Spare parts allocation by improved genetic algorithm and Monte Carlo simulation

A combined Monte Carlo (MC) simulation and Genetic Algorithm (GA) method was proposed by other researchers for the optimisation of spare parts allocation. From case studies, it was found that the number of simulation trials of the existing method tended to be either excessive or inadequate. Thus, a simulation replication number control method making full use of the advance simulation effort is proposed and implemented into the existing method. A numerical example shows significant improvement on overall simulation efficiency and that at the same time the required accuracy is guaranteed. Furthermore, it is argued that application-specific knowledge should be embedded into the general GA procedure so that the evolution process can be more efficient. Heuristic methods for initial population preparation for GA with and without considering component cost difference are proposed and illustrated for spare parts allocation. A computing experiment was designed and performed to examine the influence of parameters for replication number control and initial population preparation. The generation of availability–cost curve further indicates the necessity to adopt heuristic methods to improve searching efficiency in GA.     

Proposal and validation of an optimization method using Monte Carlo method for multi-objective functions

Solving optimization problems is essential for many engineering applications and research tools. In a previous report, we proposed a new optimization method, MOST (Monte Carlo Stochastic Optimization), using the Monte Carlo method, and applied it to benchmark problems for optimization methods, and optimization of neural network machine learning. While the proposed method MOST was a single objective, this study is an extension of MOST so that it can be applied to multi-objective functions for the purpose of improving generality. As the verification, it was applied to the optimization problem with the restriction condition first, and it was also applied to the benchmark problem for the multi-objective optimization technique verification, and the validity was confirmed. For comparison, the calculation by genetic algorithm was also carried out, and it was confirmed that MOST was excellent in calculation accuracy and calculation time.     

An adaptive Monte Carlo integration algorithm with general division approach

We propose an adaptive Monte Carlo algorithm for estimating multidimensional integrals over a hyper-rectangular region. The algorithm uses iteratively the idea of separating the domain of integration into 2^s$2^s$subregions. The proposed algorithm can be applied directly to estimate the integral using an efficient way of storage. We test the algorithm for estimating the value of a 30-dimensional integral using a two-division approach. The numerical results show that the proposed algorithm gives better results than using one-division approach.     

A rigorous sequential update strategy for parallel kinetic Monte Carlo simulation

The kinetic Monte Carlo (kMC) method is used in many scientific fields in applications involving rare-event transitions. Due to its discrete stochastic nature, efforts to parallelize kMC approaches often produce unbalanced time evolutions requiring complex implementations to ensure correct statistics. In the context of parallel kMC, the sequential update technique has shown promise by generating high quality distributions with high relative efficiencies for short-range systems. In this work, we provide an extension of the sequential update method in a parallel context that rigorously obeys detailed balance, which guarantees exact equilibrium statistics for all parallelization settings. Our approach also preserves nonequilibrium dynamics with minimal error for many parallelization settings, and can be used to achieve highly precise sampling.     

基于多假设粒子群优化的蒙特卡罗定位算法

针对传统蒙特卡罗定位(MCL)算法在结构化相似环境中容易出现定位失败的问题,提出一种基于多假设粒子群优化的改进蒙特卡罗定位方法(MPSO-CL).以激光传感器的观测信息作为适应度函数,对MCL算法的采样粒子进行多假设粒子群优化更新,使得采样粒子向当前群体中多个最优粒子方向移动,从而使得粒子迅速收敛到后验概率密度分布取值较大的区域,实现了移动机器人高效精确自主定位.实验结果表明,MPSO-MCL算法克服了相似环境中定位的粒子匮乏问题,并且提高了定位的精确度.     

单电子器件的仿真

该文介绍了一种利用正统理论与Ｍｏｎｔｅ Ｃａｒｌｏ方法模拟单电子隧空器件的认真程序。该程序可模拟电子通过包含小隧道结、电容和理想电压源的电路的输运过程,利用该程序,对单库仓岛和多库仓同的单电子晶体管（ＳＥＴ）系统进行了模拟。     

基于CVaR和Monte Carlo仿真的贷款组合决策模型

提供了一种新的贷款组合决策优化方法,该模型用更能反映贷款组合信用风险特征的CVaR作为风险度量。由于在实际中很难获取各笔贷款的历史数据,为此给出了一种基于Matlab语言的Monte Carlo仿真方法。从而使谊模型可以通过线性规划技术有效的进行求解。最后给出了一个例子。     

基于RSSI等级的蒙特卡罗定位算法应用研究

感知节点的定位是无线传感网应用的基础。现有的静态定位算法无法应用于动态传感网。针对一类目标节点移动而锚节点静止的传感网应用,提出了一种RRMCL（RSSI Rank Monte Carlo Localization）定位算法。该算法以蒙特卡罗算法为基础,利用RSSI（Received Signal Strength Indication）值与距离的单调递减关系划分通信域,减少采样区域大小。为了避免锚节点共线出现定位失效的情况,引入共线影响角度,提出了一种约束策略。仿真结果表明,提出的RRMCL与现有的MCL和MCB定位算法相比,能有效缩小采样区域,提高了定位精度和速度。     

Interacting sequential Monte Carlo samplers for trans-dimensional simulation

The methodology of interacting sequential Monte Carlo (SMC) samplers is introduced. SMC samplers are methods for sampling from a sequence of densities on a common measurable space using a combination of Markov chain Monte Carlo (MCMC) and sequential importance sampling/resampling (SIR) methodology. One of the main problems with SMC samplers when simulating from trans-dimensional, multimodal static targets is that transition kernels do not mix which leads to low particle diversity. In such situations poor Monte Carlo estimates may be derived. To deal with this problem an interacting SMC approach for static inference is introduced. The method proceeds by running SMC samplers in parallel on, initially, different regions of the state space and then moving the corresponding samples onto the entire state space. Once the samplers reach a common space the samplers are combined and allowed to interact. The method is intended to increase the diversity of the population of samples. It is established that interacting SMC admit a Feynman-Kac representation; this provides a framework for the convergence results that are developed. In addition, the methodology is demonstrated on a trans-dimensional inference problem in Bayesian mixture modelling and also, using adaptive methods, a mixture modelling problem in population genetics.     

大分子蠕动的Monte Carlo模型及计算方法

高分子链分子量巨大而且分散,结构复杂多变,存在大量不确定的问题,给理论和实验研究造成许多困难。本文建立了一种基于高分子Monte Carlo模拟算法,分析分子蠕动阻力的模型。用链动力学法构造8配位点键长涨落格子链多链体系,通过高分子链的运动使任意的初始有序态演化到平衡态,可同时获得体系的动态和静态性质。将重要性抽样法和高分子多链体系的链动力学构造法,在键长涨落模型中结合起来,形成多链体系的链动力学求解法。引入空格扩散算法,用以研究高浓度态的高分子体系。统计平衡后元胞内高分子多链体系在链动力学算法作用下蠕动失败机率,对高聚物多链体系内分子蠕动的困难程度的定量模拟,能更直观分析高分子玻璃化转变的现象。     

Optimizations and OpenMP implementation for the direct simulation Monte Carlo method

Parallel implementation of a three-dimensional direct simulation Monte Carlo (DSMC) code employing complex data structures and dynamic memory allocation is detailed for shared memory systems using Open Multi-Processing (OpenMP). Several techniques to optimize the serial implementation of the DSMC method are first discussed. Specifically for a 3-level Cartesian grid, a Cartesian-based movement technique including particle indexing is demonstrated to result in a modest decrease in overall simulation expense of 34% compared with a ray-tracing technique combined with stored cell-connectivity. Two strategies for data localization leading to optimal usage of cache memory are demonstrated to speed up certain cell-based functions (such as collision computations) by a factor of 3.38–4.36. The shared-memory parallel implementation using OpenMP is then described in detail. Synchronization points and related critical sections are identified as major factors that impact the OpenMP parallel performance. Techniques to remove all such synchronization points in the OpenMP implementation of the DSMC method are outlined. For dual-core and quad-core systems, speedups of 1.99 and 3.74, respectively, are obtained for a (free-stream flow) test simulation with low granularity. Finally, the parallel performance of identical source code employing OpenMP is shown to be strongly correlated to the underlying computer architecture. Both Symmetric Multiprocessor (SMP) and non-uniform memory access (NUMA) systems are studied in order to achieve a better understanding of their impacts on parallel scalability when using OpenMP.     

Intelligent state space pruning for Monte Carlo simulation with applications in composite power system reliability

The probabilistic reliability evaluation of composite power systems is a complicated, computation intensive, and combinatorial task. As such evaluation may suffer from issues regarding high dimensionality that lead to an increased need for computational resources, MCS is often used to evaluate the reliability of power systems. In order to alleviate this burden, an analytical method known as state space decomposition has previously been used to prune the state space that is sampled using MCS.This paper extends the state-of-the-art by proposing a novel algorithm known as intelligent state space pruning (ISSP). This algorithm leverages the intelligence of highly modified population based metaheuristic (PBM) algorithms including genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial immune systems (AIS) to quickly, efficiently, and intelligently prune the state space that is used during MCS. The presented PBMs are modified using domain-specific knowledge to improve their performance and fine tune their intelligence. This new algorithm leads to reductions of up to 90% in total computation time and iterations required for convergence when compared to non-sequential MCS. Results are reported using the IEEE Reliability Test Systems (RTS79/MRTS).     

一种基于蒙特卡罗方法的体绘制算法*

为解决医学图像三维可视化中大规模体数据显示速度与成像质量问题,引入一种基于蒙特卡罗方法的新颖体绘制算法。本算法根据给出的概率密度函数从随机样本点或其子集中选取一个点阵来进行绘制,主要适用于对大规模体数据集的有效可视化。另外,使用渐进细化的方法在图像质量和交互性上进行权衡折中,以适应实时要求。实验结果表明,在最终图像分辨率固定的情况下,本算法的时间复杂度和空间复杂度都比之前的算法要好,成像质量也满足实时绘制要求,可以在实际应用中使用。     

基于蒙特卡罗模拟修正的随机矩阵去噪方法

针对蕴含噪声信息较少的小组合股票市场,提出使用蒙特卡罗模拟修正的随机矩阵去噪方法。首先通过数据模拟生成随机矩阵,然后利用大量的模拟数据来同时修正噪声下界和上界,最终对噪声范围进行精确测定。运用道琼斯中国88指数和香港恒生50指数的数据进行实证分析,结果表明,与LCPB法、PG+法和KR法相比,在特征值、特征向量和反比参率方面, 蒙特卡罗模拟去噪方法修正后噪声范围的合理性及有效性得到很大的提升;对去噪前后的相关矩阵进行投资组合,得知在相同的期望收益率下,蒙特卡罗模拟去噪方法具有最小的风险值,能够为资产组合选择和风险管理等金融应用提供一定的参考。     

二元自由基共聚合的Monte Carlo模拟

采用Monte Carlo模拟计算方法、可视化编程语言Visual Basic,以研究非稳态、考虑前末端效应、聚合反应可逆等情况下二元自由基共聚合反应,以期得到共聚物的瞬时组成、组成分布、序列分布等决定共聚物效能的重要参数。以数值模拟代替实际实验,不仅可节约成本,而且可减少工作量。模拟结果显示,Monte Carlo模拟值与实验值符合较好,说明本模拟过程模拟效果较好,结果可靠,可用来模拟未知共聚合体系,并为实际共聚物的合成提供帮助。     

出租车上客区规模的Monte Carlo仿真计算方法

为确定出租车上客区的合理规模,分析其运行特征,同时存在乘客和出租车相互等待的情况,而不能采用停车场模型或排队论模型计算。采用Monte Carlo仿真方法,利用随机原理来拟合乘客与出租车的相互作用过程,给出了合理规模的确定流程。实例应用证明该方法具有很好的适用性;同时分析结果表明：（1）当出租车上客区泊位规模达到一定程度时,再增加并不能减少乘客等待时间;（2）高峰时段适当限制乘客的排队长度能够在较小比例的乘客损失下,极大地降低乘客的等待时间。     

基于蒙特卡罗贝叶斯推理的移动群智感知可靠任务分配机制

针对现有任务分配策略的不足,研究了在工人数量有限的移动群智感知系统中任务分配策略,借助社交网络来分配任务并获得高收益。首先,建立了社交网络的动态不确定环境,利用社交网络完成任务,传播任务。然后考虑到不同社交网络对任务的偏好不同,设置任务偏好度这一不确定指标,借助经济学风险价值的理论描述任务分配的可靠性。最后利用蒙特卡罗贝叶斯推理方法研究任务动态传播模型的复杂参数的高斯过程,设计基于知识梯度的采样算法选择蒙特卡罗采样点,从而实现高收益的任务分配方案。为了验证所提策略的性能,将其与四种基准的采样算法进行比较。实验结果表明,所提任务分配策略在提高收益方面是有效的。     

A fortran program for Monte Carlo simulation of oil-field discovery sequences

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios.     

目标跟踪中的改进Monte Carlo滤波算法

传统的Monte Carlo滤波算法在目标跟踪过程中存在严重的采样贫瘠问题,这直接导致了样本集的退化。为了解决这个问题,提出一种改进的Monte Carlo滤波算法。在样本集建立阶段,采用基于视觉机制的方法建立样本集合,使得样本集在与中心距离较近的地方密集,在离中心较远的地方稀疏,这样的样本集合建立方法能够更准确地反映人眼对事物的感知;在样本集传播阶段,获得一个区分样本优劣的阈值,将样本集合分为优劣两种,用重采样的方法对优样本集合采样,采样半数样本,用随机抽样的方法补充其余半数样本,实验结果表明,这种方法可以很好地解决样本退化的问题。