Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization.We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production measures.With the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front analysis.After experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for training.In addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate reservoirs.The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.

Learning Bayesian network structure with immune algorithm

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.

Learning Bayesian networks using genetic algorithm

A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

Causal constraint pruning for exact learning of Bayesian network structure

How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper,four different causal constraints algorithms are added into score calculations to prune possible parent sets,improving state-ofthe-art learning algorithms’efficiency.Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy.Under causal constraints,these exact learning algorithms can prune about 70%possible parent sets and reduce about 60%running time while only losing no more than 2%accuracy on average.Additionally,with sufficient samples,exact learning algorithms with causal constraints can also obtain the optimal network.In general,adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

EARLY CATARACT DETECTION BY DYNAMIC LIGHT SCATTERING WITH SPARSE BAYESIAN LEARNING

Dynamic light scattering(DLS)is a promising technique for early cataract detection and for studying cataractogenesis.A novel probabilistic analysis tool,the sparse Bayesian learning(SBL)algorithm,is described for reconstructing the most-probable size distribution ofα-crystallin and their aggregates in an ocular lens from the DLS data.The performance of the algorithm is evaluated by analyzing simulated correlation data from known distributions and DLS data from the ocular lenses of a fetal calf,a Rhesus monkey,and a man,so as to establish the required efficiency of the SBL algorithm for clinical studies.

Self-Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases

The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.

Personalized movie recommendation method based on ensemble learning

Aiming at the personalized movie recommendation problem,a recommendation algorithm in-tegrating manifold learning and ensemble learning is studied.In this work,manifold learning is used to reduce the dimension of data so that both time and space complexities of the model are mitigated.Meanwhile,gradient boosting decision tree(GBDT)is used to train the target user profile prediction model.Based on the recommendation results,Bayesian optimization algorithm is applied to optimize the recommendation model,which can effectively improve the prediction accuracy.The experimental results show that the proposed algorithm can improve the accuracy of movie recommendation.

改进贝叶斯网络在变压器故障诊断中的应用

针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。

一种用于变压器故障诊断的贝叶斯网络优化方法

针对变压器故障诊断效率低的问题,文中将油中溶解气体分析与人工智能方法相结合,提出了一种改进蝗虫优化算法优化贝叶斯网络的变压器故障诊断方法。利用差分进化算法和与模拟退火算法对蝗虫算法进行改进,提高了算法的优化能力。将改进蝗虫算法应用于贝叶斯网络结构来学习构建变压器故障诊断模型,利用所提方法对变压器进行故障诊断。实验结果表明,该方法诊断正确率达到了92.7%,与其他算法所构建的诊断模型相比具有更高的故障诊断准确率。

基于BA-MKELM的微电网故障识别与定位

提出一种基于贝叶斯算法优化多核极限学习机的微电网故障识别和定位方法。针对极限学习机输入参数和隐含层节点数随机选取导致回归能力不足的问题,引入核函数,将多项式与高斯径向基核函数加权组合构成多核极限学习机建立故障识别与定位模型,并采用贝叶斯算法对多核极限学习机相关参数进行优化,进一步提高模型的逼近能力。为了验证所提模型的故障识别与定位性能,选用极限学习机和多核极限学习机分别建立故障诊断模型进行比较分析。实验结果表明,所提方法能够高性能地识别和定位微电网中任何类型的故障,识别和定位精度更高。

基于遗传算法的Bayesian网结构学习研究

从不完备数据中学习网络结构是 Bayesian网学习的难点之一 ,计算复杂度高 ,实现困难 .针对该问题提出了一种进化算法 .设计了结合数学期望的适应度函数 ,该函数利用进化过程中的最好 Bayesian网把不完备数据转换成完备数据 ,从而大大简化了学习的复杂度 ,并保证算法能够向好的结构不断进化 .此外 ,给出了网络结构的编码方案 ,设计了相应的遗传算子 ,使得该算法能够收敛到全局最优的 Bayesian网结构 .模拟实验结果表明 ,该算法能有效地从不完备数据中学习 .

近似图引导的演化贝叶斯网络结构学习算法

贝叶斯网络结构学习是贝叶斯网络推理及应用的基础.搜索高质量的节点序是贝叶斯网络结构学习的一类重要方法.针对在节点序空间中,搜索高质量节点序存在的难以高效、准确评估解的问题,本文提出了一种近似图引导的演化贝叶斯网络结构学习算法.首先,该算法利用互信息构建无向近似图;其次,该算法通过结合节点序和无向近似图构造有向图结构,将其贝叶斯信息准则评分作为节点序的适应度来高效评估节点序,并在演化优化的框架下,使用提出的基于Kendall Tau Distance的交叉算子和基于逆度的变异算子搜索最优节点序;最后,将搜索到的最优节点序输入K2算法得到其对应的贝叶斯网络结构.在4种不同规模网络上的实验结果表明,该算法在收敛时间和准确度之间取得了较好的平衡,其评分相较于对比算法中的次优解分别提升了10.91%、12.28%、53.96%、10.87%.

基于遗传算法的Bayesian网结构增量学习的研究

已建成的Bayesian网与领域环境间可能存在较大偏差,加之领域本身固有的动态变化特性,因此在观察到新数据时,改善Bayesian网的性能和优化网络结构是十分必要的.提出了一种基于遗传算法的Bayesian网(包含结构和参数)求精算法.该算法基于上次的求精结果把已有的不完备数据转化成完备数据,以期望充分统计因子作为已有数据的主要存储形式,基于本次求精过程中的当前最佳个体对新数据进行完备化,并由遗传操作综合利用新数据和已有数据进行求精.模拟实验结果表明,该增量学习算法能较有效地从不完备数据中求精Bayesian网.

基于Bayesian改进算法的回转窑故障诊断模型研究

贝叶斯网络是数据挖掘最有效和可靠的方法之一,而贝叶斯网络结构学习是贝叶斯网络研究的关键环节。针对现有经典结构学习算法——爬山算法易陷入局部最优、效率低的问题,通过计算互信息建立最大支撑树,并将最大支撑树与简化爬山算法相结合,提出了一种新的贝叶斯网络结构学习改进算法。通过与经典的爬山法和K2算法进行比较,结果表明该改进算法不仅能够得到较高准确率的模型,而且能够提高模型建立的效率。最后基于该改进算法,结合冀东水泥集团的水泥回转窑现场运行数据,建立了水泥回转窑故障诊断模型,实现了精确快速的故障诊断。

基于Bayesian学习的适应性优化协商模型

在复杂的自动协商环境中,设计能够处理不完全信息和动态情形的协商agent有效学习机制正成为具有挑战性的议题.提出了一种基于Bayesian学习的时间依赖的双边多议题协商优化模型(BLMSEAN).通过只观察对手的历史报价,将Bayesian学习和基于混合策略的演化算法相结合,所提模型使得协商agent能够对于对手协商参数的概率分布有更精确的估计(如期限、保留报价和议题权重等),能够适应性地调整让步策略使协商双方都受益,提高了协商的成功率和效用.通过实验可以显示所提的模型学习对手私有信息和适应性调整让步策略的有效性.

传统机器学习模型的超参数优化技术评估

合理的超参数能够保证机器学习模型适应不同背景和不同任务。为了避免在模型超参数数量过多、搜索空间过大的情况下出现手动调节导致的效率低下问题,多种超参数优化技术已经被研发并运用到机器学习模型训练中。文中首先回顾了8种常见的超参数优化技术,即网格搜索、随机搜索、贝叶斯优化、Hyperband、BOHB、遗传算法、粒子群优化算法和协方差矩阵自适应进化策略,并从时间性能、最终结果、并行能力、可拓展性、稳健性和灵活性5个方面分析各类方法的优缺点。其次,将8种方法应用到LightGBM、XGBoost、随机森林和KNN这4种传统机器学习模型上,在4个基准数据集上完成了回归、二分类和多分类的实验,对各类方法进行了比较。最后总结了各类方法的优缺点,给出了不同方法的适用情景。

基于星载光子计数雷达数据的森林郁闭度估测模型优化

【目的】旨在评估星载光子计数雷达数据估测森林郁闭度(Forest Canopy Closure,FCC)的潜力,为优化森林管理规划提出一种高效率、低成本估测区域尺度FCC的新技术方法。【方法】研究以星载激光雷达ICESat-2/ATLAS光子点云数据为信息源,以滇西北生态脆弱区香格里拉为研究区,结合54块地面实测样地数据,在前期对点云数据进行去噪、分类等预处理的基础上,对研究区74808个有林地光斑冠层参数进行提取(共计59个),使用支持向量机的递归特征消除算法(SVM-RFE)优选特征变量,采用普通克里格(OK)插值出区域尺度特征变量的空间分布,基于贝叶斯优化(BO)算法改进后的随机森林(RF)、K-最近邻值法(KNN)、梯度回归(GBRT)模型建模,以决定系数(R^(2))、均方根误差(RMSE)、总体预测精度(P)、残差平方和(R_(SS))和相对均方根误差(R_(RMSE))作为模型评价指标,以此构建研究区FCC估测模型。【结果】1)由ICESat-2/ATLAS提取的光斑冠层参数经SVM-RFE优选后,6个(asr、n_toc_photons、n_ca_photons、h_min_canopy、toc_roughness、photon_rate_can)冠层参数的平均交叉验证精度高为0.60,可作为OK插值变量;2)以优选的冠层参数作为OK插值变量拟合最佳半方差函数,所有变量的块金效应(SR<25%)较弱,具有强烈的空间自相关性,除asr变量的最佳拟合模型为球状模型(R^(2)=0.689,R_(SS)=2.05×10^(-6),R_(RMSE)=0.1602)外,其余5个变量的最佳拟合模型均为指数模型(R^(2),0.71~0.93;R_(SS),2.34×10^(-9)~1.54×10^(-4);R_(RMSE),0.0239~0.1886);3)在BO-RF、BO-GBRT、BO-KNN郁闭度估测模型中,以BO-RF模型综合建模精度最高(R^(2)=0.73,RMSE=0.09、P=80.13%),可作为研究区FCC遥感估测模型;4)使用BO-RF模型估测的研究区FCC进行空间制图,均值为0.53,主要分布在0.3~0.6之间,占比77.44%。FCC高值区域总体呈现出由东南向北延伸分布的趋势,与研究区森林资源分布情况基本一致。【结论】该方法可为优化森林资源管理提供一种技术与方法上的参考。

链式回转弹仓区间不确定性动力学模型

针对具有区间不确定性参数的辨识问题,提出一种基于区间可能度转换模型的区间不确定性参数的双层嵌套辨识(Double-layer Nested Identification,DNI)方法。通过将待辨识参数分为两类,利用DNI方法辨识出第1类确定性参数,再通过基于DNI思想的区间优化方法优化第2类区间不确定性参数的区间范围;面向嵌套策略类型方法计算量庞大且效率低的问题,选用贝叶斯优化-粒子群优化(Bayesian Optimization-Particle Swarm Optimization,BO-PSO)方法作为内层算法以提高求解效率。DNI方法的内层利用BO-PSO方法计算区间上下界,外层利用改进型布谷鸟搜索(Improved Cuckoo Search,ICS)方法辨识特定参数。为进一步缩短求解时间,提出一种ICS多核极限学习机(ICS-Multiple Kernel-Extreme Learning Machine,ICS-MK-ELM)代理模型,ICS-MK-ELM代理模型克服了人工调节每个核函数超参数的困难,并且模型预测精度明显高于核ELM(Kernel ELM,KELM)和MK-ELM;将DNI方法应用于链式回转弹仓的参数辨识,解决了链式弹仓具有区间不确定性参数的辨识困难的问题,参数辨识结果表明所提DNI方法以及基于DNI思想的区间优化方法具有更高的精度和稳定性。