基于M-H采样的快速反向微分进化算法

反向微分进化(ODE)算法基于反向优化对种群进行初始化更新以保持种群多样性。但该算法中反向个体容易偏离全局最优个体,不能很快达到全局最优,在函数优化过程中收敛速度慢且容易陷入局部最优。为此,提出一种基于M-H采样的快速反向微分进化算法。M-H采样用于ODE算法的变异操作,满足马尔可夫链可逆条件。马尔可夫链的一步转移概率根据个体等级分配的选择概率进行计算,既能选择最优个体,又能寻找优化方向并保持种群多样性。仿真结果表明,M-H采样得到的个体具有马尔可夫链平稳分布特性,该算法在单峰函数和多峰函数优化中都能快速收敛,全局和局部搜索性能达到平衡,具有较高的搜索精度及较好的鲁棒性。     

改进微分进化算法在压缩感知中的应用

压缩感知是基于信号稀疏性提出的采样理论,它在压缩成像、医学图像、雷达成像、天文学、通信等领域都有广泛的应用.压缩感知问题的求解本质上是一个优化问题,本文在微分进化算法的基础上对其改进,提出了一种改进微分进化算法,将其应用于压缩感知问题的求解中,取得了良好的效果.     

微分进化粒子滤波

针对传统粒子滤波重采样算法带来的样本贫化问题,提出了一种利用微分进化算法进行重采样的粒子滤波改进方法,新方法通过引入交叉变异操作,保持了粒子的多样性并抑制了粒子退化现象,提高了目标状态的估计与跟踪能力。仿真结果表明,相对于普通粒子滤波,新算法的估计精度提高了一倍,使用较少的粒子数即可完成跟踪任务。     

基于混沌搜索的微分进化算法

针对基本微分进化算法在后期收敛速度慢,搜索能力差等问题,利用混沌搜索的随机性、遍历性以及对初值的敏感性等特性,提出了一种混合混沌搜索的微分进化算法——混沌微分进化算法。该算法既保持了基本微分进化算法结构简单的特点,又能提高算法的收敛速度、计算精度以及全局寻优能力。数值仿真结果表明,该算法的性能优于基本微分进化算法。     

基于混合蒙特卡洛算法的容差分配研究

针对航空制造业中,当容差分配问题中含有装配成功率等随机约束时,常用的数值算法往往难以处理。为提高产品制造精度,提出了混合蒙特卡洛(Hybrid Monte Carlo,HMC)算法,即把动态蒙特卡洛(Dynamic Monte Carlo,DMC)算法和静态蒙特卡罗(SMC)算法结合起来,将DMC用于容差分配的优化仿真运算,把SMC用来处理装配成功率约束。通过仿真验证了该方案的可行性,混合蒙特卡洛法既合理地处理了随机约束,证明装配准确度计算和容差分配的一致性。结果说明求解这类问题是最佳算法。     

微分进化算法整数编码策略实现研究

微分进化算法（DE）是模仿生物进化“优胜劣汰、适者生存”的一种随机优化算法,具有简单、快速、鲁棒性好等特点,已经得到广泛应用.通过运用微分进化算法的整数编码方法,在整数空间中求解,并在实数空间中计算解的适应度.使用测试函数对程序进行测试,证明了整数编码解对空间个体中寻优的快速性、准确性.     

微分进化的自适应彩色图像阈值分割

针对阈值的选择依赖于经验和试验的问题,提出了结合微分进化算法和二维最大熵算法得到图像自适应阈值的方法。该方法首先利用全局阈值法中的迭代法得到图像的阈值并初次对图像进行分割,然后利用微分进化算法并且结合二维最大熵阈值进行适应度的计算、个体编码、终断条件等计算图像的自适应阈值,最后对测试的图像应用微分进化算法实现对图像的正确分割。采用微分进化算法可以准确地对图像进行分割,是一个比较高效的方法,有效地提升了分割效果。与现有的自适应阈值分割算法相比,本文算法缩短了计算时间。阈值分割不仅可以对灰度图像进行分割,彩色图像也可以用阈值分割。     

基于微分进化算法的时间最优路径规划

提出了一种利用微分进化算法进行机器人路径规划的方法,在极坐标系下采用路径点列的极角和极径作为参数进行个体成员的矢量合成,生成的初始路径点集经过提炼处理极大提高机器人移动速度;仿真结果表明该方法可以解决大范围、多障碍环境的机器人路径规划问题。     

一种改进的微分进化算法

相对于其他优化算法来说,微分进化算法具有控制参数少、易于使用以及鲁棒性强等特点,但在搜索过程中存在着局部搜索能力弱的缺点。针对微分进化算法局部搜索能力弱的缺点,提出了一种基于局部变异的微分进化算法,该算法使个体具有良好快速收敛能力。使用典型优化函数对比较算法进行了测试,算法分析和仿真结果表明,改进以后的算法具有寻优能力...     

基于微分进化的PCNN图像分割方法

脉冲耦合神经网络(PCNN)是一种新型神经网络,可以应用于图像分割。然而在对PCNN的研究应用中,其模型参数的合理确定是个难点,这在很大程度上限制了PCNN的应用。针对这一问题,提出一种基于微分进化的PCNN图像分割方法。该方法使用微分进化算法来实现脉冲耦合神经网络参数的自动设定,并通过将其应用于图像分割,将分割结果与其他优秀分割方法比较,从而验证了该方案的正确性与可行性。     

Parallel and interacting Markov chain Monte Carlo algorithm

In many situations it is important to be able to propose N independent realizations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov chains (MCMC) interact in order to get an approximation of an independent N-sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model.     

Front-view vehicle detection by Markov chain Monte Carlo method

In this paper, we propose a new vehicle detection approach based on Markov chain Monte Carlo (MCMC). We mainly discuss the detection of vehicles in front-view static images with frequent occlusions. Models of roads and vehicles based on edge information are presented, the Bayesian problem's formulations are constructed, and a Markov chain is designed to sample proposals to detect vehicles. Using the Monte Carlo technique, we detect vehicles sequentially based on the idea of maximizing a posterior probability (MAP), performing vehicle segmentation in the meantime. Our method does not require complex preprocessing steps such as background extraction or shadow elimination, which are required in many existing methods. Experimental results show that the method has a high detection rate on vehicles and can perform successful segmentation, and reduce the influence caused by vehicle occlusion.     

Bayesian parameter inference for individual-based models using a Particle Markov Chain Monte Carlo method

Parameter estimation for agent-based and individual-based models (ABMs/IBMs) is often performed by manual tuning and model uncertainty assessment is often ignored. Bayesian inference can jointly address these issues. However, due to high computational requirements of these models and technical difficulties in applying Bayesian inference to stochastic models, the exploration of its application to ABMs/IBMs has just started. We demonstrate the feasibility of Bayesian inference for ABMs/IBMs with a Particle Markov Chain Monte Carlo (PMCMC) algorithm developed for state-space models. The algorithm profits from the model's hidden Markov structure by jointly estimating system states and the marginal likelihood of the parameters using time-series observations. The PMCMC algorithm performed well when tested on a simple predator-prey IBM using artificial observation data. Hence, it offers the possibility for Bayesian inference for ABMs/IBMs. This can yield additional insights into model behaviour and uncertainty and extend the usefulness of ABMs/IBMs in ecological and environmental research.     

Population Markov Chain Monte Carlo

Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a Metropolis-Hastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physics-inspired local reversibility conditions.     

Automatic glottal inverse filtering with the Markov chain Monte Carlo method

This paper presents a new glottal inverse filtering (GIF) method that utilizes a Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Rosenberg–Klatt (RK) model and filtered with the estimated vocal tract filter to create a synthetic speech frame. In the MCMC estimation process, the first few poles of the initial vocal tract model and the RK excitation parameter are refined in order to minimize the error between the synthetic and original speech signals in the time and frequency domain. MCMC approximates the posterior distribution of the parameters, and the final estimate of the vocal tract is found by averaging the parameter values of the Markov chain. Experiments with synthetic vowels produced by a physical modeling approach show that the MCMC-based GIF method gives more accurate results compared to two known reference methods.     

基于马尔可夫链蒙特卡罗采样的双密钥盲水印算法

为了更好地提高水印算法的安全性,提出了一种基于两种形式密钥的强鲁棒盲水印算法。首先对水印加密,然后将每块载体的第一个奇异值组成矩阵Q再分块离散小波变换(DWT)获得四个子带,通过对四个子带进行马尔可夫链蒙特卡罗(MCMC)采样决定第k个水印位量化嵌入到矩阵Q的第k块低频、水平、垂直和高频子带中的一个并记录当前嵌入子带的密钥位,这样做不仅使水印位随机分配,而且提高了水印算法的安全性。实验结果表明,所提算法在满足不可见性的条件下,不仅对常规的图像攻击具备较强的鲁棒性,而且在水印嵌入过程中通过MCMC采样实现了用不同的密钥嵌入,提高了水印算法的安全性。     

Improving Markov Chain Monte Carlo Model Search for Data Mining

The motivation of this paper is the application of MCMC model scoring procedures to data mining problems, involving a large number of competing models and other relevant model choice aspects.To achieve this aim we analyze one of the most popular Markov Chain Monte Carlo methods for structural learning in graphical models, namely, the MC ³ algorithm proposed by D. Madigan and J. York (International Statistical Review, 63, 215–232, 1995). Our aim is to improve their algorithm to make it an effective and reliable tool in the field of data mining. In such context, typically highly dimensional in the number of variables, little can be known a priori and, therefore, a good model search algorithm is crucial.We present and describe in detail our implementation of the MC ³ algorithm, which provides an efficient general framework for computations with both Directed Acyclic Graphical (DAG) models and Undirected Decomposable Models (UDG). We believe that the possibility of commuting easily between the two classes of models constitutes an important asset in data mining, where an a priori knowledge of causal effects is usually difficult to establish.Furthermore, in order to improve the MC ³ method we propose provide several graphical monitors which can help extracting results and assessing the goodness of the Markov chain Monte Carlo approximation to the posterior distribution of interest.We apply our proposed methodology first to the well-known coronary heart disease dataset (D. Edwards &; T. Havránek, Biometrika, 72:2, 339–351, 1985). We then introduce a novel data mining application which concerns market basket analysis.     

圆筒受限高分子链的Monte Carlo模拟

高分子链的构象性质是高分子研究中的重要研究对象.当高分子链受限于各种微腔中时,链的构象熵与微腔的形状和体积有十分密切的关系.平面壁限制条件下的高分子构象研究极多,然而在考虑高分子链穿越半径很小的纳米管时,有必要将微腔视为细长管道,而不是平面壁,因此约束在软管中的高分子链在近几年中得到了更多的重视.现有文献中主要讨论构象和能量与约束半径R 之间的关系,没有考虑温度 T 的影响.本文采用自避行走(SAW)为模型链,利用硬球链模型模拟高分子链在无限长圆筒受限条件下的结构.模型由弹性链连接的硬球构成,球与球之间通过弹性势能函数相互作用,利用标准的 Metropolis算法,通过Monte Carlo 模拟,改变约束半径 R 和与绝对温度 T 成反比例关系的相关系数 K,分别计算了高分子链的平均回转半径,平均末端距,平均弹性势能,能量均方涨落,能量相对涨落与 R、T 的关系.结果表明,圆筒受限高分子链的构像主要取决于 R,在 R 小于某个临界值时高分子链受到明显的约束作用,当 R 超过临界值时高分子链变为无扰链.温度 T 并不明显地影响受限高分子链的构象.能量性质在 R 较小时主要取决于 R,但 R 为临界受限值附近时,能量性质受 T 影响明显,容易出现能量和涨落的状态突变.     

Positron emission tomography by Markov chain Monte Carlo with auxiliary variables

We propose a new algorithm for positron emission tomography (PET) image reconstruction. The algorithm belongs to the family of Markov chain Monte Carlo methods with auxiliary variables. The idea is to iteratively generate hidden variables at one step and use them for image restoration at another step. The well-known model of Vardi et al. (J. Amer. Statist. Assoc. 80 (1985) 8) for PET is combined with the Bayesian model of Lasota and Niemiro (Pattern Recognition 36 (2003) 931) for the underlying images. This latter model takes advantage of the fact that medical images often consist of relatively few grey-levels of unknown intensity. The algorithm of Lasota and Niemiro (Pattern Recognition 36 (2003) 931) is used in the image restoration part of the PET algorithm, essentially as a noise-filtering and smoothing device. It is now equipped with an additional data reconstruction step. We include simulation results which suggest that the method is truly reliable. We also describe a version of the basic algorithm, in which a random simulation step is replaced by computation of expected value, similarly as in the EM algorithm.     

An evolutionary Monte Carlo algorithm for Bayesian block clustering of data matrices

In many applications, it is of interest to simultaneously cluster row and column variables in a data set, identifying local subgroups within a data matrix that share some common characteristic. When a small set of variables is believed to be associated with a set of responses, block clustering or biclustering is a more appropriate technique to use compared to one-dimensional clustering. A flexible framework for Bayesian model-based block clustering, that can determine multiple block clusters in a data matrix through a novel and efficient evolutionary Monte Carlo-based methodology, is proposed. The performance of this methodology is illustrated through a number of simulation studies and an application to data from genome-wide association studies.