拓扑结构优化方面都取得了进展, 也为进一步理论分析提供条件. 最后总结全文并提出潜在研究方向.     

在线社交网络社区的启发式挖掘框架

指出基于全局优化的社区挖掘方法的不足,给出OSNs网络及其社区挖掘的形式定义,提出一个启发式社区挖掘框架,在此框架下对包括LWP,Clauset,Schaeffer,Papadopoulos,Bagrow与Chen在内的6种启发式社区挖掘算法进行分析比较.通过3个真实OSNs网络的实验比较,验证了启发式社区挖掘框架的可行性,在结果社区有效性与时间效率上对6种启发式算法进行比较,实验结论为网络社区挖掘的工程实践与理论研究提供了借鉴.     

IP网络中的拥塞控制

以拥塞控制机制演化的历史为线索，回顾了IP网络中已有的拥塞控制技术；总结了TCP流量控制、端到端流量控制和中间节点增强机制等各研究子方向中需要解决的问题；重点分析了主动队列管理这一热点领域中已有策略和算法的优缺点，并在此基础上，归纳和阐述了大多数算法所采用的启发式设计加仿真试验验证的模式存在的不足；提出了应用控制理论中的分析和设计方法研究网络拥塞控制的思路，并给出几个有意义的研究方向。     

一种基于景观特征的浮点数编码遗传算法研究

遗传算法作为一种适应性搜索技术得到了普遍的应用,但其搜索效率不如启发式搜索.已有研究者将启发式知识用于二进制编码遗传算法,但浮点数编码在函数优化和约束优化领域明显有效于其它编码.本文基于算法运行时的景观特征作为启发式知识,用于浮点数编码遗传算法,力求提高其搜索效率、增强其局部搜索能力、拓展其应用领域.本文的理论研究和实验结果表明,将景观特征用于浮点数编码遗传算法,理论是可靠的,方法是可行的.     

一种改进的计算机网络k-划分优化遗传算法

本文运用无向图多划分优化的方法研究计算机网络k-划分优化问题，结合问题本身的特点，设计了一种启发式遗传算法，从适应度函数设计，遗传操作算子以及参数选取等方面对经典遗传算法进行了改进，实验研究验证了算法的正确性和高效性。     

基于生物网络的智能控制与优化研究进展

生物网络在生物系统中扮演着维持动态平衡的重要角色,受其启发可提出多种新颖的智能控制和优化的理论与技术.为此,对这一研究领域进行了较系统的综述,主要包括受免疫网络、神经内分泌网络、神经内分泌免疫网络、生物整体网络等研究领域启发而形成的控制理论、技术及其应用方法.未来基于生物网络的智能控制与优化,将更加注重对上述生物网络间互连融合以及协同机制的进一步挖掘,及其在控制理论和工程方面的创新应用,同时充分吸收生物学最新成果,抽象并产生多种新型生物智能控制器及其控制方法,以更好地满足复杂系统对智能系统的应用需求.     

求解仿真优化问题的知识型启发式搜索方法

仿真优化研究基于仿真的目标优化问题,已经成为系统仿真和运筹学等领域共同关注的热点和前沿课题.针对离散事件动态系统仿真优化中的难点问题,提出了一种全新的知识型启发式搜索方法.采用知识模型和启发式搜索模型相结合的集成建模思路,以启发式搜索模型为基础,同时突出知识模型的作用,将启发式搜索模型和知识模型进行优化组合、优势互补,以提高启发式搜索技术的效率.基于期望值模型的数值仿真,验证了方法的可行性和有效性.仿真结果表明,无论是求解质量还是求解速度,都优于其它几种现有方法.研究结果表明,将知识模型合理地嵌入到现有启发式搜索方法中,可以有效地解决复杂的仿真优化问题.     

计算机网络服务质量保证机制优化评价方法分析与展望

从理论实际出发,阐述了应用优化理论辅助网络系统的设计,进而为计算机网络服务质量保证提供机制优化的评价方法,建立起更为智能的网络应用体系,保证网络系统的平稳运行,提高网络服务质量。     

An efficient integer programming formulation for the assignment of base stations to controllers in cellular networks

In mobile networks, the assignment of base stations to controllers when planning the network has a strong impact on network performance. In a previous paper, the authors formulated the assignment of base stations to packet controllers in GSM-EDGE Radio Access Network (GERAN) as a graph partitioning problem, which was solved by a heuristic method. In this paper, an exact method is presented to find optimal solutions that can be used as a benchmark. The proposed method is based on an effective re-formulation of the classical integer linear programming model of the graph partitioning problem, which is solved by the branch-and-cut algorithm in a commercial optimization package. Performance assessment is based on an extensive set of problem instances built from data of a live network. Preliminary analysis shows some properties of the graphs in this problem justifying the limitations of heuristic approaches and the need for more sophisticated methods. Results show that the proposed method outperforms classical heuristic algorithms used for benchmarking, even under runtime constraints. Likewise, it improves the efficiency of exact methods previously applied to similar problems in the cellular field.     

自底向上优化神经网络的方法

引荐了一种自动优化神经网络的新方法。这种启迪方法综合采用了相关有效算法,通过快速自底向上构造神经网络算法,可以获得优化结构的神经网络,即时选定参数算法动态优化神经网络的学习参数,并且快速交叉校验算法为解决过度适应问题提供了捷径。实验证明,这种启迪方法能自动有效地优化神经网络,与其它算法相较而言,具有更好的归纳性能、优化的网络结构和更快的学习速度。     

On the Use of an Enhanced Hopfield Neural Model to Solve FMS Performance Optimization Problem

The performance of a Flexible Manufacturing System (FMS) is generally linked to its productivity and is often limited by poor use of available resources. One of the main goals in the automated factory environment is, therefore, the exploitation of resources to the full, in such a way as to optimize productivity. As widely documented in literature, this is a hard task on account of its computational complexity. For this reason a number of heuristic techniques are currently available, the best known of which are based on Event Graphs, which are a particular class of Petri Nets. The paper proposes a performance optimization technique which, although it is based on Event Graphs, applies algorithms which are different from traditional heuristic ones. More specifically, a novel neural model is used to solve the optimization problem. The neural model was obtained by making significant changes to a network that is well known in literature: the Hopfield network. The solution proposed is an original one and features several advantages against the most known heuristic approaches to the problem, the most important of which is the possibility of optimal or close to optimal solutions in a polynomial time, proportional to the size of the FMS. In addition, the possibility of simple, economical hardware implementation of the neural model favours its integration in the automated factory environment, allowing real-time supervision and optimization of productivity. The aim of the paper is to present the new neural model and its use in optimizing the performance of FMSs. A comparison of the neural approach with classical heuristic solutions and its real-time calculation capability, will also be treated in the paper.     

基于核心化技术的点覆盖改进算法

点覆盖是一个著名的NP难解问题，在通信网络和生物信息学等领域具有重要应用。针对点覆盖的研究主要集中在启发式或近似算法，其主要不足是无法实现全局最优。核心化是处理难解问题的一种新方法。提出融合启发式操作和核心化操作的算法框架，利用核心化技术进行点覆盖启发式算法优化。核心化操作挖掘出全局最优的顶点集，而启发式操作改变网络拓扑，使下一轮核心化操作能够继续，两者交叉执行实现解精度优化。实验结果表明，提出的算法在不同网络中均能实现不同程度的优化，在几乎所有稀疏网络实例中获得了最优解。     

Heuristic approaches to the asymmetric travelling salesman problem with replenishment arcs

The Asymmetric Travelling Salesman Problem with Replenishment Arcs (RATSP) is a new class of problem arising from work related to aircraft routing. This paper describes the problem and presents heuristic approaches for solving the RATSP. We use simulated annealing to obtain feasible solutions, and hence, upper bounds on the optimum value, and solve a Lagrangean dual problem using a subgradient optimization method to obtain lower bounds. While previous methods failed to obtain optimal solutions to some problem classes after 2 h of computation time, with average gaps ranging from 15% to 30%, our heuristic approaches take only 15–20 min to obtain feasible solutions, with gaps of less than 3%. We give computational results comparing these approaches.     

A Multiobjective Optimization Model for Dynamic Reconfiguration of Ring Topologies with Stochastic Load

In this paper we describe a heuristic procedure to generate solutions to a multiobjective stochastic, optimization problem for a dynamic telecommunications network. Generating Pareto optimal solutions can be difficult since the optimization problem is computationally challenging and moreover the network must be reconfigured in near real time, for example, to recover connectivity after a severe weather event. There are two main contributions of this paper. First, we show mathematically how a certain deterministic equivalent optimization problem can be solved instead of the stochastic one, thus facilitating computations. Second, we test our heuristic under a wide set of simulated conditions (e.g., atmospheric obscuration due to differing levels of cloud cover, different demand patterns) and show that it achieves near Pareto optimality in a short amount of time.     

Neural techniques for combinatorial optimization with applications

After more than a decade of research, there now exist several neural-network techniques for solving NP-hard combinatorial optimization problems. Hopfield networks and self-organizing maps are the two main categories into which most of the approaches can be divided. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions, and the lack of generalizability of the self-organizing approaches (being only applicable to Euclidean problems). The paper proposes two new techniques which have overcome these pitfalls: a Hopfield network which enables feasibility of the solutions to be ensured and improved solution quality through escape from local minima, and a self-organizing neural network which generalizes to solve a broad class of combinatorial optimization problems. Two sample practical optimization problems from Australian industry are then used to test the performances of the neural techniques against more traditional heuristic solutions.     

视频服务器网络中影像对象映射问题的研究

视频服务器网络中的影像对象映射问题是一种新的组合优化问题.服务器网络可以建立在基于局域网的工作站网络之上,也可以建立在广域网之上.基于对用户的服务请求模式、服务器网络的存储容量和通信带宽等因素的综合考虑,研究了服务器网络中影像对象映射问题,利用局部搜索算法给出了一套对该映射问题的解决方案.然后用一套基准集实例对给出的算法集进行验证.结果表明,在较短的计算时间内,该算法可以得到近似最优解的方案.     

Adaptive volume constraint algorithm for stress limit-based topology optimization

Homogenization or density-based topology optimization methods work by distributing a fixed amount of material to the most effective areas of the design domain so as to create an optimal structural configuration that meets the minimum compliance criteria. These topology optimization methods generally cannot control the maximum stress levels of the structure; therefore, the smoothened optimum structure is not guaranteed to be ready for immediate use. This can be because it is either unsafe if the maximum stress at this structure exceeds the strength limit, or over designed if the maximum stress is far below the stress limit. Difficult and complex shape optimization must then be done to obtain a minimum-weight structure that meets the maximum stress constraint. This paper proposes an adaptive volume constraint (AVC) algorithm, a heuristic approach, in place of traditional topology optimization methods so that the maximum stress in the optimal structural configuration will be below the predefined stress limit and the smoothened structure will be directly applicable. In order to test the applicability and robustness of the AVC algorithm, topology optimization using both a traditional fixed volume constraint and an AVC are tested on a number of configuration design problems. To further illustrate the usefulness of the AVC algorithm, shape optimizations at the maximum stress constraint are also conducted on the smooth structural models by both optimization approaches on an identical problem set.