异构无线网络负载均衡研究*

针对异构网络中的带宽分配及网络的负载均衡问题,提出了一种异构无线网络负载均衡的联合优化模型,该模型在一定条件下利用用户的带宽进行负载成本和网络效用的建立,并通过求解网络与用户的关联矩阵来得到带宽在网络中的优化分配方案,使异构无线网络达到负载均衡。仿真结果表明,联合优化模型的结果在最小化负载成本以及优化网络总效用性能之间,它让用户通过在不同网络间的切换使网络带宽优化分配、网络负载达到均衡。     

一种基于约束优化的虚拟网络映射方法

虚拟网络映射问题将不同的虚拟网络应用映射到相同的基础设施网络中,这是一个极具挑战性的问题.针对该问题,提出了一种基于约束优化的虚拟网络映射方法,将映射问题分解为节点映射和链路映射两个阶段,其中,前者是将虚拟节点映射到物理节点上,后者将虚拟链路映射到物理路径上,它们都是NP难问题.针对节点映射和链路映射分别提出了node-mapping算法和link-mapping算法.node-mapping算法基于贪婪算法的思想,映射时考虑了物理节点所能提供的资源数量以及物理节点间距离两个因素,该算法能够保证基础设施网络中各节点间的负载相对均衡;同时,通过采用访问控制机制,过滤一些异常的虚拟网络请求,能够有效地提高资源的使用效率.link-mapping算法基于人工智能领域中的分布式约束优化思想,其能够保证得到的解是全局最优的,即映射链路的代价最小.最后,通过模拟实验对该方法进行验证,实验结果表明该方法在求解虚拟网络映射问题时的性能良好.     

ANDSF实现的无线异构网络选择算法

针对目前大多数异构网络选择算法没有考虑网络负载这一问题,提出了一种基于接入网发现和选择功能(ANDSF)实现的异构网络选择算法.对原有ANDSF架构进行优化升级,使之可以随时获取网络侧动态信息;在此架构的基础上,结合业务特点和网络特点,通过层次分析法和代价函数算法为每类业务选择最合适的网络.仿真结果表明:该算法能够提高接入成功率约15％,降低接入时延约50 ms左右,同时一定程度上均衡了网络负载,有利于实现四网协同发展.     

基于博弈论的排队网络业务分配算法

在排队网络中用户最优的用户均衡(UE)业务分配模型和全局代价最小化的系统最优(S0)业务分配模型之间存在着本质上的矛盾,很难同时实现.同时由于网络业务流的动态性和随机性,无法通过确定的模型 对其进行建模.为了解决这两个问题,提出了一种基于博弈论的排队网络业务分配算法.该算法将UE和SO之间的竞争建模为一种博弈,然后使用斯坦科尔伯格主从博弈理论对两者的收益进行均衡,并获得了更加可行的业务分配.仿真比较了该模型和非合作模型下SO及UE的性能,仿真结果验证了该模型的可行性,并且相对确定模型,该模型对网络业务流的建模更精确.     

一种虚拟化深度包检测部署机制

网络功能虚拟化转变了网络架构和网络业务的部署。在网络功能虚拟化架构中,实现虚拟化深度包检测只需在传输路径上进行一次扫描,但高效部署深度包检测功能引擎成为难题。将深度包检测功能部署问题形式化为线性规划问题以满足约束条件,并提出一种基于代价最小的贪婪算法和优化的贪婪算法来解决深度包检测功能部署问题。该算法对部署代价和网络资源代价进行折衷,实现了最小化的部署代价。实验结果表明,所提算法能够实现深度包检测功能部署并取得近似最优解。     

基于链路负载分级的无线Mesh网信道分配算法

由于无线Mesh网络信道分配算法的性能增益与网络的流量负载特点密切相关,在对多射频多信道无线Mesh网络的流量特点进行分析的基础上,提出一种静态信道分配的启发式算法LPFCA。该算法根据无线链路在网络拓扑中的位置信息来估计无线链路的预期负载情况,并对网络中无线链路的预期负载进行量化分级,利用整数线性规划方法对信道分配进行描述并应用目标函数对信道分配进行优化,使网络总的干扰权重最小化。仿真结果表明,相比于现有的算法,该算法在吞吐量上平均提升了18.9%。     

基于禁忌遗传优化的网络拥塞控制算法

为解决网络拥塞问题,提出一种基于禁忌遗传优化的网络拥塞控制算法TSGA。该算法将禁忌搜索和遗传算法相结合,以网络资源消耗最小化和网络负载均衡分布为目标,建立多约束QoS路由优化数学模型。仿真实验结果证明,该算法可实现网络拥塞控制,有效提高网络性能。     

基于多目标粒子群优化的虚拟网络映射算法

在虚拟网络映射中,多数研究只考虑一个映射目标,不能体现多方的利益。为此,将多目标算法和粒子群算法结合,提出了一种基于多目标粒子群优化（PSO）的虚拟网络映射算法（VNE-MOPSO）。首先,在基本的粒子群算法中引入交叉算子,扩大了种群优化的搜索空间;其次,在多目标优化算法中引入非支配排序、拥挤距离排序,从而加快种群的收敛;最后,以同时最小化成本和节点负载均衡度为虚拟网络映射目标函数,采用多目标粒子群优化算法求解虚拟网络映射问题（VNMP）。实验结果表明,采用该算法求解虚拟网络映射问题,在网络请求接受率、平均成本、平均节点负载均衡度、基础设施提供商的收益等方面具有优势。     

多业务CDMA网络参数优化模型与算法

在考虑覆盖率、软切换率、业务信道拥塞率和功率损耗等网络质量指标的基础上,建立多业务CDMA网络参数优化问题的数学模型.通过分析模型的特点,设计了一种基于约束优化遗传算法(COGA)的求解方法,并给出了算法实现的各种关键技术.对一个实际算例进行实验研究,仿真结果表明算法能够有效地配置各种网络参数,网络性能得到提升,优于实际DT(driver test)的优化效果,从而表明所建模型和算法能够为多业务CDMA网络参数优化问题提供快速的解决方案,有效地指导实际的网络管理工作.     

基于最优功率控制和马尔可夫链优化的异构无线网络

为了提高异构无线网络的资源利用率以及网络吞吐量,提出一种基于最优功率控制和马尔可夫链优化的异构无线网络,通过建立异构网络模型对异构网络的业务负载情况进行分析,采用有限容量下的最优功率控制方法来使业务能够选择合适的接入网络,并采用基于马尔可夫链来进行网络吞吐量优化,在对不同形式的网络进行速率分配时,采用了多权重优化进行求解最优解,可以降低网络的阻塞情况,提高网络的利用效率.实验仿真结果及分析表明,该算法在提高网络吞吐量、减少网络利用效率上具有较好的效果.     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

Real-time dynamic optimization of batch systems

In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.     

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.     

An integrated approach to topology,sizing, and shape optimization

Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.     

Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.     

多元插值全局优化算法及其应用

本文介绍一种多元插值逼近和动态搜索轨迹相结合的全局优化算法.该算法大大减少了目标函数计算次数,寻优收敛速度快,算法稳定,且可获得全局极小,有效地解决了大规模非线性复杂动态系统的参数优化问题.一个具有8个控制参数的电力系统优化控制问题,采用该算法仅访问目标函数78次,便可求得最优控制器参数。     

云搜索优化算法

本文将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,建立了一种新的智能优化算法-云搜索优化算法(CSO)。生成与移动的云可以弥漫于整个搜索空间,这使得新算法具有较强的全局搜索能力;收缩与扩张的云团在形态上会有千奇百态的变化,这使得算法具有较强的局部搜索能力;降雨后产生新的云团可以保持云团的多样性,这也是使搜索避免陷入局优的有效手段。实验表明,基于这三点建立的新算法具有优异的性能,benchmark函数最优值的计算结果以及与已有智能优化算法的比较展现了新算法精确的、稳定的全局求解能力。     

Value Creation in a Network: The Role of Pricing and Revenue Optimization and Enterprise Profit OptimizationTM

The Internet has created a virtual upheaval in the structural features of the supply and demand chains for most businesses. New agents and marketplaces have surfaced. The potential to create value and enhance profitable opportunities has attracted both buyers and sellers to the Internet. Yet, the Internet has proven to be more complex than originally thought. With information comes complexity: the more the information in real time, the greater the difficulty in interpretation and absorption. How can the value-creating potential of the Internet still be realized, its complexity notwithstanding? This paper argues that with the emergence of innovative tools, the expectations of the Internet as a medium for enhanced profit opportunities can still be realized. Creating value on a continuing basis is central to sustaining profitable opportunities. This paper provides an overview of the value creation process in electronic networks, the emergence of the Internet as a viable business communication and collaboration medium, the proclamation by many that the future of the Internet resides in “embedded intelligence”, and the perspectives of pragmatists who point out the other facet of the Internet—its complexity. The paper then reviews some recent new tools that have emerged to address this complexity. In particular, the promise of Pricing and Revenue Optimization (PRO) and Enterprise Profit Optimization^TM (EPO) tools is discussed. The paper suggests that as buyers and sellers adopt EPO, the market will see the emergence of a truly intelligent network—a virtual network—of private and semi-public profitable communities.     

粒子群算法在组合优化问题上的研究与发展

粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。     

SEO技术研究

为了利用搜索引擎优化SEO（Search Engine Optimization）技术给网站带来高质量的流量并将其转化为商业利益,理解搜索引擎的算法和排名原理十分必要。通过对网站的结构优化、关键词优化、单页优化、防止被搜索引擎惩罚和挽救被惩罚网站等技术的研究,达到提高网站排名,实现网站的价值目的。