交通信号控制的二层规划模型与算法研究

本文研究了交叉口信号控制的二层规划模型的求解算法．上层模型采用了一种直接处理约束的改进的粒子群算法，下层则采用仿射尺度内点算法，得到了一种信号控制二层规划模型．并对模拟路网进行了数值实验，表明算法是有效的和可行的．     

一种基于进化算法的非线性两层规划的快速全局优化方法

由于非线性两层规划具有非凸性、NP-难等计算困难，高效的算法并不多见。本文设计了一种新的进化算法，基于此进化算法提出了求解带有一重或多重下层的非线性两层规划的高效算法。该算法充分利用两层规划的结构特点。最后，给出了六个不同类型的算例，数值结果表明，本算法是快速和有效的。     

求解二层规划问题的遗传算法

本文求解二层规划问题的遗传算法，给出了算法基本框架并对算法实现进行了研究．算法适用于各类线性和非线性二层规划问题．数值计算结果显示，该方法是可行和有效的．     

基于粒子群算法的非线性二层规划问题的求解算法

粒子群算法（Particle Swarm Optimization，PSO）是一种新兴的优化技术，其思想来源于人工生命和演化计算理论。PSO通过粒子追随自己找到的最好解和整个群的最好解来完成优化。该算法简单易实现，可调参数少，已得到了广泛研究和应用。本文根据该算法能够有效的求出非凸数学规划全局最优解的特点，对非线性二层规划的上下层问题求解，并根据二层规划的特点，给出了求解非线性二层规划问题全局最优解的有效算法。数值计算结果表明该算法有效。     

多层神经网络的一个快速算法

本文对文[4]提出的前馈式多层神经网络的单参数动态搜索（SPDS）算法进行了深入的分析，给出了实现快速一维搜索的两个方案，从而实现了多层神经网络更为快速的学习训练．     

三层前向人工神经网络全局最优逼近

提出了求解不等式约束非线性优化问题的群体复合形进化算法 ,提出的算法能充分利用目标函数值的信息、优化搜索过程具有较强的方向性和目标性 ,收敛速度较快 ,且是全局优化算法 ;将群体复合形进化算法应用于三层前向人工神经网络逼近 ,提出了三层前向人工神经网络全局最优逼近算法 ;将三层前向人工神经网络全局最优逼近算法应用于实例 ,表明了提出的全局最优逼近算法的有效性 .     

多产品单层能力受限批量问题的前向启发式算法的可行性过程

本文讨论多产品、单层、能力受限批量问题的前向启发式算法，指出了这类算法中现有算法的一些不足之处。为了克服这些不足，我们给出了可行解存在的充分必要条件，并在此基础上提出一个可行化算法。     

能力受限的批量问题的数学模型与算法新进展

古典库存问题和能力无限的单层批量问题的研究已经有几十年的历史了，但由于实际生产中产品结构往往比较复杂、生产能力总有限的，因此能力受限的批量问题（尤其是多层批量问题）成为了近年来运筹学、管理科学和工业工程等领域的研究热点之一。本文在综合大量国内外有关文献的基础上，对一般批量问题的数学模型作了比较系统，全面的介绍，重点讨论能力受限的单层批量问题和多层批量问题的一些新算法，并指出一些值得深入研究和努力实     

多层线性规划问题的整体优化算法

本文研究了求解多层线性规划问题的整体优化算法,利用流动等值面技术,证明了算法的有限终止性,并给出实际例子验证了算法的有效性.     

一个关于非对称距离的旅行商问题的迭代算法

本对非对称距离的旅行商问题，给出了一个迭代算法，并分析了此迭代算法的复杂度为M^nO(N^4)，其中，N是问题中旅行商所要经过的城镇数，M是两城镇间的最大距离。最后用实例对此算法进行了验算和说明。     

二元有理插值的迭加算法

在已有基础上给出一种新的算法,即迭加插值算法,并给出相应的插值有理函数的具体表达式,以及与已有算法比较,该算法具有较大的灵活性,更便于实际应用.     

矩形网格上二元有理插值的表现公式

运用迭加算法给出矩形网格上二元有理插值函数的表现公式,特别给出了在对角情形下使用迭加算法得到的插值公式.这种方法具有较大的灵活性,且易于编写程序,便于实际应用.     

基于时间切片叠加的警车巡逻路线规划

在城市中,有效的安排警车巡逻对于降低犯罪率,预防潜在犯罪案件发生和及时处理案件具有十分重要的意义.通过一些必要简化首先确定了巡逻方案应当满足的条件以及方案的评价体系.通过随机贪心算法求解足够多的可行静态解,并引入时间片叠加的思想在静态解的基础上应用深度优先搜索算法,将求解动态巡逻问题转化为在有向连通图中寻找使目标函数达到最大的约束环路的问题,最终求得动态巡逻方案.最后,通过实例对模型进行了验证和评价.     

KMP算法的一种新的简化算法

通过对字符串模式匹配算法BF与KMP的分析,提出了一种简化KMP算法的方法,构造了一种新的计算next函数的方法,简化后的算法比KMP更清晰直观.经过复杂性分析和上机实验,得出当模式串的长度不大时,简化算法是一种高效的模式匹配算法.     

A variable-metric method using a nonquadratic model

In this paper, a new variable-metric method based on a rational, rather than a quadratic, model is proposed. A switching algorithm is also introduced which selects either the standard quadratic model or the new rational model, depending on which has the smallest condition number. Several functions are used to test the new method, and it is concluded that it is as efficient as the standard model in general and is superior for problems of high dimensionality. Considerable improvement is also obtained for high-dimensional problems when the switching algorithm is used.     

Dynamic Multi-level Overlay Graphs for Shortest Paths

Multi-level overlay graphs represent a speed-up technique for shortest paths computation which is based on a hierarchical decomposition of a weighted directed graph G. They have been shown to be experimentally efficient, especially when applied to timetable information. However, no theoretical result on the cost of constructing, maintaining and querying multi-level overlay graphs in a dynamic environment is known. In this paper, we show theoretical properties of multi-level overlay graphs that lead us to the definition of a new data structure for the computation and the maintenance of an overlay graph of G while weight decrease or weight increase operations are performed on G. Our solution is theoretically faster than the recomputation from scratch and allows queries that can be performed more efficiently than running Dijkstra’s shortest paths algorithm on G. This work was partially supported by the Future and Emerging Technologies Unit of EC (IST priority – 6th FP), under contract no. FP6-021235-2 (project ARRIVAL).     

A new constrained fixed-point algorithm for ordering independent components

Independent component analysis (ICA) aims to recover a set of unknown mutually independent components (ICs) from their observed mixtures without knowledge of the mixing coefficients. In the classical ICA model there exists ICs’ indeterminacy on permutation and dilation. Constrained ICA is one of methods for solving this problem through introducing constraints into the classical ICA model. In this paper we first present a new constrained ICA model which composed of three parts: a maximum likelihood criterion as an objective function, statistical measures as inequality constraints and the normalization of demixing matrix as equality constraints. Next, we incorporate the new fixed-point (newFP) algorithm into this constrained ICA model to construct a new constrained fixed-point algorithm. Computation simulations on synthesized signals and speech signals demonstrate that this combination both can eliminate ICs’ indeterminacy to a certain extent, and can provide better performance. Moreover, comparison results with the existing algorithm verify the efficiency of our new algorithm furthermore, and show that it is more simple to implement than the existing algorithm due to its advantage of not using the learning rate. Finally, this new algorithm is also applied for the real-world fetal ECG data, experiment results further indicate the efficiency of the new constrained fixed-point algorithm.     

The Adjoint Newton Algorithm for Large-Scale Unconstrained Optimization in Meteorology Applications

A new algorithm is presented for carrying out large-scale unconstrained optimization required in variational data assimilation using the Newton method. The algorithm is referred to as the adjoint Newton algorithm. The adjoint Newton algorithm is based on the first- and second-order adjoint techniques allowing us to obtain the Newton line search direction by integrating a tangent linear equations model backwards in time (starting from a final condition with negative time steps). The error present in approximating the Hessian (the matrix of second-order derivatives) of the cost function with respect to the control variables in the quasi-Newton type algorithm is thus completely eliminated, while the storage problem related to the Hessian no longer exists since the explicit Hessian is not required in this algorithm. The adjoint Newton algorithm is applied to three one-dimensional models and to a two-dimensional limited-area shallow water equations model with both model generated and First Global Geophysical Experiment data. We compare the performance of the adjoint Newton algorithm with that of truncated Newton, adjoint truncated Newton, and LBFGS methods. Our numerical tests indicate that the adjoint Newton algorithm is very efficient and could find the minima within three or four iterations for problems tested here. In the case of the two-dimensional shallow water equations model, the adjoint Newton algorithm improves upon the efficiencies of the truncated Newton and LBFGS methods by a factor of at least 14 in terms of the CPU time required to satisfy the same convergence criterion.The Newton, truncated Newton and LBFGS methods are general purpose unconstrained minimization methods. The adjoint Newton algorithm is only useful for optimal control problems where the model equations serve as strong constraints and their corresponding tangent linear model may be integrated backwards in time. When the backwards integration of the tangent linear model is ill-posed in the sense of Hadamard, the adjoint Newton algorithm may not work. Thus, the adjoint Newton algorithm must be used with some caution. A possible solution to avoid the current weakness of the adjoint Newton algorithm is proposed.     

实时系统中单处理器调度算法的优化设计研究

研究实时系统的建模与调度问题是运筹与控制领域研究的热点问题, 对实时系统中的单处理器的调度算法进行了分析与研究, 特别是对其中的单调速率算法和最早时间限优先算法进行了深入的研究, 指出单调速率算法是一种典型的静态调度算法, 并且证明了单调速率算法是单处理器最优的静态优先级调度算法, 同时还指出最早时间限优先算法是一种典型的动态优先级调度算法,证明了最早时间限优先算法是单处理器的最优的动态优先级调度算法. 最后, 为了更好地进行实时系统的建模与调度, 引入了一种新的对任务执行行为进行抽象的方法--T-LET平面方法, 利用这种方法建立了单处理器流调度模型和BLREF调度算法, 并指出这种模型和算法都具有很强的几何背景.     

新组合模型在光电功率预测中的应用

为提高光伏预测要求的精准性,文章提出一种新算法将神经网络和ARMA算法改进组合,构成NEW ARMA-BP模型算法.以某30兆瓦的光伏电站采集的输出功率为输入样本,基于ARMA和BP神经网络算法在Matlab环境下依次搭建了相应的预测模型,预估光伏短期输出量.采用"误差正态检验图"判断基于两种不同算法的误差水平,依据两种单模型预测误差,运用所提出的新方法计算权值并获得新的预测值.基于Matlab的仿真结论验证了组合预测在光伏输出预测领域的适用性.