用于不确定性分析的高斯过程响应面模型设计点选择方法

为推动高斯过程响应面模型在复杂耗时数值模拟不确定性分析中的应用,提出一种可自动实现位置优化的高效设计点选择方法,即先在标准超立方体上生成拉丁超立方设计点,然后利用输入变量的已知概率分布将其映射回原始设计空间.将该方法与基于假设均匀分布的传统拉丁超立方设计方法进行比较,探讨它们对所建立的高斯过程响应面模型和不确定性分析结...     

车身响应面模型参数的拉丁超立方采样仿真

针对传统汽车车身采样方法没有计算减薄率,导致方法存在精度低、耗时长等问题,提出一种基于车身响应面模型参数的拉丁超立方采样法.运用参数优化对车身的质量做综合评价,获得参数的最大减薄率,通过设计样品点建立响应面近似模型,同时根据确定性系数来反映模型的优化目标和设计变量之间的规律,采用拉丁超立方采样法将样品点在设计空间内合理划分,运算获得目标函数的最小值,通过均匀性评价参数来保证样品的高性能和均衡性.仿真结果表明,所提方法能够提高车身的质量与精度,相比其它方法具有较强适用性、准确性以及优质的鲁棒性.     

融合核函数在改进径向基代理模型中的应用

针对径向基代理模型技术在近似高维问题时预测性能较差的不足,提出一种基于融合核函数的改进径向基代理模型技术。在拉丁超立方设计抽样不均匀的情况下,通过定义一种辅助函数与距离评判标准,提出基于均匀抽样的拉丁超立方设计,并应用于代理模型的构建中;为提高模型预测精度与计算效率,考虑样本点因素,采用局部密集加点、全局均匀选点和最小距离筛选的多策略建模技术构建径向基代理模型;同时,为避免该技术在近似高维问题时可能产生的结构风险,考虑结构因素对预测精度的影响,对逆多二次和立方核函数进行了权重式的融合,构建了基于融合核函数的改进径向基代理模型。利用数值和工程算例进行测试仿真,结果表明该技术不仅满足精度要求,且明显提高计算效率,具有更高的预测稳定性。     

拉丁超立方抽样在传染性疾病风险分析中的应用

传染性疾病,即人们常说的传染病,自古以来就是困扰人类社会的重大问题。近年来随着经济全球化的进程不断深化,传染病流行爆发的可能性不断增加,对传染病进行风险分析就显得越发重要。风险评估方法通常可分为定性和定量分析两大类,其中定量分析是通过数学建模试图将安全风险进行量化分析评估的一种方法。拉丁超立方抽样法是目前定量风险分析中较常使用的一种基于蒙特卡洛模拟的分层抽样方法。由于该方法抽样得到的输入随机变量的样本空间总是比随机抽样的覆盖面大,使得拉丁超立方抽样得到广泛推广及应用。本文对风险分析和拉丁超立方抽样法的基本原理进行介绍,并重点回顾了拉丁超立方抽样法在人类传染性疾病,动物传染性疾病以及人畜共患传染性疾病相关的风险分析中的应用。通过本文的论述,是人们对于拉丁超立方抽样法有了更清晰的概念,并了解其在当前传染性疾病预警中的应用前景。     

基于改进离散型萤火虫群优化算法和分形维数的属性选择方法

属性选择是数据挖掘领域中数据预处理的一个重要方法。文中提出一种融合离散型萤火虫群优化算法(DGSO)与分形维数的属性选择方法。该方法以分形维数作为属性子集的评估度量准则,以DGSO作为搜索策略。为分析该方法的可行性和有效性,采用6个UCI数据集进行实验。结合10-fold交叉验证和SVM对属性选择前后的分类准确率进行分析,并进行搜索策略和评估度量准则间的性能对比及详细的参数分析。结果表明该方法具有较高的可行性和有效性。     

基于贝叶斯优化的作战仿真方法

随着仿真规模的扩大,仿真过程中的影响因素增多,仿真方案空间呈指数级增长,对仿真提出新的挑战.提出贝叶斯优化仿真方法,基于仿真结果构建高斯回归模型,并采用序贯策略逐步在线学习更新高斯回归模型,从而不断优化实验方案.仿真结果显示,相对于近正交拉丁超立方实验设计方法,寻找作战效能相同的试验方案,采用贝叶斯优化试验方法所需的实验次数更少;在相同时间条件,新方法找到的实验方案作战效能更高,有效证明了算法的有效性.     

考虑随机不确定性的常态仿真模型确认试验设计

模型确认试验是一种新的试验,其目的在于度量仿真模型的可信度.为了得到低成本、高可信度的模型确认试验方案,提出一种随机不确定性模型确认试验设计方法.首先,基于面积确认度量指标提出一种新的无量纲的模型确认度量指标(面积确认度量指标因子),并且在其基础上发展了基于专家系统的仿真模型准确性定性评判准则;然后,建立随机不确定性模型确认试验优化设计模型,提出该优化模型的求解方法;最后,通过两个数值算例对提出的模型确认试验设计方法进行验证.结果表明,小样本情况下,试验方案的随机性会影响模型评判结果的可信度;面积度量指标因子随试验样本数量的增加而收敛;随机不确定性模型确认试验设计方法能够避免试验方案对模型确认结果的影响.     

降低OFDM立方度量的最优限幅滤波算法及神经网络实现

正交频分复用(OFDM)信号的一个主要缺点是信号包络波动过大。峰均功率比是常用的度量OFDM信号包络波动大小的指标,而近期研究表明立方度量可以更加准确地度量OFDM信号包络波动。传统限幅滤波技术可以有效降低立方度量,但其滤波设计并不能保证处理后的信号性能达到最优。针对这一问题,提出了一种最优的限幅滤波设计方案来降低立方度量,其关键思想是考虑滤波操作对信号带内、带外部分的影响,将滤波器设计建模为一个优化问题,通过求解得到最优的滤波器,并与限幅操作结合降低立方度量。由于优化问题的求解复杂度较高,还提出了一种基于深度神经网络的最优限幅滤波实现方案。仿真结果表明,所提出的最优限幅滤波算法及其神经网络实现方案性能相当,但后者的复杂度要低得多。与其它的已知算法相比,新提出的算法及其神经网络实现方案的性能都具有明显的优势。     

翼型气动特性的集成优化方法

为减少汽车翼型气动特性的数值仿真计算量并提高优化效率,利用Isight和拉丁超立方抽样得到实验矩阵并进行数值仿真,基于仿真结果建立近似模型,使用进化算法进行全局优化．结果表明：该集成优化方法能得出合理的翼型安装参数,而且仅依赖于近似模型的优化过程就能根据不同的约束条件和优化目标快速得出合理的结果,显著缩短优化周期．     

基于多工况的车门结构多目标优化设计方法研究

车门结构的单一工况优化设计常难以满足车门设计要求。本文基于车门垂直刚度和一阶模态两工况,提出一种车门结构多工况多目标优化设计方法。以车门垂直刚度、一阶频率和车门质量为优化目标,通过均匀拉丁方试验设计和响应面方法得出车门垂直刚度和一阶频率的近似数学模型,并在此基础构建车门结构多目标优化模型。最后采用多目标遗传算法对其进行求解,最终优化结果在车门质量减少的情况下,垂直刚度和一阶频率均达到设计要求,达到预期优化效果。     

最优实验设计的改进随机进化算法

详细阐述构造最优实验设计的原始随机进化算法,并在原始算法的基础上,拓展广度搜索,改进深度搜索,以提高最优实验设计的计算速度。通过不同规模和不同优化准则的拉丁超立方体最优实验设计,验证改进算法的应用效果。算例分析表明,改进算法能够比原始算法节省约30%～60%的机时完成最优实验设计,而且改进算法对应于优化准则的最优值与原始算法最优值的差别仅为1%～3%。可见,改进算法能够兼顾最优实验设计的计算时间和优化质量,明显提高最优实验设计的构造效率。     

面向复杂装备精密产品质量特性的Kriging-RBDO可靠性优化设计

不确定设计参数情形下的复杂装备柔顺机构精密产品质量特性波动与可靠性疲劳退化是精密微机电系统领域的基础性工程难题.针对这一基础性工程难题,提出一种面向复杂装备柔顺机构精密产品可靠性优化设计模型.利用拉丁超立方试验设计(Latin hypercube design,LHD)构建试验设计组合方案,通过有限元数值模拟获取各试验设计组合方案的质量特性值.据此,采用Kriging代理模型建立质量特性与不确定设计参数之间复杂非线性函数关系模型.在此基础上,引入基于可靠性优化设计(Reliability-based design optimization,RBDO)策略,构建面向复杂装备柔顺机构精密产品Kriging-RBDO可靠性优化设计模型.算例表明,所提出的方法在不确定设计参数情形下的复杂装备柔顺机构精密产品早期质量设计方面具有良好的抗疲劳退化特性.     

The integration of design of experiments, surrogate modeling and optimization for thermoscience research

This paper presents an integrated approach for the solution of complex optimization problems in thermoscience research. The cited approach is based on the design of computational experiments (DOE), surrogate modeling, and optimization. The DOE/surrogate modeling techniques under consideration include: A-optimal/classical linear regression, Latin hypercube/artificial neural networks, and Latin hypercube/Sugeno-type fuzzy models. These techniques are coupled with both local (modified Newtons method) and global (genetic algorithms) optimization methods. The proposed approach proved to be an effective, efficient and robust modeling and optimization tool in the context of a case study, and holds promise for use in larger scale optimization problems in thermoscience research.     

Neural network and regression spline value function approximations for stochastic dynamic programming

Dynamic programming is a multi-stage optimization method that is applicable to many problems in engineering. A statistical perspective of value function approximation in high-dimensional, continuous-state stochastic dynamic programming (SDP) was first presented using orthogonal array (OA) experimental designs and multivariate adaptive regression splines (MARS). Given the popularity of artificial neural networks (ANNs) for high-dimensional modeling in engineering, this paper presents an implementation of ANNs as an alternative to MARS. Comparisons consider the differences in methodological objectives, computational complexity, model accuracy, and numerical SDP solutions. Two applications are presented: a nine-dimensional inventory forecasting problem and an eight-dimensional water reservoir problem. Both OAs and OA-based Latin hypercube experimental designs are explored, and OA space-filling quality is considered.     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

Orthogonal-column Latin hypercube designs with small samples

Latin hypercube designs with zero pair-wise column correlations are examined for their space-filling properties. Such designs, known as orthogonal-column Latin hypercube designs, are often used in computer experiments and in screening experiments, since all coefficients in a first-order model are estimated independently of each other. This makes interpretation of the factor effects particularly simple. Complete or partial enumeration searches are carried out to investigate the space-filling properties of all orthogonal-column Latin hypercube designs, with from 5 to 9 runs, and, from 2 to 5 factors. In cases where there are several designs with similar properties, the designs with minimum mean squared distance are determined. The maximum number of factors that can be accommodated in orthogonal-column Latin hypercube designs is determined for each design size, and designs found by various algorithmic methods proposed in the literature are identified.     

拉丁超立方体抽样遗传算法求解图的二划分问题

图的二划分问题是一个典型的NP-hard组合优化问题, 在许多领域都有重要应用. 近年来, 传统遗传算法等各种智能优化方法被引入到该问题的求解中来, 但效果不理想. 基于理想浓度模型的机理分析, 利用拉丁超立方体抽样的理论和方法, 对遗传算法中的交叉操作进行了重新设计, 并在分析图二划分问题特点的基础上, 结合局部搜索策略, 给出了一个解决图二划分问题的新的遗传算法, 称之为拉丁超立方体抽样遗传算法. 通过将该算法与简单遗传算法和佳点集遗传算法进行求解图二划分问题的仿真模拟比较, 可以看出新的算法提高了求解的质量、速度和精度.     

基于Sculptor和Isight的SUV前阻风板结构优化

以某SUV车型为研究对象,通过Isight集成Sculptor和FLUENT建立优化平台,在Sculptor中建立控制体,与SUV几何模型建立映射关系;将阻风板的高度和安装角度参数化,并应用Isight的DOE拉丁超立方设计方法创建实验样本点.在此基础上,用Isight驱动FLUENT进行仿真计算并建立Kriging近似模型,得到目标函数风阻系数与变量高度和角度之间的关系.采用多岛遗传算法对近似模型寻找最优解.优化结果表明,通过对阻风板的优化,其阻力因数下降6.1%.     

Multiobjective optimization for crash safety design of vehicles using stepwise regression model

In automotive industry, structural optimization for crashworthiness criteria is of special importance. Due to the high nonlinearities, however, there exists substantial difficulty to obtain accurate continuum or discrete sensitivities. For this reason, metamodel or surrogate model methods have been extensively employed in vehicle design with industry interest. This paper presents a multiobjective optimization procedure for the vehicle design, where the weight, acceleration characteristics and toe-board intrusion are considered as the design objectives. The response surface method with linear and quadratic basis functions is employed to formulate these objectives, in which optimal Latin hypercube sampling and stepwise regression techniques are implemented. In this study, a nondominated sorting genetic algorithm is employed to search for Pareto solution to a full-scale vehicle design problem that undergoes both the full frontal and 40% offset-frontal crashes. The results demonstrate the capability and potential of this procedure in solving the crashworthiness design of vehicles.     

拉丁超立方抽样的自适应高斯小孔成像蝴蝶优化算法

针对蝴蝶优化算法存在种群多样性差、寻优精度低、收敛速度慢的不足,提出了拉丁超立方抽样的自适应高斯小孔成像蝴蝶优化算法。首先利用拉丁超立方抽样种群初始化策略以提高种群的多样性,从而增强算法的全局搜索能力;然后引入在不同进化时期自动调节搜索范围的自适应最优引导策略,平衡算法的全局和局部搜索能力,从而提升算法的寻优精度;最后采用高斯小孔成像策略,对最优个体进行扰动,使得种群个体向最优个体靠近,以进一步提升算法的寻优精度并加快算法的收敛速度。通过对14个基准测试函数进行仿真实验以及Wilcoxon秩和检验,结果表明改进算法的寻优精度、收敛速度、稳定性和可扩展性等性能均得到了较大提高。