具有离散性的连续性模型映射

为了实现在连续性空间中的离散分段问题,采用解决离散性问题的方法虚拟构造连续性集合,将连续性问题映射为分段的离散问题,根据集合中元素的离散特性实现连续性模型的分段求解.通过数据结构的设计与压缩路径的算法证实,模型的映射能够解决实际问题的分段求解.     

基于磨光反演映射的拓扑优化ICM方法

对结构拓扑优化ICM(independent continuous mapping)方法中的磨光映射和过滤映射加以拓广,利用反演映射极限形式的磨光特性构造其与过滤映射相协调的复合映射.由于该复合映射的叠加离散效应,首先引入幂函数和正弦函数的复合形式过滤函数,用ICM方法建立位移约束下重量最小为目标的连续体结构拓扑优化模型,并采用二次规划精确对偶算法进行求解.再将求得的离散解为主的连续最优解依照动态反演策略,用最佳阈值和理性反演函数求出最严格的0-1离散解,给出了拓扑优化"离散→连续"和"连续→离散"先后相反的二阶段解法.基于MATLAB软件平台开发了相应的拓扑优化计算程序,给出的数值算例对该文提出的方法进行验证,结果表明:该方法计算效率高,最优解灰度单元少,反演后结构重量更小,并且能够计算出更合理的结构拓扑.     

基于度分析的广义胞映射分析方法

非线性动力系统因为其自身特点而难于分析.广义胞映射通过将连续空间与连续映射离散化的手段,成为一种有力的非线性动力系统的分析工具.然而其分析算法存在着结果精确性差、算法步骤复杂且效率低下等缺陷,因此限制了该算法在高精度、大规模分析下的应用.本文在提出胞的度等概念的基础上,通过度分析方法得出不同类胞的度特征,从而以胞的类作为研究对象,提出了高效率、高精度的广义胞映射分析方法.     

带平衡约束的离散网络平衡设计问题的遗传算法

本文研究了带平衡约束的离散网络设计问题及其求解算法.模型中上层是一个离散网络设计的数学规划模型,采用遗传算法来求解.下层是采用变分不等式描述的用户平衡配流问题,利用对角化方法直接求解.通过实例对算法进行验证,结果表明该算法是有效的.     

约束锥扰动多目标规划锥有效解集的闭性和半连续性

本文研究拓扑向量空间中目标映射和约束映射均为连续,约束映射的约束锥为半连续的条件下,受扰动可达目标集的锥有效点集和锥弱有效点集的闭性、半连续性和锥半连续性.在此基础上,得到了约束锥扰动多目标规划问题的锥有效解集和锥弱有效解集的闭性和半连续性.     

Lenth方法对Poisson分布有效性的研究

无重复试验的饱和设计可节省大量的试验时间和费用,带来较大的经济效益,饱和析因设计在实际应用中使用越来越多.但以往统计工作者大部分都是在试验响应变量服从连续分布(如正态分布,t分布,指数分布,Weibull分布等)和Pareto效应稀疏条件下研究的,一直以来还没有人对试验响应变量服从离散分布饱和析因设计进行过研究.本文就...     

四水平计算机试验设计的构造

编码理论在最优设计的构造中已被广泛应用.本文通过修正Gray映射编码变换来构造用于计算机试验的四水平均匀设计,其效率通过离散偏差进行刻画;同时也给出了离散偏差的下界,这个下界可作为获得最优设计的一个基准.     

投影均匀分片拉丁超立方体设计

空间填充设计是有效的计算机试验设计,比如均匀设计、最大最小距离拉丁超立方体设计等.虽然这些设计在整个试验空间中有较好的均匀性,但其低维投影均匀性可能并不理想.对于因子是定量的计算机试验,已有文献构造了诸如最大投影设计、均匀投影设计等相适应的设计;而对于同时含有定性因子和定量因子的计算机试验,尚未有投影均匀设计的相关文献.文章提出了综合投影均匀准则,利用门限接受算法构造了投影均匀的分片拉丁超立方体设计.在新构造设计中,整体设计与每一片设计均具有良好的投影均匀性.模拟结果显示,与随机分片拉丁超立方体设计相比,利用新构造设计进行试验而拟合的高斯过程模型具有更小的均方根预测误差.     

病例队列研究下带约束的Cox模型中参数的一种加权估计方法

许多大型队列研究的主要预算和成本通常来自昂贵的关键协变量的采集与测量.在有限的预算或者时间下,观测大型队列中所有研究对象的昂贵协变量往往是不可行和低效的.因此,研究人员一直致力于寻找和使用能节约成本并能达到预设效率的抽样设计方法.对于生存数据,病例队列设计正是这样一种具有成本效益的有偏抽样机制.进一步,在病例队列研究中,为了利用更多的数据先验信息来提高研究的效率,可以在统计建模过程中对模型参数进行合理的假设和约束.本文研究病例队列设计下带约束的Cox模型中参数的估计方法.我们提出了一种加权约束估计的方法,并建立了所提出估计的渐近理论.发展了一种新的约束MM算法来实现所提出的加权约束估计的数值计算.通过统计模拟研究评估了所提出方法在有限样本量下的表现.分析了一个肾母细胞瘤的实际数据来展示所提出方法的实际应用价值.     

混水平均匀设计的构造

我们用离散偏差来度量部分因子设计的均匀性,本文的目的在于寻找一些构造混水平均匀设计的方法,这些方法比文献中已有的方法更简单且计算成本更低.我们得到了离散偏差的一个下界,如果一个U 型设计的离散偏差值达到这个下界,那么该设计是—个均匀设计.我们建立了均匀设计与组合设计理论中一致可分解设计之间的联系.通过一致可分解设计,我们提出了一些构造均匀设计的新方法,同时也给出了许多均匀设计存在的无穷类.     

An evolutionary programming approach to mixed-variable optimization problems

Many engineering optimization problems frequently encounter discrete variables as well as continuous variables and the presence of nonlinear discrete variables considerably adds to the solution complexity. Very few of the existing methods can find a globally optimal solution when the objective functions are non-convex and non-differentiable. In this paper, we present a mixed-variable evolutionary programming (MVEP) technique for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. The MVEP provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Some examples of mixed-variable optimization problems in the literature are tested, which demonstrate that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.     

Optimal design of a hoist structure frame

In an attempt to find the most cost effective design of a multipurpose hoisting device that can be easily mounted on and removed from a regular farm vehicle, cost optimisation including both material and manufacturing expenditure, is performed on the main frame supporting the device. The optimisation is constrained by local and global buckling and fatigue conditions. Implementation of Snyman’s gradient-based LFOPC optimisation algorithm to the continuous optimisation problem, results in the economic determination of an unambiguous continuous solution, which is then utilised as the starting point for a neighbourhood search within the discrete set of profiles available, to attain the discrete optimum.

This optimum is further investigated for a different steel grade and for the manufacturing and material cost pertaining to different countries. The effect of variations in the formulation of the objective function for optimisation is also investigated. The results indicate that considerable cost benefits can be obtained by optimisation, that costing in different countries do not necessarily result in the same most cost effective design, and that accurate formulation of the objective function, i.e. realistic mathematical modelling, is of utmost importance in obtaining the intended design optimum.     

Design of a motorcycle frame using neuroacceleration strategies in MOEAs

Designing a low-budget lightweight motorcycle frame with superior dynamic and mechanical properties is a complex engineering problem. This complexity is due in part to the presence of multiple design objectives—mass, structural stress and rigidity—, the high computational cost of the finite element (FE) simulations used to evaluate the objectives, and the nature of the design variables in the frame’s geometry (discrete and continuous). Therefore, this paper presents a neuroacceleration strategy for multiobjective evolutionary algorithms (MOEAs) based on the combined use of real (FE simulations) and approximate fitness function evaluations. The proposed approach accelerates convergence to the Pareto optimal front (POF) comprised of nondominated frame designs. The proposed MOEA uses a mixed genotype to encode discrete and continuous design variables, and a set of genetic operators applied according to the type of variable. The results show that the proposed neuro-accelerated MOEAs, NN-NSGA II and NN-MicroGA, improve upon the performance of their original counterparts, NSGA II and MicroGA. Thus, this neuroacceleration strategy is shown to be effective and probably applicable to other FE-based engineering design problems.     

Discrete boundary smoothing using control node parameterisation for aerodynamic shape optimisation

This paper presents an automated aerodynamic optimisation algorithm using a novel method of parameterising the search domain and geometry by employing user–defined control nodes. The displacement of the control nodes is coupled to the shape boundary movement via a ‘discrete boundary smoothing’. This is initiated by a linear deformation followed by a discrete smoothing step to act on the boundary during the mesh movement based on the change in its second derivative. Implementing the discrete boundary smoothing allows both linear and non-linear shape deformation along the same boundary dependent on the preference of the user. The domain mesh movement is coupled to the shape boundary movement via a Delaunay graph mapping. An optimisation algorithm called Modified Cuckoo Search (MCS) is used acting within the prescribed design space defined by the allowed range of control node displacement. In order to obtain the aerodynamic design fitness a finite volume compressible Navier-Stokes solver is utilized. The resulting coupled algorithm is applied to a range of case studies in two dimensional space including the optimisation of a RAE2822 aerofoil and the optimisation of an intake duct under subsonic, transonic and supersonic flow conditions. The discrete mesh–based optimisation approach outlined is shown to be effective in terms of its generalised applicability, intuitiveness and design space definition.     

Global optimization for generalized geometric programming problems with discrete variables

Generalized geometric programming (GGP) problems occur frequently in engineering design and management, but most existing methods for solving GGP actually only consider continuous variables. This article presents a new branch-and-bound algorithm for globally solving GGP problems with discrete variables. For minimizing the problem, an equivalent monotonic optimization problem (P) with discrete variables is presented by exploiting the special structure of GGP. In the algorithm, the lower bounds are computed by solving ordinary linear programming problems that are derived via a linearization technique. In contrast to pure branch-and-bound methods, the algorithm can perform a domain reduction cut per iteration by using the monotonicity of problem (P), which can suppress the rapid growth of branching tree in the branch-and-bound search so that the performance of the algorithm is further improved. Computational results for several sample examples and small randomly generated problems are reported to vindicate our conclusions.     

A two-stage hybrid optimization for honeycomb-type cellular structures under out-of-plane dynamic impact

Honeycomb structures with better balance between lightweight and crashworthiness have aroused growing attentions. However, structural parameters design by traditional optimization algorithm in small design space is not sufficient to significantly enhance the specific energy absorption (SEA) with the lower peak acceleration (a_max). In this paper, a two-stage hybrid optimization for honeycomb-type cellular parameters is proposed to achieve rapid positioning of design space and significantly increase crashworthiness in a larger variable domain under out-of-plane dynamic impact. In stage I, a Taguchi-based grey correlation discrete optimization, combining Taguchi analysis, grey relational analysis, analysis of variance (ANOVA) with grey entropy measurement, is performed to determine the initial optimal value with a higher robustness and the significant influence variables. In stage II, a multi-objective design technique, namely non-nominated sorting genetic algorithm II based on surrogated model, is adopted to maximize the SEA and minimize the a_max in a relatively small design domain. And it is found that the proposed two-stage hybrid method can broaden the optimal design space compared to that of traditional method attributable to its center point positioned by stage I. And the final optimization based on the proposed strategy is superior to the original structure, i.e., the SEA is increased by 47.55% and the a_max is decreased by 80.8%. Therefore, the proposed algorithm can also be used to solve other more complicated engineering problems in a large design space with insightful design data.     

A Learning Algorithm for the Control of Continuous Action Set-Point Regulator Systems

The convergence properties for reinforcement learning approaches, such as temporal differences and Q-learning, have been established under moderate assumptions for discrete state and action spaces. In practice, however, many systems have either continuous action spaces or a large number of discrete elements. This paper presents an approximate dynamic programming approach to reinforcement learning for continuous action set-point regulator problems, which learns near-optimal control policies based on scalar performance measures. The continuous-action space (CAS) algorithm uses derivative-free line search methods to obtain the optimal action in the continuous space. The theoretical convergence properties of the algorithm are presented. Several heuristic stopping criteria are investigated and practical application is illustrated by two example problems—the inverted pendulum balancing problem and the power system stabilization problem.     

蚁群遗传混合算法

将蚁群遗传混合算法分别求解离散空间的和连续空间优化问题.求解旅行商问题的混合算法是以遗传算法为整个算法的框架,利用了蚁群算法中的信息素特性的进行交叉操作;根据旅行商问题的特点,给出了4种变异策略;针对遗传算法存在的过早收敛问题,加入2-0pt方法对问题求解进行了局部优化.与模拟退火算法、标准遗传算法和标准蚁群算法进行比较,4种混合算法效果都比较好,策略D的混合算法效果最好.求解连续空间优化问题是以蚁群算法为整个算法的框架,加入遗传算法的交叉操作和变异操作,用测试函数验证了混合蚁群算法的正确性.     

宏观经济分布参数系统的临界值问题

本文把经济系统作为一类生灭过程来考虑 .应用人口控制论和森林系统的成功经验 ,研究经济系统的临界值问题 .首先 ,基于实际的经济分析、预测模型 ,在宏观层次上建立经济系统的控制模型 .连续模型便于理论研究 ,离散模型便于计算机仿真 .然后在这个控制模型的基础上 ,寻找使国民经济持续发展所需要的最小资产积累率表达形式 .本文得到的理论值将帮助我们更深刻地理解经济系统     

Robin型离散Schwarz波形松弛算法的收敛性分析

Schwarz波形松弛(Schwarz waveform relaxation,SWR)是一种新型区域分解算法,是当今并行计算研究领域的焦点之一,但针对该算法的收敛性分析基本上都停留在时空连续层面.从实际计算角度看,分析离散SWR算法的收敛性更重要.本文考虑SWR研究领域中非常流行的Robin型人工边界条件,分析时空离散参数t和x、模型参数等因素对算法收敛速度的影响.Robin型人工边界条件中含有一个自由参数p,可以用来优化算法的收敛速度,但最优参数的选取却需要求解一个非常复杂的极小-极大问题.本文对该极小-极大问题进行深入分析,给出最优参数的计算方法.本文给出的数值实验结果表明所获最优参数具有以下优点:(1)相比连续情形下所获最优参数,利用离散情形下获得的参数可以进一步提高Robin型SWR算法在实际计算中的收敛速度,当固定t或x而令另一个趋于零时,利用离散情形下所获参数可以使算法的收敛速度具有鲁棒性(即收敛速度不随离散参数的减小而持续变慢).(2)相比连续情形下所获收敛速度估计,离散情形下获得的收敛速度估计可以更加准确地预测算法的实际收敛速度.