指数族ACD模型的参数估计及其Bootstrap置信区间

针对指数族ACD模型,采用Scoring算法对模型进行参数估计,得到指数族情况下Scoring算法中方向向量的计算公式,并讨论了该算法的收敛性以及估计值的相合性.本文运用W ild Bootstrap构建参数的置信区间和置信带,将其结果与传统的残差Bootstrap做比较,数据模拟的结果表明,W ild Bootstrap处理问题更加快捷,结果更加准确.     

逻辑系统(W)、W、Wn中的广义矛盾式及一种降级算法

研究了多值逻辑系统(W)、W、Wn中的广义矛盾式,并对这些多值逻辑系统中的广义矛盾式进行了分划,强调了这种分划的不重不漏.在(W)、W、Wn中给出了一种降级算法,但(W)、W中的矛盾式并不能由对非矛盾式进行降级得到.而在Wn中,矛盾式可由非矛盾式经过有限次降级得到.     

具有一般控制策略的成批服务排队的计算

本文研究W.B.Powell提出的具有一般控制策略的成批服务排队系统M/G/l。求出了队长平稳分布母函数的关键性系数矩阵,改进了平稳队长的算法,从而较好地解决了该排队系统的计算问题。     

FMT问题的两种三Ⅰ算法及其还原性

进一步研究FMT问题,得到该问题的三Ⅰ算法的一般计算公式,提出该问题的一种新算法三Ⅰ^*算法,给出新算法的一般计算公式,讨论两种算法的还原性问题,明确两种还原性的含义,证明FMT问题的三Ⅰ算法是W－还原的,而三Ⅰ^*算法是Z－还原的。     

灵活单纯形算法

本文概述C.B.Garcia和W.I.Zangwill的灵活单纯形算法,论证算法的可行性,并对在优化问题中应用灵活单纯形算法的前景进行探讨。 1.引言自从Scarf首先利用Lemke、Lemke和Howson的互补原理来计算非线性映射不动点以来,许多求不动点或零点的算法出现了。例如:Merrill提出的重复开始算法,Kuhn和Mackinnon提出的“三明治”算法,Eaves提出的单纯同伦算法等等。     

供给总量限定需求区间约束型运输问题——时限费用优化模型与算法

本文目的是为建立与运输问题有关的决策支持系统提供方便.本文建立了供给总量限定需求区间约束型运输问题的对时限与费用两个目标进行优化的多目标规划模型,给出了求解模型的算法,并举例说明了算法的应用.该算法能求得问题的最优解,并具有易于编程实现、收敛性好等优点.数值实验表明该算法有较高的计算效率,可用于求解某些类型的指派问题.     

核范数和谱范数下广义Sylvester方程最小二乘问题的有效算法

本文从数值角度讨论Schatten q-范数下的广义Sylvester方程约束最小二乘问题min x∈s‖N∑i=1A_iXB_i—C‖_q,其中S为闭凸约束集合,Schatten q-范数定义为‖M‖_q~q=∑_(i=1)~nσ_i~q(M),其中σ_i(M)为M∈R~(n×n)的奇异值.该问题的几类特殊情形在图像处理、控制论等领域有广泛的应用.q=2即Frobenius范数下该问题已被充分研究,故本文着重讨论q=1,+∞,即核范数和谱范数下该问题的数值求解.采用的数值方法是非精确标准容易执行的部分非精确交替方向法,并结合奇异值阈值算法,Moreau-Yosida正则化算法,谱投影算法和LSQR算法等求解相应子问题.给出算法的收敛性证明,并用数值算例验证其高效可行性.     

空中加油问题的递推模型与调度策略

首先对空中加油问题进行了分析,提取了相关性质,在此基础上建立了问题的递推模型.根据该模型,提出了一种启发式搜索算法.该算法计算复杂度低,适用性好.对应于辅机是否可以多次起飞,该算法分为两子算法.对这两种不同情况下的具体问题,设计了相关的优化函数.所有算法都在计算机中运行,并得到了相应结果.值得指出的是,提出的启发式搜索算法十分高效.对于问题1和问题2,该算法所得解是约束条件下的最优调度策略.对于问题3,问题4,问题5,该算法所得解逼近最优调度策略.     

协作供应条件下成本优化模型及其算法

在保证供应不间断的前提下,讨论了多供应点、多时间需求的协作供应问题,建立了使包括运输成本、购货成本以及库存成本在内的总成本最小的数学模型.设计了一个求解该模型的算法,证明了该算法的可行性和最优性,并给出了该算法的算法复杂度.最后给出了一个算例,和相关算法相比较可知该算法更具有实际应用价值.     

基于乌鸦喝水的元启发式优化算法

启发式优化算法已成为求解复杂优化问题的一种有效方法,可用于解决传统的优化方法难以求解的问题.受乌鸦喝水寓言故事启发,提出一种新型元启发式优化算法—乌鸦喝水算法,首先建立了乌鸦喝水算法数学模型;其次,给出实现该算法的详细步骤;最后,将该算法用于基准函数优化,并将该算法与乌鸦搜索算法、粒子群优化算法、多元宇宙优化算法、花授粉算法、布谷鸟算法等群智能算法进行了比较.仿真实验结果表明,乌鸦喝水算法优于其他算法.     

多层防御模式下武器目标分配决策的群体智能优化算法

武器目标分配(WTA)是军事运筹学中经典的NP完全问题,迄今为止未找到求精确解的多项式时间算法.针对武器数量、布防空间、运行维护成本以及人力资源等多约束下的多层防御WTA问题,采用粒子群优化(PSO)和蚁群优化(ACO)两种群体智能算法求解.给出了PSO和ACO算法实现方案,通过一个算例评估两个算法的性能.结果表明,两种算法都能给出高质量的近似最优解,对求解WTA问题是有效的.PSO在解的质量、算法鲁棒性和计算效率方面均优于ACO.     

Heuristic approaches for solving transit vehicle scheduling problem with route and fueling time constraints

Electric bus scheduling problem can be defined as vehicle scheduling problem with route and fueling time constraints (VSPRFTC). Every vehicle’s travel miles (route time) after charging is limited, thus the vehicle must be recharged after taking several trips and the minimal charging time (fueling time) must be satisfied. A multiple ant colony algorithm (ACA) was presented to solve VSPRFTC based on ACA used to solve traveling salesman problem (TSP), a new metaheuristic approach inspired by the foraging behavior of real colonies of ants. The VSPRFTC considered in this paper minimizes a multiple, hierarchical objective function: the first objective is to minimize the number of tours (or vehicles) and the second is to minimize the total deadhead time. New improvement of ACA as well as detailed operating steps was provided on the basis of former algorithm. Then in order to settle contradiction between accelerating convergence and avoiding prematurity or stagnation, improvement on route construction rule and Pheromone updating rule was adopted. A group feasible trip sets (blocks) had been produced after the process of applying ACA. In dealing with the fueling time constraint a bipartite graphic model and its optimization algorithm are developed for trip set connecting in a hub and spoke network system to minimize the number of vehicle required. The maximum matching of the bipartite graph is obtained by calculating the maximum inflow with the Ford–Fulkerson algorithm. At last, an example was analyzed to demonstrate the correctness of the application of this algorithm. It proved to be more efficient and robust in solving this problem.     

求解图着色问题的量子蚁群算法

针对经典的图着色问题,在蚁群算法的基础上结合量子计算提出一种求解图着色问题的量子蚁群算法. 将量子比特和量子逻辑门引入到蚁群算法中,较好地避免了蚁群算法搜索易陷入局部极小的缺陷,并显著加快了算法的运算速度. 通过图着色实例的大量仿真实验,表明算法对图着色问题的求解是可行的、有效的,且具有通用性.     

启发式蚁群算法及其在高填石路堤稳定性分析中的应用

模仿蚁群之群体行为而发展起来的蚁群算法 ( ACA) ,目前多用于求解组合优化问题 .为了让 ACA能求解复杂边坡工程问题 ,本文对蚁群算法的结构和蚂蚁转移概率的确定方法进行改进 ,得到一个新的评价复杂边坡稳定性的方法——启发式蚁群算法 ( HACA) ,探讨了 HACA在高填石路堤稳定性分析中的应用 .     

零售业连锁网点选址与布局演化模型

蚁群算法是近年来出现的一种新型仿生优化算法,是求解复杂优化问题有效方法.本文建立了基于蚁群算法的零售业连锁网点选址与布局演化模型,并利用Matlab进行仿真研究.通过对模拟结果的分析,验证了零售业连锁网点的选址与布局规律.     

Accelerating Galerkin BEM for linear elasticity using adaptive cross approximation

The adaptive cross approximation (ACA) algorithm (Numer. Math. 2000; 86 :565–589; Computing 2003; 70 (1):1–24) provides a means to compute data‐sparse approximants of discrete integral formulations of elliptic boundary value problems with almost linear complexity. ACA uses only few of the original entries for the approximation of the whole matrix and is therefore well‐suited to speed up existing computer codes. In this article we extend the convergence proof of ACA to Galerkin discretizations. Additionally, we prove that ACA can be applied to integral formulations of systems of second‐order elliptic operators without adaptation to the respective problem. The results of applying ACA to boundary integral formulations of linear elasticity are reported. Furthermore, we comment on recent implementation issues of ACA for non‐smooth boundaries. Copyright © 2006 John Wiley & Sons, Ltd.     

Construction of Hamiltonian cycles by recurrent neural networks in graphs of distributed computer systems

An algorithm based on a recurrent neural Wang’s network and the WTA (“Winner takes all”) principle is applied to the construction of Hamiltonian cycles in graphs of distributed computer systems (CSs). The algorithm is used for: 1) regular graphs (2D- and 3D-tori, and hypercubes) of distributed CSs and 2) 2D-tori disturbed by removing an arbitrary edge. The neural network parameters for the construction of Hamiltonian cycles and suboptimal cycles with a length close to that of Hamiltonian ones are determined. Our experiments show that the iterative method (Jacobi, Gauss-Seidel, or SOR) used for solving the system of differential equations describing a neural network strongly affects the process of cycle construction and depends on the number of torus nodes.     

On some applications of quasideviation means

ABSTRACT

We show that willingness to accept (WTA) and willingness to pay (WTP) are particular cases of quasideviation means. Applying this fact we characterize some properties of WTA and WTP related to the experimentally observed disparity between them.     

Matrix valued adaptive cross approximation

A new variant of the Adaptive Cross Approximation (ACA) for approximation of dense block matrices is presented. This algorithm can be applied to matrices arising from the Boundary Element Methods (BEM) for elliptic or Maxwell systems of partial differential equations. The usual interpolation property of the ACA is generalised for the matrix valued case. Some numerical examples demonstrate the efficiency of the new method. The main example will be the electromagnetic scattering problem, that is, the exterior boundary value problem for the Maxwell system. Here, we will show that the matrix valued ACA method works well for high order BEM, and the corresponding high rate of convergence is preserved. Another example shows the efficiency of the new method in comparison with the standard technique, whilst approximating the smoothed version of the matrix valued fundamental solution of the time harmonic Maxwell system. Copyright © 2016 John Wiley & Sons, Ltd.     

流水车间作业排序问题蚁群算法研究

本文运用蚁群算法研究辨台处理机、目标函数为时间表长最小的同顺序排列流水车间作业排序问题，设计出解决该问题的算法步骤与流程。最后，通过仿真比较该算法与解决该问题的其它启发式算法性能，计算效果比较满意。