水火电力系统短期有功经济调度的一种有效快速算法

本文将约束松驰法和经典解析法相结合求解水火电力系统有功经济调度问题。系统被分为水火电两子系统。考虑网损，两子系统都采用直接的解析解法，用约束松驰法处理火电机组出力极限的约束，使本文方法既具有解析法的快理性，又具有处理狭窄约束域和大规模问题的有效性。     

基于混合遗传算法的热轧生产调度优化方法

热轧生产调度是一个复杂的约束组合优化问题，其生产约束包括连续轧制板坯的宽度、厚度和硬度跳变要求，轧制单元的最大长度，产品库存及交货期等。基于多旅行商模型，建立了热轧生产批量调度问题的优化模型，并提出一种混合遗传算法（遗传算法、局部搜索）求解该问题。通过应用串行边重组和并行边重组的遗传交叉算子，算法在优化过程中可以很好地处理调度约束。针对工业数据的仿真结果证明该调度模型和混合遗传算法的并行求解策略可以有效地解决热轧生产批量调度问题。     

奥运调度问题模型转换及优化研究

本文研究了奥运会调度问题的模型转换和优化. (1)时间区间约束是奥运会调度问题的关键约束, 本文建立了一种时间区间模型语言以描述这个调度问题. (2)奥运会调度问题是一个约束满足问题, 考虑其本质复杂性, 本文通过柔化决赛时间约束将约束满足问题转化为约束优化问题. (3)约束优化模型中, 项由场地约束关联起来, 如果去掉场地约束, 各项则是相互独立的. 因而本文通过松弛场地约束将约束优化问题分解为若干子问题. 全局优化解通过调整拉格朗日乘子获得. (4)为了调整拉格朗日乘子, 本文研究了变直径次梯度投影算法, 此算法不依赖于任何先验知识收敛, 本文给出了收敛效率. 仿真结果说明了算法的收敛性, 显示出变直径次梯度投影算法与简化算法在性能上的差别, 并且表明原约束满足问题的相变现象可以通过变直径次梯度投影算法获得正的对偶值的概率和首次获得正的对偶值的时间来识别.     

一种基于神经网络的生产调度方法

提出解决具有开、完工期限制的约束Job-shop生产调度问题的一种神经网络方法. 该方法通过约束神经网络,描述各种加工约束条件,并对不满足约束的开工时间进行相应调 节,得到可行调度方案;然后由梯度搜索算法优化可行调度方案,直至得到最终优化可行调度 解.理论分析、仿真实验表明了方法的有效性.     

基于混合粒子群算法的试验选址问题研究

大型海上试验涉及分散在全国各地的人员、平台、测量设备和产品等资源。试验海区分布在漫长的海岸线上，将这些资源运输到合适的海区属于运输问题。试验海区的选择会影响资源的取舍，资源的取舍与试验流程优化这一车间调度问题密切相关，而试验流程优化反过来又会影响资源的取舍和海区的选择。因此，试验海区的选择是运输问题和车
车间调度问题的耦合。本文建立了该问题的数学模型，并分别用粒子群算法和排队论处理流程优化中的时间约束和资源约束，再用启发式算法对运输问题进行优化。最后，以某产品的试验为例对算法进行了验证，结果表明了该方法的有效性。     

约束优化进化算法综述

约束优化进化算法主要研究如何利用进化计算方法求解约束优化问题,是进化计算领城的一个重要研究课题.约束优化问题求解存在约束区域离散、等式约束、非线性约束等挑战,其问题的本质是如何处理可行解与不可行解的关系才能使得算法更高效.本文首先介绍了约束优化问题的定义,然后系统地分析了目前存在的约束优化方法,同时基于约束处理机制将这些方法分为罚函数法、可行性法则、随机排序法、约束处理法、多目标优化法、混合法六类,并从约束处理方法的方面对约束优化进化算法的最新研究进展进行综述.最后,指出约束优化进化算法需进一步研究的方向与关键问题.     

遗传算法在车辆调度优化问题中的研究

车辆调度优化问题是一个有约束的组合优化问题,属于NP难题（Nondeterministic Polynomial Problem）。随着问题输入规模的扩大,求解时间呈几何级数上升,传统的优化算法本身存在着过早收敛于局部值的问题。针对这一问题在染色体编码、算子的自适应机制和约束的处理等方面对标准遗传算法进行了改进。测试结果表明,该算法提高了优化算法的质量和搜索效率,具有良好的效果。     

贵州电网节能发电调度实践及思考

节能发电调度的实施对于调度部门提出了更高的要求。以贵州电网的节能发电调度实际经验为基础，对贵州电网在节能发电调度方面采取的水火电协调优化调度、节能发电调度煤耗在线监测、烟气脱硫远程实时监测与脱硫电量考核、计及安全约束和网损修正的节能发电调度四项措施进行了总结介绍，指出当前工作中存在实际问题并提出相应的解决思路，以保证节...     

基于差分算法的反渗透海水淡化优化调度

以系统运行费用为目标的反渗透海水淡化优化调度是一类带有约束的非线性优化问题。针对这一问题,提出一种改进的差分进化算法。该算法对基本差分进化算法中的变异因子和交叉因子进行改进;定义约束违反度函数,将约束优化问题转化为无约束的优化问题。以24小时为一个周期,通过改进的差分进化算法对系统模型进行优化调度。仿真结果表明,改进的算法可以对机组进行优化操作,有效的降低了系统的生产成本。     

峰值能耗下随机订单的模型预测控制

针对带峰值能耗约束的随机订单并行机调度问题，即所有机器总功耗不得超过给定阈值，提出了基于模型预测控制的算法来最小化订单的期望生产周期。研究难点在于通过优化能源分配，在峰值能耗约束的前提下，提高设备的工作效率，并采用合理的滚动调度决策的方式处理由客户订单到达时间和需求量随机变化所引起的不确定性。研究对优化策略、生产周期、产品差异、机器速度等因素的影响，进行了全面的理论分析，并通过一系列数值实验，验证了所设计算法的有效性，挖掘了其中的有益管理启示来更好地指导实践。     

城市道路建设时序决策的鲁棒优化

为提高城市道路建设时序决策的鲁棒性,提出了城市道路建设时序决策优化的双 层规划模型。模型假定出行需求在一定范围内扰动,上层规划是在有限资金的约束下寻求各建设阶段的系统总出行时间与系统总出行时间对出行需求的灵敏度之间的综合最小值,下层规划为各建设阶段的随机用户均衡配流。文中推导出了系统总出行时间对出行需求灵敏度的计算式,并给出了模型的求解算法。最后以一个测试路网为例,对基于系统总出行时间、基于灵敏度、基于系统总出行时间与灵敏度综合出行时间的决策优化模型进行了计算分析,结果显示3种决策优化模型均可寻求到各自目标最优的城市道路建设时序,但在需求不确定的情景下基于灵敏度、基于系统总出行时间与灵敏度综合出行时间的决策优化结果更具鲁棒性。     

On the use of ROC analysis for the optimization of abstaining classifiers

Classifiers that refrain from classification in certain cases can significantly reduce the misclassification cost. However, the parameters for such abstaining classifiers are often set in a rather ad-hoc manner. We propose a method to optimally build a specific type of abstaining binary classifiers using ROC analysis. These classifiers are built based on optimization criteria in the following three models: cost-based, bounded-abstention and bounded-improvement. We show that selecting the optimal classifier in the first model is similar to known iso-performance lines and uses only the slopes of ROC curves, whereas selecting the optimal classifier in the remaining two models is not straightforward. We investigate the properties of the convex-down ROCCH (ROC Convex Hull) and present a simple and efficient algorithm for finding the optimal classifier in these models, namely, the bounded-abstention and bounded-improvement models. We demonstrate the application of these models to effectively reduce misclassification cost in real-life classification systems. The method has been validated with an ROC building algorithm and cross-validation on 15 UCI KDD datasets. An early version of this paper was published at ICML2005. Action Editor: Johannes Fürnkranz.     

基于扩展知识结构图的智能教学规划

智能教学规划是智能教学系统的核心组成部分,领域知识模型、教学方法模型以及学习者模型是智能教学系统的三个要素。提出了扩展知识结构图(ExtendedKnowledgeStructureGraph,EKSG)的概念,通过扩展知识结构图将领域知识模型、教学方法模型、学习者模型有机地结合起来。在此基础上,给出了根据扩展知识结构图制定学习者关于学习目标的最优教学规划算法,规划算法由两部分组成:判定算法JUDGE和最优规划生成算法TPLAN,其中JUDGE判断规划问题是否有解,在有解的情况下求出最优解图,TPLAN根据最优解图计算最优规划。证明了算法的正确性,分析了算法的效率。     

汽车主动悬架高精度数学模型的计算机仿真

由于车辆主动悬架在实际应用中存在一些问题，因此需要一个高精度的数学模型来真实地描述实际的系统。该文分别对两个1／4车辆主动悬架系统的数学模型进行描述，并分别对整个悬架系统采用最优控制策略进行仿真，通过比较可以得出建立高精度数学模型对主动悬架系统研究的重要性。     

A 3D multi-grid algorithm for the Chan–Vese model of variational image segmentation

Variational segmentation models provide effective tools for image processing applications. Although existing models are continually refined to increase their capabilities, solution of such models is often a slow process, since fast methods are not immediately applicable to nonlinear problems. This paper presents an efficient multi-grid algorithm for solving the Chan–Vese model in three dimensions, generalizing our previous work on the topic in two dimensions, but this direct generalized method is low performance or unfeasible. So here, we first present two general smoothers for a nonlinear multi-grid method and then give our three new adaptive smoothers which can choose optimal a parameter of the smoothers automatically, also we analyse them using a local Fourier analysis and our theorem to inform how to obtain an optimal parameter and the best smoother selection. Finally, various advantages of our recommended algorithm are illustrated, using both synthetic and real images.     

改进型Dijkstra算法在最优路径选择中的应用研究

为了有效提高市民出行效率。减小城市资源消耗,优化城市环境,在改进型Dijkstra算法的基础上设计并实现城市公交线路查询系统。Dijkstra算法是图论中求解最短路径问题的一种优秀算法。在Dijkstra算法的基础上．进行对存储结构进行优化、对结点进行分类、对权值进行排序这三大改进．并将改进型Dijkstra算法作为最优公交线路查询算法的核心。以时间最小、费用最小、综合最优这三种需求作为评价标准分别建立数学模型,通过数学模型将搜索两点间的最优路线问题转化成求解两点间的最短路径问题．最后以南昌市的公交路网为例对算法和数学模型进行仿真测试。仿真结果验证算法和数据模型的有效性。     

A general and optimal approach for three inventory models with a linear trend in demand

This study provides a general and simple algorithm to obtain an optimal solution for three inventory models with a replenishment batching policy, production batching policy, and an integrated replenishment/production batching policy in a manufacturing system, under a finite time horizon and linear trend in demand. This study determines the replenishment or production schedule with one general equation for these three problems and provides fully theoretical proofs for relaxing some of the conjectures in previous studies. A general and explicit procedure to derive the optimal solution for these three inventory models is presented, while considering both linearly increasing and decreasing demands. In addition, demonstrations of applicability are performed.     

集成灰色支持向量机预测模型研究与应用

对灰色预测GM(1,1)模型进行了分析,提出了集成灰色支持向量机的预测模型。分别对影响灰色预测GM(1,1)模型精度的背景值的计算、初值的选取以及数据序列的光滑度进行改进,提出了背景GM模型、初值GM模型、光滑度GM模型,并结合支持向量机的特点,将一维原始数据序列通过三个灰色模型得到的三组值作为支持向量机的输入,原始序列作为支持向量机的输出,训练得到最佳支持向量回归机模型。仿真结果表明了该模型的有效性。     

Railway freight transportation planning with mixed uncertainty of randomness and fuzziness

The railway freight transportation planning problem under the mixed uncertain environment of fuzziness and randomness is investigated in this paper, in which the optimal paths, the amount of commodities passing through each path and the frequency of services need to be determined. Based on the chance measure and critical values of the random fuzzy variable, three chance-constrained programming models are constructed for the problem with respect to different criteria. Some equivalents of objectives and constraints are also discussed in order to investigate mathematical properties of the models. To solve the models, a potential path searching algorithm, simulation algorithms and a genetic algorithm are integrated as a hybrid algorithm to solve an optimal solution. Finally, some numerical examples are performed to show the applications of the models and the algorithm.     

An expert control system using neural networks for the electrolytic process in zinc hydrometallurgy

The final step in zinc hydrometallurgy is the electrolytic process, which involves passing an electrical current through insoluble electrodes to cause the decomposition of an aqueous zinc sulfate electrolyte and the deposition of metallic zinc at the cathode. For the electrolytic process studied, the most important process parameters for control are the concentrations of zinc and sulfuric acid in the electrolyte. This paper describes an expert control system for determining and tracking the optimal concentrations of zinc and sulfuric acid, which uses neural networks, rule models and a single-loop control scheme. The system is now being used to control the electrolytic process in a hydrometallurgical zinc plant. In this paper, the system architecture, which features an expert controller and three single-loop controllers, is first explained. Next, neural networks and rule models are constructed based on the chemical reactions involved, empirical knowledge and statistical data on the process. Then, the expert controller for determining the optimal concentrations is designed using the neural networks and rule models. The three single-loop controllers use the PI algorithm to track the optimal concentrations. Finally, the results of actual runs using the system are presented. They show that the system provides not only high-purity metallic zinc, but also significant economic benefits.