使用矩阵实现LRU的页面置换算法

操作系统的内存管理一直是计算机领域研究的一个重要方向。文中分析了几种常用内存管理中的页面置换算法及其存在的问题,提出了LUR页面置换算法的操作系统内存管理中比较接近理想算法的一种页面置换算法,并阐述了使用矩阵方法实现该页面置换算法的原理。     

A multiobjective study of the Gaussian cluster paging in the Reporting Cells strategy

Mobile location management is an important task in mobile networks. This paper presents an analysis of the Gaussian cluster paging in the Reporting Cells strategy. Reporting Cells define a location update strategy in which some network cells are configured as Reporting Cells with the aim of tracking the subscribers’ movement. On the other hand, the Gaussian cluster paging is a paging scheme where we assume that the probability of finding a callee's terminal decreases as we move away from the last updated location. This paging scheme is studied in a multiobjective way for different probability thresholds, and considering time-delay constraints. With the goal of finding the best possible sets of non-dominated solutions, we propose our implementation of a well-known multiobjective evolutionary algorithm: the Non-dominated Sorting Genetic Algorithm II. By means of an experimental study, we show that each probability threshold has its own non-dominated region in the objective space.     

基于RMC的蒙特卡罗程序性能优化

蒙特卡罗MC方法是核反应堆设计和分析中重要的粒子输运模拟方法.MC方法能够模拟复杂几何形状且计算结果精度高,缺点是需要耗费大量时间进行上亿规模粒子模拟.如何提高蒙特卡罗程序的性能成为大规模蒙特卡罗数值模拟的挑战.基于堆用蒙特卡罗分析程序RM C,先后开展了基于TCMalloc动态内存分配优化、OpenMP线程调度策略优...     

An accessibility measure for the combined travel demand model

The accessibility-oriented transport planning method is critical for many high population density cities in China. Most definitions of accessibility only consider spatial separation and ignore the influence of traveler choice on accessibility. In this paper, the combined travel demand model is employed in transport planning. The travelers’ choice behavior of the model is based on the random utility theory. The model overcomes inconsistence problem of the sequential four-step model on travel behavior and con...     

A new algorithm inspired in the behavior of the social-spider for constrained optimization

During the past decade, solving constrained optimization problems with swarm algorithms has received considerable attention among researchers and practitioners. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. The SSO-C algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. For constraint handling, the proposed algorithm incorporates the combination of two different paradigms in order to direct the search towards feasible regions of the search space. In particular, it has been added: (1) a penalty function which introduces a tendency term into the original objective function to penalize constraint violations in order to solve a constrained problem as an unconstrained one; (2) a feasibility criterion to bias the generation of new individuals toward feasible regions increasing also their probability of getting better solutions. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. Simulation and comparisons based on several well-studied benchmarks functions and real-world engineering problems demonstrate the effectiveness, efficiency and stability of the proposed method.     

Sporadic model building for efficiency enhancement of the hierarchical BOA

Efficiency enhancement techniques—such as parallelization and hybridization—are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once in every few iterations (generations), whereas in the remaining iterations, only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup, which decreases the asymptotic time complexity of model building in hBOA by a factor of to where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, if model building is the bottleneck, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building. The paper also presents a dimensional model to provide a heuristic for scaling the structure-building period, which is the only parameter of the proposed sporadic model-building approach. The paper then tests the proposed method and the rule for setting the structure-building period on the problem of finding ground states of 2D and 3D Ising spin glasses.     

蚁群算法在需水预测模型参数优化中的应用

为了解决投影寻踪（PP）需水预测模型的高维、非正态、非线性参数优化问题,提高需水预测的精度,尝试用基于网格划分的自适应连续域蚁群算法(ACA)在不同拟合和预测时长内对模型参数进行优化组合,并运用该模型进行年需水量预测。基于改进蚁群算法的投影寻踪需水预测模型参数优化进行了实例仿真。对基于改进蚁群算法的预测精度与基于人工免疫算法（AIA）和BP神经网络的模型(BPANN)参数优化结果分别进行了比较,实验结果表明：1）这三种算法的拟合精度相对误差绝对值分别小于2%、10%和10%;2）预测精度相对误差绝对值分别小于6%、11%和12%;3）改进蚁群算法能收敛到全局最优解,收敛速度较快。因此,改进蚁群算法的投影寻踪需水预测结果明显优于人工免疫算法和BP神经网络。该方法可推广到其他类似的高维非线性问题上。     

A risk-reward model for the on-line leasing of depreciable equipment

The optimal deterministic strategy for the on-line leasing of depreciable equipment is presented here for cases with and without an interest rate. A risk-reward model in which the on-line investor can develop optimal strategies based on his/her risk tolerance and forecast is discussed. Numerical analysis shows that the competitive performance is significantly improved in the proposed model.     

Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model

Owing to the sporadic nature of demand for aircraft maintenance repair parts, airline operators perceive difficulties in forecasting and are still looking for superior forecasting methods. This paper deals with techniques applicable to predicting spare parts demand for airline fleets. The experimental results of 13 forecasting methods, including those used by aviation companies, are examined and clarified through statistical analysis. The general linear model approach is used to explain the variation attributable to different experimental factors and their interactions. Actual historical data for hard-time and condition-monitoring components from an airlines operator are used, in order to compare different forecasting methods when facing intermittent demand. The results confirm the continued superiority of the weighted moving average, Holt and Croston method for intermittent demand, whereas most commonly used methods by airlines are found to be questionable, consistently producing poor forecasting performance. We have, however, devised a new approach to forecasting evaluation, a predictive error-forecasting model which compares and evaluates forecasting methods based on their factor levels when faced with intermittent demand. A simple example is presented to illustrate the performance of the mathematical model. It is suggested that these findings may be applicable to other industrial sectors, which have similar demand patterns to those of airlines.Scope and purposeDemand forecasting is one of the most crucial issues of inventory management. Forecasts, which form the basis for the planning of inventory levels, are probably the biggest challenge in the repair and overhaul industry, as the one common problem facing airlines throughout the world is the need to know the short-term part demand forecast with the highest possible degree of accuracy. The high cost of modern aircraft and the expense of such repairable spares as aircraft engines and avionics constitute a large part of the total investment of many airline operators. These parts, though low in demand, are critical to operations and their unavailability can lead to excessive down time costs. Most airline materials managers deal with intermittent demand, which tends to be random and has a large proportion of zero values. In an effort to achieve this, the study has presented a model that could be of great benefit to airline operators and other maintenance service organisations. It will enable them to select in advance the appropriate forecasting method that better meets their cyclical demand for parts. This approach is consistent with the purpose of this study, which aims to compare different forecasting methods when faced with intermittent demand.     

具有两类请求的云计算中心服务器数量的优化

为提高云计算中心的服务质量,节约系统成本,针对具有两类用户请求的云计算中心,提出云计算中心的服务器数量的优化方案。首先,建立了具有两类用户请求的排队模型,分析系统的稳态概率分布、平均队长等性能指标;然后,建立了云计算中心的能耗模型;最后,联合系统的等待成本和能耗成本,构建系统的成本函数,对系统的服务器数量进行优化,从而使系统的成本最小。数值分析结果表明最优服务器数量是用户请求到达率的非减函数,为了使系统成本最小,云计算中心需要动态调整服务器的数量。     

An analysis of the equilibrium of migration models for biogeography-based optimization

Motivated by the migration mechanisms of ecosystems, various extensions to biogeography-based optimization (BBO) are proposed here. As a global optimization method, BBO is an original algorithm based on the mathematical model of organism distribution in biological systems. BBO is an evolutionary process that achieves information sharing by biogeography-based migration operators. In BBO, habitats represent candidate problem solutions, and species migration represents the sharing of features between candidate solutions according to the fitness of the habitats. This paper generalizes equilibrium species count results in biogeography theory, explores the behavior of six different migration models in BBO, and investigates performance through 23 benchmark functions with a wide range of dimensions and diverse complexities. The performance study shows that sinusoidal migration curves provide the best performance among the six different models that we explored. In addition, comparison with other biology-based optimization algorithms is investigated, and the influence of the population size, problem dimension, mutation rate, and maximum migration rate of BBO are also studied.     

Implementation and performance of a parallel version of the HIRLAM limited area atmospheric model

The HIRLAM (high resolution limited area modelling) limited-area atmospheric model was originally developed and optimized for shared memory vector-based computers, and has been used for operational weather forecasting on such machines for several years. This paper describes the algorithms applied to obtain a highly parallel implementation of the model, suitable for distributed memory machines. The performance results presented indicate that the parallelization effort has been successful, and the Norwegian Meteorological Institute will run the parallel version in production on a Cray T3E.     

基于固定内存和状态管理的模型检测方法*

在研究固定内存和状态管理的基础上,提出了一种新的模型检测方法,使得在任何计算机上都能对任意规模的并发系统进行模型检测.     

Entropic-FV修正模型预测聚合物体系溶胀行为

Entropic-FV（EFV）修正模型是对原模型的自由体积分数项进行修改而获得,前者与后者具有相同的统计力学基础,且表达式简单,无需增加模型参数。将其用于聚乙二醇／甲醇二元体系中组分活度的计算,或与UNIQUAC-HB模型结合对聚乙烯醇膜／乙二醇／水和聚氨酯／正己烷／苯三元体系的溶胀平衡组成进行预测,并与EFV和原修正模型的预测结果以及文献值进行比较,结果显示该模型预测的准确度最高、稳定性较好。     

洪水预报经验模型的混合蛙跳优化率定

PI模型广泛应用于洪水预报和水资源管理等领域,为防汛、抗旱和水资源利用等重大决策提供了重要依据。API模型定线往往采用人工的方式进行绘制,在精度上和效率上比较欠缺。江苏省洪水预警预报平台在研发过程中为提高API模型预报精度,采用了混合蛙跳算法（SFLA）对API模型定线进行优化。本文主要介绍了混合蛙跳算法对API模型进行参数优化和智能定线的方法,并通过混合蛙跳算法、传统方法对71场洪水进行绘制,与传统定线方法相比,证实混合蛙跳法能有效提高API模型预报精度。     

Exploring the affect factors of knowledge sharing behavior: The relations model theory perspective

With a view to achieve the ultimate goal of a permanent development, operation, and growth, to any business and enterprise, the strategy of knowledge management must be reinforced, and the sooner the better. In despite of those new and high interests shown toward the organization-embedded knowledge, not much concrete finding has been obtained regarding how and why employees are reluctant to share what they know.In our research, we proposed to base on the relations model theory to explore how different relation models, cultivated and shaped by different corporate cultures, give their influences on the willingness of knowledge sharing from employees. In the mean time, with a view to get closer to the realistic circumstance in the office, we give it a shot to include additional moderating variables, task inter-dependence, as well as time-of-cooperation, into our full research framework, aiming to see if they will disturb the influencing processes between the four principal relations and the willingness of employees to share their knowledge.The result reflects the distinct impact from communal sharing and equality matching on the willingness of sharing, while a subtle but negative impact of market pricing on the sharing willingness. There is no clear effect of authority ranking. Furthermore, in the analysis of interaction mode including additional moderators, the result has exhibited that task inter-dependence does moderate the relationship between communal sharing/equality matching/market pricing and the notion of sharing, while time-of-cooperation also adjusts the influencing processes between communal sharing, equality matching, market pricing, and willingness of sharing. This analysis and study grant us some clues regarding how corporate culture would eventually leverage employees’ intention in sharing their knowledge, and advise the business organizations how they should correctly formulate the knowledge management strategy and activities to augment the knowledge inter-flows between employees.