战时血液保障组织资源优化的遗传算法模型

资源优化配置是战时血液保障组织系统系统中的一个重点，也是一个难点问题。本文在对传统算法模型在解决优化配置问题上的缺陷进行分析和研究的基础上，提出一个基于并行遗传算法的资源优化配置算法模型。考虑到血液保障组织系统系统中资源的特殊性．编码方式采用二进制编码，并行模型为粗粒度并行模型。该模型不但可以在血液全生命周期中的每个环节选1个或多个机构参加，而且不会遗漏任何一个可行的优化配置方案。最后通过具体实例以说明该模型的有效性。     

基于扩展CGSPN的维修保障系统仿真评估与优化方法

该文在原有CGSPN模型的基础上,引入了随机着色的概念,并将扩展的CGSPN模型应用于考虑装备层次结构的维修保障系统建模中。通过基于该模型的仿真方法可以得到系统的维修保障能力评估指标,该文还给出了综合考虑修理资源与备件资源的资源优化配置算法,通过该算法,可以得到基于仿真方法的资源优化问题的满意解。最后,给出了一个算例来说明该方法的合理性。     

QoS保障的LTE-A飞蜂窝资源块分配与MCS选择研究

针对LTE-A飞蜂窝网络下行链路的资源块(Resource Block,RB)分配与调制编码策略(Modulation-and-Co-ding Scheme,MCS)选择问题,构建了整数线性规划模型,以在保障每个飞蜂窝用户最小吞吐量的需求下,最大化飞蜂窝系统吞吐量。其中,吞吐量是衡量网络性能最重要的服务质量(Quality of Service,QoS)指标之一。鉴于此问题是一个NP难问题,提出了一种ACOGA智能优化算法。该算法结合遗传算法(Genetic Algorithm,GA)与蚁群优化(Ant Colony Optimization,ACO)算法,可实现RB的动态分配与MCS的动态选择,并收敛到一种近优的分配策略。其中,GA算法动态地优化ACO算法中的参数配置,ACO算法利用优化后的参数配置执行RB分配与MCS选择。仿真表明,与采用静态参数配置的ACO算法相比较,ACOGA算法可使飞蜂窝系统的吞吐量提高12%以上,并显著提高了收敛速率。     

基于遗传算法的航空弹药保障人员优化配置

航空弹药保障任务中人员合理高效的配置是一个多约束条件下的组合优化问题。在建立保障人员配置模型的基础上,通过操作因子对模型的合理描述,应用遗传算法对模型进行优化求解,得到合理的保障人员数量和最优的保障人员配置方案。实例仿真验证了模型的正确性及算法的有效性。     

考虑服务质量的并行MapReduce启发式车载云资源调度

为提高车载云计算资源调度的可靠性,减少数据处理时间,提出一种服务质量感知的并行MapReduce启发式车载云资源调度算法。在MapReduce并行计算模型的基础上,设计云计算环境中以车载单元为基础的车辆并行检测服务框架,利用相对优先级因子构建车载云计算调度模型,并通过启发式并行优化算法对模型进行优化,降低算法复杂度。在NS-3中的仿真结果表明,该算法可有效缩短作业执行时间,并具有较高的可靠性。     

基于帕累托最优的云资源调度研究

针对在云环境中,服务资源在各用户间难以实现最优动态分配的问题,利用帕累托最优理论与粒子群优化算法相互结合应用于云计算模型中,对各种服务资源的效用进行最优化配置,最终使资源利用率达到一个最优的状态。通过CloudSim对云服务资源调度进行仿真实验,结果表明,采用帕累托最优算法优化后的云计算模型具有更好的系统性能,使得资源的调度和配置达到最优。     

任务与资源双向匹配的动态任务调度策略研究

并行测试系统中的测试任务的执行时间是不确定的,测试任务过程具有随机性。为实现测试任务优化执行的目的,建立了并行自动测试系统的动态任务调动模型,并提出了基于测试任务剩余工作量和测试资源剩余负载的启发式调度规则,并在测试任务过程Petri网模型的运行演化算法中采用该规则,实现并行测试任务的动态调度。最后通过实例仿真,验证了该策略的可行性和优越性。     

大数据分类挖掘算法及其概念漂移应用研究

随着大数据应用研究的不断深入和分布式机器学习中流计算框架的涌现,针对数据流中概念漂移问题的研究是面向大数据挖掘领域的研究热点之一。现有的针对概念漂移的研究成果主要还是依赖于数据结构和算法优化,通过计算资源有限的独立计算机完成概念漂移的检测。为此,提出一种面向大数据的基于Storm的抵抗概念漂移的分类挖掘算法S-CVFDT(Storm-concept very fast decision tree)及系统。该系统采用并行化窗口和S-CVFDT算法,利用并行化窗口机制检测数据流中的突变型概念漂移,从而自适应地改变并行窗口大小,并通过S-CVFDT算法不断更新渐进性概念漂移时的模型。分析与实验结果表明,该算法可以快速有效地检测到突变型概念漂移,降低系统因为突变型概念漂移造成的资源浪费,且模型建立效率、分类精度得到提高。     

云计算中基于NSGA Ⅱ的虚拟资源调度算法

云计算环境下将物理资源抽象为同一的虚拟资源,如何将虚拟资源调度到物理资源上是云计算中一个基本且复杂的问题.对虚拟资源的调度进行建模并证明其难解性,将该模型的求解转化以系统负载均衡为优化目标的多目标优化问题,提出采用改进的基于非支配排序的遗传算法(NSGA Ⅱ)来求解该问题.与针对具体环境的调度算法相比,抽象的模型更能代表典型的云计算环境中的虚拟资源调度问题.对提出模型进行了仿真,实验结果表明了该模型的有效性和NSGA Ⅱ算法求解该问题的可行性,同时对比随机算法、静态算法和排序匹配调度算法,NSGA Ⅱ算法优于其他算法.     

基于混沌遗传算法的维修保障资源优化方法研究

维修保障资源优化集成作为保障资源分析的一个关键问题,是一个典型的集合覆盖问题,属于经典的N-P难题;针对现有优化方法存在的不足,通过对保障资源优化集成问题的分析,提出一种基于混沌遗传算法的保障资源优化方法;算法以遗传算法为主流程,利用混沌现象不重复遍历的特点优化生成初始种群,然后对每次迭代中的个体以一定的概率进行混沌优化;以C17电路为例,对算法的有效性进行了验证,结果表明,与传统方法相比较,该方法搜索速度快,优化效果明显,该方法已在工程实践得到应用。     

基于遗传算法的信息流安全标记自动化挖掘

分布式信息流模型可以细粒度地控制信息的访问和传播,但其灵活性也增加了系统安全需求表达和策略管理实施的复杂性,严重制约了模型在云计算等分布式系统中的应用。对此,设计了一种基于安全断言的策略描述语言以表达安全需求;形式化定义了信息流安全标记挖掘问题,分析并证明了该问题是NP完全问题;提出了基于遗传算法的标记挖掘近似最优化算法。实验结果表明,算法可自动挖掘出策略配置最优解方案,有效提高了分布式信息流模型在实际系统中的可用性。     

基于遗传算法的网络安全配置自动生成框架

合理配置网络安全设备以对信息系统实施必要的访问控制,是网络安全管理的一项重要任务。随着网络规模的不断扩大,各种用户权限之间会形成复杂的依赖关系,传统基于人工的方式配置网络访问控制策略,主要是依据业务系统的实际需求,按照最小权限的原则进行分配,这种分配方式忽略了权限之间的依赖关系,容易产生过授权的现象,从而为网络带来安全隐患。为解决该问题,提出了一个基于遗传算法的安全配置自动生成框架。首先,以网络规划信息和配置信息为基础,确定用户可能的权限,提取网络基础语义,构建相应的网络安全风险评估模型,实现不同安全配置的安全评估;然后,对网络中所有可能的安全配置进行合理编码,确定遗传算子和算法参数,生成初始种群;最后,通过遗传算法,自动选取较优个体来生成子代个体。该框架能够通过自动比较不同的安全配置下的网络安全风险,以及在可能的配置空间内自动搜索安全配置的最优解,来实现网络安全设备访问控制策略的自动生成。构造一个拥有20个设备、30个服务的模拟网络环境对该框架进行验证,在该模拟环境下,该框架能够在种群样本数目为150的条件下,不超过10次迭代即可找到较优的安全配置。实验结果充分表明,该框架能够根据网络的安全需求,自动生成合理的网络安全配置。     

The economic impact of cyber terrorism

What is the economic impact of cyber terrorism? Can organizations achieve strategic advantage in the cyber terrorism game? A general game theoretical model is proposed to study the optimal information systems (ISs) security investment and then applied to compare the losses caused by cyber terrorists and common hackers. Literature is reviewed on IS security, game theoretical models of IS security, cyber terrorism, cyber deterrence and IS security breach function. Simulations with varying levels of attacker’s preference, breach function sensitivity and deterrence level are carried out to determine sensitivity to the optimal IS security investment. Results suggest that organizations should invest more to protect their strategic information systems against cyber terrorists who have long-term goals.     

Towards the automatic and optimal selection of risk treatments for business processes using a constraint programming approach

ContextThe use of Business Process Management Systems (BPMS) has emerged in the IT arena for the automation of business processes. In the majority of cases, the issue of security is overlooked by default in these systems, and hence the potential cost and consequences of the materialization of threats could produce catastrophic loss for organizations. Therefore, the early selection of security controls that mitigate risks is a real and important necessity. Nevertheless, there exists an enormous range of IT security controls and their configuration is a human, manual, time-consuming and error-prone task. Furthermore, configurations are carried out separately from the organization perspective and involve many security stakeholders. This separation makes difficult to ensure the effectiveness of the configuration with regard to organizational requirements.ObjectiveIn this paper, we strive to provide security stakeholders with automated tools for the optimal selection of IT security configurations in accordance with a range of business process scenarios and organizational multi-criteria.MethodAn approach based on feature model analysis and constraint programming techniques is presented, which enable the automated analysis and selection of optimal security configurations.ResultsA catalogue of feature models is determined by analyzing typical IT security controls for BPMSs for the enforcement of the standard goals of security: integrity, confidentiality, availability, authorization, and authentication. These feature models have been implemented through constraint programs, and Constraint Programming techniques based on optimized and non-optimized searches are used to automate the selection and generation of configurations. In order to compare the results of the determination of configuration a comparative analysis is given.ConclusionIn this paper, we present innovative tools based on feature models, Constraint Programming and multi-objective techniques that enable the agile, adaptable and automatic selection and generation of security configurations in accordance with the needs of the organization.     

中央空调系统并联水泵节能优化群智能控制算法

本文针对现有中央空调系统中并联水泵优化算法在群智能控制方面适应性不足问 题,提出了一种基于群智能平台的并联水泵优化算法。首先建立了并联水泵工作特性模型及 适应度函数,其次提出了一种分布式的估计算法,每个水泵通过与相邻水泵交互信息完成对 并联水泵运行的优化,最后分别以大小相同冷冻水循环泵系统和大小不同冷冻水循环泵系统 进行仿真验证并同时与集中式估计算法做对比。结果表明,该分布式算法可在满足末端流量 需求的情况下,对大小相同并联水泵进行台数配置和转速比优化;在末端流量变化时,不同 大小水泵可通过转速比等比例调节,实现对转速比的优化。     

Utilizing variant differentiation to mitigate manufacturing complexity in mixed-model assembly systems

A measure of product variety induced complexity has been proposed for mixed-model assembly systems with serial, parallel and hybrid configurations. The complexity model was built based on the assumption of identical parallel stations, i.e., same product variants are produced at all parallel stations in the same volume and with the same mix ratio. In this paper, the existing complexity model is extended to general mixed-model assembly systems with non-identical parallel stations in the presence of product variety. Then it is discussed that how to reduce the system complexity using the variant differentiation, based on which a mathematical formulation is developed to minimize the complexity of a mixed-model assembly system. The formulated problem is a non-linear programming problem and then solved by genetic algorithm. Last the developed complexity mitigation model is applied to the configuration selection of assembly systems, i.e., to identify the system configuration with the minimum complexity.     

A new compression based approach for reconfiguration overhead reduction in virtex based RTR systems

The advantage of RTR systems usually comes with some costs. The required time to map some areas of a program to an FPGA is considerable and affects the performance of RTR systems. Several methods have been developed to speed up the configuration process in these systems. Configuration compression can reduce the total number of write operations to load a configuration and it has been proven to be an efficient technique for dealing with the configuration overhead. In this paper, we have developed a new approach for reconfiguration overhead reduction in Virtex Based RTR Systems by using a compression technique based on Lempel-Ziv (LZ) algorithm. Since the order of the sequence of configuration frames affects the compression rate, we have proposed an algorithm based on Genetic Algorithm for finding the optimal configuration sequence of frames. The proposed algorithm will be applied to the input configuration file in a batch (offline) manner, and its time complexity is tolerable considering the overhead reduction obtained by having the optimal sequence of frames in run-time configuration decompression. Also, corresponding to our approach, a hardware model has been designed for configuration decompression.     

战时备件配送的MDVRP问题及其遗传算法求解

战时备件配送的车辆调度是提高装备保障效率的关键因素。以装备战斗效能损失最小化为车辆调度的目标,建立了多仓库车辆路径问题MDVRP（Multi—Depot Vehicle Routing Problem）模型,并应用混合遗传算法对问题进行了求解。算法中,设计了串行、并行及半并行三种交叉算子,并应用局部搜索模块对子个体进行改进。对算例的计算实验表明,半并行交叉算子在精度方面优于另外两种交叉算子。     

Enterprise security pattern: A model-driven architecture instance

To secure their information assets, organizations should seek support from enterprise security architectures. Security patterns are a good way to build and test new security mechanisms, but they have some limitations related to their usability. In previous work, we defined a new type of security pattern called Enterprise Security Pattern. The main objective of these patterns is to provide an instance of model-driven architecture, which offers a solution to recurring problems that have to do with information systems security. In recent years, the hiring of Software as a Service (SaaS) from cloud providers has become very popular. There seem to be many advantages of using these services, but organizations need to be aware of a variety of threats, as well as being prepared to handle them. In another work undertaken previously, we defined an enterprise security pattern called Secure Software as a Service (Secure SaaS), which the organizations could apply to protect their information assets when using SaaS. In this paper, we present different instances of the solution models of the enterprise security pattern Secure SaaS, aiming to verify the risks that an organization would assume if each of the instances were deployed. With this approach, we intend to show how the design decisions adopted when performing the transformations between the solution models can have a direct impact on the security provided by the pattern.