不确定信息下分式半无限优化问题的近似最优性刻画

该文研究了一类带不确定参数的多目标分式半无限优化问题。首先借助鲁棒优化方法,引入该不确定多目标分式优化问题的鲁棒对应优化模型,并借助Dinkelbach方法,将该鲁棒对应优化模型转化为一般的多目标优化问题。随后借助一种标量化方法,建立了该优化问题的标量化问题,并刻画了它们的解之间的关系。最后借助一类鲁棒型次微分约束规格,建立了该不确定多目标分式优化问题拟近似有效解的鲁棒最优性条件。     

多响应优化方法的比较和应用研究

本文对目前几种多响应优化的主要方法进行了分析和总结,考察了马氏距离法、多元损失函数法、满意度函数法、概率法以及双响应曲面等几种主要方法在多响应优化中的应用,并对实例的多响应的预测值和方差进行了优化,比较了这几种方法的优缺点。研究结果表明,满意度函数法和概率法在处理多响应优化过程中能够取得较好的优化效果。     

土地资源的可拓性及优化配置系统结构模型

本文应用可拓学的理论和方法，对土地资源的特征及优化配置原理进行分析，设计了优化配置系统结构模型，为土地资源的优化配置和合理利用提供了一种新的科学方法。     

基于替代航路的进场航班排序优化方法

针对机场终端区航班延误日益严重的现状,为减轻空中交通管制员工作负荷,缓解航班延误,研究了进场航班排序优化问题.通过引入替代进近航路和时间偏差成本的概念,建立了进场航班排序模型;基于调度窗口和冻结范围概念,提出了动态优化方法;结合进场航班排序模型与动态优化方法,以国内某机场的运行数据进行了仿真验证.仿真结果表明:与实际运行相比,基于替代航路的进场航班排序优化方法优化效果显著,减少了约25%的进场航班延误,提高了机场终端区航空器的运行效率.优化方法可以即时获得最优方案,辅助空中交通管制员做出决策.     

解线性约束优化问题的新锥模型信赖域法

本文提出了一个解线性等式约束优化问题的新锥模型信赖域方法.论文采用零空间技术消除了新锥模型子问题中的线性等式约束,用折线法求解转换后的子问题,并给出了解线性等式约束优化问题的信赖域方法.论文提出并证明了该方法的全局收敛性,并给出了该方法解线性等式约束优化问题的数值实验.理论和数值实验结果表明新锥模型信赖域方法是有效的,这给出了用新锥模型进一步研究非线性优化的基础.     

解线性约束优化问题的新锥模型信赖域3法

本文提出了一个解线性等式约束优化问题的新锥模型信赖域方法．论文采用零空间技术消除了新锥模型子问题中的线性等式约束，用折线法求解转换后的子问题，并给出了解线性等式约束优化问题的信赖域方法．论文提出并证明了该方法的全局收敛性，并给出了该方法解线性等式约束优化问题的数值实验．理论和数值实验结果表明新锥模型信赖域方法是有效的，这给出了用新锥模型进一步研究非线性优化的基础．     

基于非线性规划的社会系统协调发展优化模型及其应用

社会系统协调发展优化问题是优化理论运用较少的领域.基于非线性规划方法,依据社会系统协调发展内涵,对社会系统协调发展目标函数做了具体改进,构建了社会系统协调发展优化模型,并根据协调发展类型分为超前型优化模型与滞后型优化模型.这两种优化模型的区别主要是约束条件的不同.依据优化模型的优化解,可以为某地区社会系统的协调发展提供清晰的调节与控制路径.最后利用上述方法对徐州地区物流基础系统、经济基础系统的协调发展进行了具体优化与调控.     

油田注水系统拓扑布局优化的混合遗传算法

以投资最小为目标函数,建立了注水系统拓扑布局优化数学模型.根据模型特点,将优化问题分为两层,分别采用遗传算法和非线性优化方法进行求解.并对遗传算法的操作过程进行了改进,调整了适应函数,改进了交叉和变异操作,结合了模拟退火算法,在操作过程中使约束条件得到满足,减少了不可行解的产生,使遗传算法的优化性能得到了提高.优化算例说明了该方法的有效性.     

框架结构多目标优化方法

提出了一种框架结构多目标优化方法。基于力法给出了框架结构的等效刚度、最大弯矩随结构材料、几何等设计参数的变化,利用动能等效给出了框架结构的等效质量,进而得到框架结构一阶固有频率随结构材料、几何等参数的变化,用有限元方法验证了所得频率、弯矩公式的正确性。以固有频率、最大弯矩为优化目标函数构造框架结构的多目标优化模型,通过单目标优化得到频率、弯矩最优值,以单目标优化值为基础构造全局目标优化函数,将多目标优化转化为单目标优化。     

基于改进鲸鱼优化算法的物流路径优化

针对物流配送车辆路径难以优化以至于影响物流配送系统效率和效益的问题,提出一种基于鲸鱼优化算法的车辆路径优化方法.首先,建立物流配送系统数学模型.其次,针对传统鲸鱼优化算法易陷入局部最优的问题,采用随机惯性权重和非均匀变异策略对算法进行改进,并用过测试函数对比试验验证了所提方法的有效性.最后通过改进的鲸鱼优化算法进行物流路径优化实验,实验结果验证了所提出的改进鲸鱼优化算法能够高效的优化物流配送车辆路配送路径,降低了物流配送成本.     

Boosting for Correlated Binary Classification

Boosting is a successful method for dealing with problems of high-dimensional classification of independent data. However, existing variants do not address the correlations in the context of longitudinal or cluster study-designs with measurements collected across two or more time points or in clusters. This article presents two new variants of boosting with a focus on high-dimensional classification problems with matched-pair binary responses or, more generally, any correlated binary responses. The first method is based on the generic functional gradient descent algorithm and the second method is based on a direct likelihood optimization approach. The performance and the computational requirements of the algorithms were evaluated using simulations. Whereas the performance of the two methods is similar, the computational efficiency of the generic-functional-gradient-descent-based algorithm far exceeds that of the direct-likelihood-optimization-based algorithm. The former method is illustrated using data on gene expression changes in de novo and relapsed childhood acute lymphoblastic leukemia. Computer code implementing the algorithms and the relevant dataset are available online as supplemental materials.     

Optimization of arches using genetic algorithm

An optimization procedure is presented for the minimum weight and strain energy optimization for arch structures subjected to constraints on stress, displacement and weight responses. Both thickness and shape variables defining the natural line of the arch are considered. The computer program which is developed in this study can be used to optimize thick, thin and variable thickness curved beams/arches. An automated optimization procedure is adopted which integrates finite element analysis, parametric cubic spline geometry definition, automatic mesh generation and genetic algorithm methods. Several examples are presented to illustrate optimal arch structures with smooth shapes and thickness variations. The changes in the relative contributions of the bending, membrane and shear strain energies are monitored during the whole process of optimization.     

A SELF-ADAPTIVE TECHNIQUE FOR A KIND OF NONLINEAR CONJUGATE GRADIENT METHODS

Conjugate gradient methods are a class of important methods for unconstrained optimization, especially when the dimension is large. In 2001, Dai and Liao have proposed a new conjugate condition, based on it two nonlinear conjugate gradient methods are constructed. With trust region idea, this paper gives a self-adaptive technique for the two methods. The numerical results show that this technique works well for the given nonlinear optimization test problems.     

Superimposed optimization methods for the mixed equilibrium problem and variational inclusion

The purpose of this paper is to construct two superimposed optimization methods for solving the mixed equilibrium problem and variational inclusion. We show that the proposed superimposed methods converge strongly to a solution of some optimization problem. Note that our methods do not involve any projection.     

Nonconvex Stochastic Optimization for Model Reduction

In this paper a global stochastic optimization algorithm, which is almost surely (a.s.) convergent, is applied to the model reduction problem. The proposed method is compared with the balanced truncation and Hankel norm approximation methods by examples in step responses and in approximation errors as well. Simulation shows that the proposed algorithm provides better results.     

两类解非线性约束问题的超记忆可行方向法

本文将无约束超记忆梯度法推广到非线性不等式约束优化问题上来,给出了两类形式很一般的超记忆可行方向法,并在非退化及连续可微等较弱的假设下证明了其全局收敛性.适当选取算法中的参量及记忆方向,不仅可得到一些已知的方法及新方法,而且还可能加快算法的收敛速度.     

无线通信系统设计中的两个优化问题和相关优化方法

无线通信系统设计中的许多问题可建模为优化问题.一方面,这些优化问题常常具有高度的非线性性,一般情况下难于求解;另一方面,它们又有自身的特殊结构,例如隐含的凸性、可分性等.利用优化的方法结合问题的特殊结构求解和处理无线通信系统设计问题是近年来学术界研究的热点.本文重点讨论无线通信系统设计中的两个优化问题和相关优化方法,包括多用户干扰信道最大最小准则下的联合传输/接收波束成形设计和多输入多输出(Multi-Input Multi-Output,MIMO)检测问题,主要介绍现代优化技术结合问题的特殊结构在求解和处理上述两个问题的最新进展.     

Application of robust optimization to automated test assembly

In automated test assembly (ATA), 0-1 linear programming (0-1 LP) methods are applied to select questions (items) from an item bank to assemble an optimal test. The objective in this 0-1 LP optimization problem is to assemble a test that measures, in as precise a way as possible, the ability of candidates. Item response theory (IRT) is commonly applied to model the relationship between the responses of candidates and their ability level. Parameters that describe the characteristics of each item, such as difficulty level and the extent to which an item differentiates between more and less able test takers (discrimination) are estimated in the application of the IRT model. Unfortunately, since all parameters in IRT models have to be estimated, they do have a level of uncertainty to them. Some of the other parameters in the test assembly model, such as average response times, have been estimated with uncertainty as well. General 0-1 LP methods do not take this uncertainty into account, and overestimate the predicted level of measurement precision. In this paper, alternative robust optimization methods are applied. It is demonstrated how the Bertsimas and Sim method can be applied to take this uncertainty into account in ATA. The impact of applying this method is illustrated in two numerical examples. Implications are discussed, and some directions for future research are presented.     

On the final steps of Newton and higher order methods

The Newton method is one of the most used methods for solving nonlinear system of equations when the Jacobian matrix is nonsingular. The method converges to a solution with Q-order two for initial points sufficiently close to the solution. The method of Halley and the method of Chebyshev are among methods that have local and cubic rate of convergence. Combining these methods with a backtracking and curvilinear strategy for unconstrained optimization problems these methods have been shown to be globally convergent. The backtracking forces a strict decrease of the function of the unconstrained optimization problem. It is shown that no damping of the step in the backtracking routine is needed close to a strict local minimizer and the global method behaves as a local method. The local behavior for the unconstrained optimization problem is investigated by considering problems with two unknowns and it is shown that there are no significant differences in the region where the global method turn into a local method for second and third order methods. Further, the final steps to reach a predefined tolerance are investigated. It is shown that the region where the higher order methods terminate in one or two iteration is significantly larger than the corresponding region for Newton’s method.     

Robustness of mechanical systems against uncertainties

In this paper, one can propose a method which takes into account the propagation of uncertainties in the finite element models in a multi-objective optimization procedure. This method is based on the coupling of stochastic response surface method (SRSM) and a genetic algorithm provided with a new robustness criterion. The SRSM is based on the use of stochastic finite element method (SFEM) via the use of the polynomial chaos expansion (PC). Thus, one can avoid the use of Monte Carlo simulation (MCS) whose costs become prohibitive in the optimization problems, especially when the finite element models are large and have a considerable number of design parameters.The objective of this study is on one hand to quantify efficiently the effects of these uncertainties on the responses variability or the cost functions which one wishes to optimize and on the other hand, to calculate solutions which are both optimal and robust with respect to the uncertainties of design parameters.In order to study the propagation of input uncertainties on the mechanical structure responses and the robust multi-objective optimization with respect to these uncertainty, two numerical examples were simulated. The results which relate to the quantification of the uncertainty effects on the responses variability were compared with those obtained by the reference method (REF) using MCS and with those of the deterministic response surfaces methodology (RSM).In the same way, the robust multi-objective optimization results resulting from the SRSM method were compared with those obtained by the direct optimization considered as reference (REF) and with RSM methodology.The SRSM method application to the response variability study and the robust multi-objective optimization gave convincing results.