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.     

Optimal electricity rate structures for peak demand reduction using economic model predictive control

Economic model predictive control (EMPC) has recently gained popularity for managing energy consumption in buildings that are exposed to non-constant electricity prices, such as time-of-use prices or real-time prices. These electricity prices are employed directly in the objective function of the EMPC problem. This paper considers how electricity prices can be designed in order to achieve a specific objective, which in this case is minimizing peak electricity demand. A primal-dual formulation of the EMPC problem is presented that is used to determine optimal prices that minimize peak demand. The method is demonstrated on a simulated community of 900 residential homes to create a pricing structure that minimizes the peak demand of the community of homes. The pricing structure shows that homes should be given a 1-h peak demand duration, and that the peak prices given to the homes should be spread unevenly across 6 h of the afternoon.     

A virtual stack facility for mini-computer and microcomputer implementation of the finite element method

In this paper, the efficient implementation of the finite element method on mini-computers and microcomputers is considered. The main limitations of memory and address space are overcome and a software solution proposed with the use of a virtual stack facility which is implemented and tested. A new replacement algorithm for a virtual stack is presented and shown to be more efficient than other known implementations.     

基于共享存储器通信方式的并行遗传算法

文章具体分析了共享存储器的通信方法，提出了基于共享存储器通信方式的并行遗传算法。     

考虑驾乘人员公平性的网约出租车合乘路径优化模型

为了充分发挥合乘出租车承载率高、运营效益高及交通资源省等优点, 缓解城市打车难问题, 对网约出租车合乘路径优化问题开展研究. 首先针对路网中网约出租车的供需情况, 以系统路径最短为优化目标建立目标函数, 其次考虑网约出租车额定载客量、路径合理性、乘客利益及驾驶员收益与时间窗因素, 构建优化模型的约束条件, 并结合绕行距离与乘客公平性原则进行费用约束, 使得绕行距离长的合乘子路径获得更多的费用补偿, 然后基于遗传算法思想, 针对合乘路径中乘客需求起终点的次序问题, 设计了改进的交叉与变异算子. 最后依据大连市区局部路网高峰时段内的出租车供需数据, 利用合乘路径优化模型及算法进行求解. 研究表明, 优化模型及算法可以短时间寻求到系统近似最优解, 所得合乘方案较非合乘出行模式有效减少了出租车空驶率与乘客的出行成本, 提高了驾驶员的平均收益.     

Evaluating alternative Frequentist inferential approaches for optimal order quantities in the newsvendor model under Exponential demand

Three estimation policies for the optimal order quantity of the classical newsvendor model when the demand is Exponentially distributed are evaluated in this paper. The evaluation is performed analytically for different combinations of sample sizes and values of the requested critical fractile. The statistical measures that have been chosen to perform the evaluation are (a) the actual critical fractile, namely, the actual probability for the estimated order quantities to meet the demand of the period, (b) the mean square error of the estimators for the optimal order quantity, and (c) the range of deviations of estimated order quantities from the optimal order quantity, provided that the probability of taking such a range is the same for the three estimation policies. For small and moderate sample sizes, no estimation policy predominates over the other two approaches, and the choice should be made on a subjective base according to the individual preferences of researchers or practitioners.     

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.     

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。     

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.     

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.