An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization

A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In this study,a hybrid feature selection model was developed based on ant colony opti-mization(ACO)and k-nearest neighbor(kNN)classifier to investigate the rela-tionship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing(UNOS).The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’functional status(i.e.,quality of life)after lung transplantation.The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS.The developed approach examined the reliability and validity of the lung allocation process.The results are promising regarding accuracy prediction to be 91.3%and low computational time,along with better decision capabilities,emphasizing the potential for automatic classification of the lung and other organs allocation pro-cesses.In addition,the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung alloca-tion strategies.Having such ACO-kNN model,a medical professional can sum-marize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ.

基于VPNRS-RF的飞机液压系统故障诊断模型

针对飞机液压系统故障诊断特征值冗余难以精准获取和准确率不高的问题,提出一种基于变精度邻域粗糙集(Variable Precision Neighborhood Rough Set,VPNRS)和随机森林(Random Forest,RF)相结合的特征选择算法,并以此为基础建立飞机液压系统故障诊断模型。VPNRS-RF算法主要是利用随机森林算法分别对变精度邻域和模糊熵粗糙集约简后的特征进行重要度排序,再次筛选后确定最优特征子集,使用最优特征子集对在线贯序极限学习机(OSELM)分类模型进行训练,从而提高故障信息获取准确率。最后以飞机起落架收放系统为例进行仿真研究,验证了VPNRS-RFOSELM模型的优越性。

New immune multiobjective optimization algorithm and its application in boiler combustion optimization

In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.

复合材料风力机叶片参数化设计及优化

风力机叶片重量过大不仅会影响叶片的结构性能,还会增加运输、维修等成本。为解决叶片质量问题,本文选取经典的1.5 MW复合材料风力机叶片,叶片主梁、腹板、前缘和后缘分区铺设不同的复合材料。由于复合材料的铺设厚度从叶根向叶尖呈现递减趋势,且失效位置也多位于叶根处,故取距离叶根约1/3部分为研究对象来简化问题。为便于后期优化,采用ANSYS APDL参数化构建复合材料风力机叶片模型。考虑到陷入局部最优的情况,选择子集模拟优化方法进行随机优化。设计变量为腹板位置,约束函数为极限载荷下叶片满足位移及Tsai-Wu准则校核,目标为叶片重量最小。通过30组子集模拟优化统计结果可知:当腹板位置约在29.4%、53.3%弦长处时,相较于确定性设计,叶片减重约15.4%。最终结果表明,该优化方法在风力机叶片结构优化方面具有可行性,且优化结果对工程设计具有参考价值。

An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data:A COVID-19 Case Study

Feature Subset Selection(FSS)is an NP-hard problem to remove redundant and irrelevant features particularly from medical data,and it can be effectively addressed by metaheuristic algorithms.However,existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method,resulting in suboptimal solutions that hinder diagnosis and prediction accuracy.This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm(IBQANA)for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms.The proposed IBQANA’s contributions include the Hybrid Binary Operator(HBO)and the Distance-based Binary Search Strategy(DBSS).HBO is designed to convert continuous values into binary solutions,even for values outside the[0,1]range,ensuring accurate binary mapping.On the other hand,DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence.By combining exploration and exploitation phases based on an adaptive probability function,DBSS effectively avoids local optima.The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets,with feature numbers ranging from 8 to 10,509.IBQANA's effectiveness is evaluated regarding the accuracy,fitness,and selected features and compared with seven binary metaheuristic algorithms.Furthermore,IBQANA is utilized to detect COVID-19.The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets.The proposed method presents a promising solution to the FSS problem in medical data preprocessing.

Solving the subset sum problem by the quantum Ising model with variational quantum optimization based on conditional values at risk

The subset sum problem is a combinatorial optimization problem,and its complexity belongs to the nondeterministic polynomial time complete(NP-Complete)class.This problem is widely used in encryption,planning or scheduling,and integer partitions.An accurate search algorithm with polynomial time complexity has not been found,which makes it challenging to be solved on classical computers.To effectively solve this problem,we translate it into the quantum Ising model and solve it with a variational quantum optimization method based on conditional values at risk.The proposed model needs only n qubits to encode 2ndimensional search space,which can effectively save the encoding quantum resources.The model inherits the advantages of variational quantum algorithms and can obtain good performance at shallow circuit depths while being robust to noise,and it is convenient to be deployed in the Noisy Intermediate Scale Quantum era.We investigate the effects of the scalability,the variational ansatz type,the variational depth,and noise on the model.Moreover,we also discuss the performance of the model under different conditional values at risk.Through computer simulation,the scale can reach more than nine qubits.By selecting the noise type,we construct simulators with different QVs and study the performance of the model with them.In addition,we deploy the model on a superconducting quantum computer of the Origin Quantum Technology Company and successfully solve the subset sum problem.This model provides a new perspective for solving the subset sum problem.

基于灰色关联分析法的物联网数据异常检测方法

物联网数据具有构成复杂、规模庞大、异常形式多样化的特征,导致基于传统异常检测方法得到的结果正确率偏低。文中提出了一种基于灰色关联分析法的物联网数据异常检测方法。该方法利用灰色关联模型计算得到灰色关联系数,对原始物联网数据中的自有变量相关性进行排序,随后采用前向选择法进行变量排序选择,确定反推得到的特征参数误差达到最小值时的变量,并将其作为最优变量子集;在异常检测阶段,用最优变量子集对物联网数据进行异常位置寻优,实现异常检测。测试结果表明,该方法在不同攻击下,异常数据检测结果的F1值未出现明显的波动,对应的F1值为0.84~0.90。

Development of Granular Fuzzy Relation Equations Based on a Subset of Data

Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy rules.As each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant relevance.In this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy rules.The subset of rules is selected by maximizing their performance of the obtained solutions.The originality of this study is conducted in the following ways.Starting with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and efficiency.Then,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.

改进灰色马尔科夫模型算法的快递物流路径配送及最优选址研究

电子商务的发展推动着快递物流需求量的增加,但各配送节点之间的服务效率以及快递“爆仓”所带来的时间制约严重影响着当前快递业的发展。基于此,借助灰色模型以及最优子集法对灰色马尔科夫模型进行改进,并对快递物流动态选址以及快递需求量两方面进行考虑。实验结果表明,改进Grey Markov模型的物流快递平均迭代速率和平均相对误差达到了7.64%和0.0623,路径节点配送效率提升了13.24%,收敛时间误差波动值小于1.29s。该模型能有效对快递物流进行动态规划,在合理范围内实现最优选址。

基于多目标快速探索随机树的移动机器人巡检路径优化方法

针对移动机器人需要访问多目标的巡检路径规划问题,该文提出一种多目标快速探索随机树路径优化方法。首先,根据提供的环境地图与巡检目标点,该文采用一种RRT-Connect-ACO算法得到目标点的巡检顺序和可行路径;然后,通过引入信息子集,对路径进行优化,得到最终的最优路径。实验结果表明,与现有的多目标路径规划算法相比,该方法考虑了地形的影响,得到的最优路径更符合实际情况。

一种建筑工程场地参数V_(S30)的外推模型修正方法

为提高覆盖土层较浅工程场地上钻孔的外推V_(S30)值精度,选取日本KiK-net台站中符合一定条件的钻孔数据,利用相关系数矩阵排除共线性问题以确定可用于回归的3个体现钻孔剖面特性的参数,通过最优子集法建立常数外推V_(S30)残差与这3个参数的优选函数关系,利用调整后R^(2)、贝叶斯信息准则和k倍交叉验证3种特征选择方法确定其中的最优关系。根据修正后预测V_(S30)与实际观测V_(S30)间残差均值和标准差的分布给出推荐的常数外推模型修正函数。结果表明,该V_(S30)的外推模型能够较好提高底部常速度模型的预测精度,并且在新疆地区具有一定适用性。提出的方法可以对中国不同地区的场地V_(S30)经验估计模型建立提供参考。

改进ReliefF算法在北斗信号干扰特征选择中的应用

北斗导航信号在城市环境中容易受到复杂的干扰,这些干扰严重影响北斗接收机的定位精度。干扰特征中包含着对评估目的贡献较少的特征,这部分贡献较少的特征会影响分类结果的准确性。论文仿真了北斗卫星干扰信号并对信号进行特征提取组成预选特征集。同时提出了一种使用新的距离计算方式并可在所有样本核心圈进行迭代的改进型Re⁃liefF算法。可行性验证表明改进型ReliefF算法在选取特征为6维的情况下分类精度提升2%。最后使用改进型ReliefF算法提出预选特征集中贡献较大的部分,实现了由12维到8维的特征降维,得到8维最优特征子集。

基于VBA编程的全子集模型筛选算法

文章基于VBA编程方法,实现对全部子集模型的筛选。结果表明:用VBA方法实现的模型估计,操作方法简单友好,可以充分利用EXCEL灵活的数据处理与数据交换功能完成全部计算和分析工作;无须太过严格的模型设定,基于变量之间的相关性假设选择原始自变量和派生出自变量,构建符合要求的回归模型;设计的优化算法科学精巧,可以大大减少计算量;可以按特定要求进行全子集模型搜索,获得满足需要的模型;用多个优选模型进行联合预测、边际效应分析、弹性系数计算,可避免单一模型的偶然性因素影响。

采用优化特征子集选取和改进SVR的养殖禽舍温度预测算法

为提高养殖禽舍温度预测算精度,降低数据冗余度和差异性对预测结果的影响,提出一种基于智能优化特征子集选取和模糊聚类改进SVR(Support Vector Regression)的温度预测模型。首先,构建最优特征子集选取模型,通过设计最优特征子集选取指标,以降低特征之间冗余度和数据维度;采用改进的离散灰狼算法对特征子集选取模型进行求解,以实现最优特征子集选取。其次,建立模糊聚类改进SVR预测机制,通过设计多度量核FCM(Fuzzy C-means)算法,以实现数据样本自动分类;提出与数据样本分类相对应的SVR预测算法,并采用灰狼算法对SVR参数进行优化,最大程度降低样本数据差异性对预测精度的影响。最后,融合最优特征子集选取和模糊聚类改进SVR预测机制,以实现养殖禽舍温度高精度预测。仿真结果表明,该算法实现不同季节条件下养殖禽舍温度的高精度预测,相比于其他预测算法,预测精度提高约23.7%~37.8%。所提养殖禽舍温度预测算法具有良好的预测性能,具有一定的推广应用价值。

木材抗拉强度的近红外光谱MC-UVE-IVSO建模方法

木材抗拉强度是评价木材力学性质的重要指标。针对近红外光谱建模中样本数据量小、波长信息冗余所导致预测模型精度低的问题,提出一种基于模型集群分析MC-UVE-IVSO波长优选的木材抗拉强度建模方法。以桦木为例,选取150个桦木样本作为实验对象,首先使用900~1700 nm波段的近红外光谱仪采集试件光谱数据,并采用力学试验机获得相应的抗拉强度真值;然后对采集的光谱数据运用多元散射校正(MSC)、一阶求导和卷积平滑(SG)相结合的方法进行预处理,完成光谱平滑滤波;分别采用变量组合集群分析算法(VCPA)、蒙特卡罗无信息变量消除法(MC-UVE)、迭代变量子集优化算法(IVSO)及MC-UVE-IVSO组合优化算法进行波长筛选,并对比优选波长结果;最后在优选近红外波长基础上,建立桦木抗拉强度的偏最小二乘预测模型(PLS)。实验结果表明:基于MC-UVE-IVSO算法优选波长的PLS模型,光谱变量数由512减小到98,优选波长占总波长的19%,其预测决定系数R^(2)为0.94,预测均方根误差RMSEP为7.50,性能偏差比RPD为3.16,相比于全波段、MC-UVE、VCPA、MC-UVE-VCPA与IVSO相应的R^(2)(0.92、0.93、0.82、0.87、0.93)、RMSEP(17.91、11.7、14.91、12.12、8.47)和RPD(2.81、2.91、2.25、2.28、2.78)均有不同程度提升;通过统计特征波长所建立的预测模型箱形图,进一步证明了MC-UVE-IVSO算法在处理多变量波长的稳定性。实验结果表明,MC-UVE方法可以消除与建模不相关的多数变量,而IVSO算法能有效搜索出最优变量子集,基于MC-UVE-IVSO的光谱优选算法提升了木材抗拉强度预测模型的准确性和稳定性,为木材近红外光谱的无损、快速与精准检测提供了一定的理论基础。

基于不同MCMC抽样子集模拟的模型修正方法对比研究

子集模拟方法作为结构可靠度分析方法,也可应用于工程优化问题,诸如优化设计、模型修正等.为研究基于不同蒙特卡洛马尔可夫链(MonteCarloMarkovChain,MCMC)抽样的子集模拟优化方法(Subset Simulation Optimization,SSO),以有限元模型修正作为优化背景问题,开展其精度和效率的对比研究.介绍标准SSO和子集模拟(Subset Simulation,SS)常见的MCMC抽样方法,并基于上述不同MCMC抽样的SSO开展某局部损伤悬臂梁(10维变量)的有限元模型修正,修正结果与基于遗传算法(Genetic Algorithm,GA)的模型修正方法进行对比;而后将上述不同MCMC抽样的SSO修正方法应用于某四层钢框架有限元模型修正中(11维变量).结果表明,采用随机游走的延迟拒绝修正M-H方法(MMH algorithm with Delayed Rejection,MMHDR)和自适应条件抽样方法(Adaptive Conditional Sampling,ACS)的SSO有限元模型修正具有较好的精度和效率,在工程结构有限元模型修正中更具优势.

基于改进最优子集算法的癌症数据分析

随着技术的不断进步,大量的数据可以通过微阵列芯片等传感器收集到。癌症检测中,可以使用机器学习方法分析癌症微阵列数据。但是,机器学习方法在遇到超高维度的情况时表现不佳。文章提出使用Ball相关系数和Abess方法相结合的Ball-Abess方法来解决癌症微阵列数据遇到的超高维问题。与其他分类方法相比,利用该方法能够得到更好的结果。

基于蚁群优化的特征选择新方法

利用蚁群优化算法解决特征选择问题,以获得能代表问题空间的较优特征子集,并能降低分类系统的搜索空间。以航空纹理影像的特征选择和分类问题为例,利用主分量变换和蚁群优化算法分别对原始纹理影像特征集合进行特征提取、选择和分类。结果表明,本文方法不仅能够降低图像特征空间维数,减少图像分类的工作量,而且还可以提高分类识别的正确率。

最优子集回归方法在季节气候预测中的应用

利用DEMETER计划多个模式的模拟资料研究1959～2001年多模式集合预报的季节降水在中国区域的表现,并结合最优子集回归（OSR）方法对中国区域的季节降水进行降尺度预报,比较其与多模式集合预报的技巧。研究表明：多个单模式在中国区域对季节降水的模拟性能普遍较差,多元线性回归（MLR）集合的预报技巧不如集合平均（EM）。利用OSR方法进行降尺度预报可以极大改善中国区域季节降水的预报技巧。夏季,降水距平相关系数（ACC）在长江以南、西藏以及内蒙古中部等地区提高很显著,ACC在中国区域的平均达到0.29,明显高于多模式集合平均与多元线性回归集合。冬季,OSR方法可以改善多模式集合在中国北方地区较低的预报技巧。概率Brier技巧评分（BSS）也表明了OSR方法对季节降水预报的改善。需要说明的是,虽然OSR方法在中国区域能明显提高季节降水的预报技巧,但是其选取的预报因子与中国区域季节降水的物理机制问题仍有待于进一步的研究。

基于均生函数-最优子集回归预测模型的青藏高原气温和降水短期预测

以青藏高原78个站点50年的逐年降水和温度数据为基础,使用SOFM人工神经网络模型对高原的降水和温度变化进行了分区,并采用均生函数-最优子集回归(MGF-OSR)预测模型对青藏高原的降水和温度进行了5年情景的预测。预测结果表明:总体而言,今后5年青藏高原的降水年际波动较大,并没有显著的趋势;但青海东南和西藏东部部分地区有明显的减少。青藏高原的总体温度变化增加趋势显著,仅高原东南部明显降温。