基于模拟退火的CRS算法

本文是对非线性规划问题提出的一种算法,该算法把模拟退火算法应用到CRS算法中,根据模拟退火算法每一次迭代都体现集中和扩散两个策略的平衡的特点,使CRS算法更能够搜索到全局最优解,而不会陷入局部最优解。最后把提出的算法应用到两个典型的函数优化问题中,结果表明,算法是可行的、有效的     

现代优化计算方法在蛋白质结构预测中的应用

现代优化计算方法在蛋白质结构预测中占有重要地位.简要地介绍了模拟退火算法,遗传算法,人工神经网络和图论算法在蛋白质结构预测中的应用.对国内外近年来应用这些算法,特别是在蛋白质构象搜索问题中,解决蛋白质结构预测的研究作了回顾,并分析、比较了这几种算法的效果和特点.     

混料模型D—最优试验设计的改进DE算法研究

差分进化算法(Differential Evolution Algorithm)有较强的全局收敛能力和稳健性,应用该算法可解决工程学、计算机科学等领域的一些复杂优化问题。本文介绍一个改进DE算法,该算法适用于混料模型的近似最优设计问题,对于具有附加约束试验域的混料问题也可高效求解。最后,本文给出应用改进DE算法求解混料模型D-最优试验设计的例子。     

分组检测方法综述

分组检测是一种经济且有效的检测算法。它在流行病学、农业及基因组学等领域有着广泛的应用。本文主要从分组检测的起源、发展及其应用着手,着重于介绍分组检测算法中的统计推断方法,并讨论了目前分组检测算法面临的一些问题和挑战。     

一般欧氏空间点集凸包的快速实时算法

点集凸包算法是被Shmaos等称之为计算几何中的基本问题之一,这是由于它在计算机辅助设计、计算机图形学、模式识别和运筹学等领域中有着十分广泛的应用。 对于2、3维凸包算法的研究已有许多成果,给出了各种不同意义下的最佳算法(参见[2])。但是对于高维空间点集凸包算法的研究却甚少,目前只有两种算法在计算几何中得到应用。造成这种局面的因素乃是高维空间的抽象性质,缺少2,3维空间的那种几     

基于改进蝙蝠算法的贝叶斯网络结构学习

针对蝙蝠算法在搜索评分阶段易陷入局部最优且收敛精度低,以及基于蝙蝠算法的贝叶斯网络结构学习不完善等缺点,将模拟退火算法的思想引入到蝙蝠算法中,并对某些蝙蝠个体进行高斯扰动,提出了一种改进蝙蝠算法的贝叶斯网络结构混合学习算法.混合算法首先应用最大最小父子节点集合算法(Max-min parents and children,MMPC)来构建初始无向网络的框架,然后利用改进的蝙蝠算法进行评分搜索并确定边的方向.最后把应用本算法学习的ALARM网,和蚁群算法(MMACO)、蜂群算法(MMABC)进行比较,结果表明本混合算法具有较强的学习能力和更好的收敛速度,并且能够得到与真实网络更匹配的贝叶斯网络.     

求解运输问题的一个算法

给出一个求解问题的数值算法，证明了算法的理论依据，并举例说明算法的应用。     

GIHS-TSFNN并行学习算法及其应用研究

针对传统T-S模糊神经网络的随机初始网络参数导致网络学习速度慢、易陷入局部解以及运算精度低等缺陷,提出了一种应用佳点集的改进和声搜索算法(GIHS)优化T-S模糊神经网络的并行学习算法.首先应用佳点集择优构造更加高质量的初始和声库,然后搜索过程中进行参数动态调整,并且每次迭代产生多个新解,充分利用和声记忆库的信息,以提高算法的全局搜索能力和收敛速度.其次,将GIHS算法与T-S神经网络相结合构建并行学习算法,实现两种算法的并行交互集成,得到了最优参数配置以提高T-S模糊神经网络的泛化能力.最后将该算法应用到农业干旱等级预测中以解决旱情评估问题.仿真实验表明,GIHS算法性能优于基本HS和IHS算法,且与T-S模糊神经网络、HS算法优化的T-S模糊神经网络和IHS算法优化的T-S模糊神经网络相比,具有更高的预测准确度.     

求车间最优逐月生产计划的一个算法

给出一个求车间最优逐月生产计划的数值算法，证明了算法的理论依据，并举例说明算法的应用。     

求解排课表问题的一个启发式数值算法

为了便于建立与排课表问题有关的决策支持系统,本文给出一个求解排课表问题的启发式数值算法,证明了算法的正确性,并举例说明算法的应用.该算法具有易于编程实现、收敛性好等优点,大量数值实值表明该算法能求得问题的最优解.     

The Γ-algorithm and some applications

In this paper the power of the Γ-algorithm for obtaining the dual of a given cone and some of its multiple applications is discussed. The meaning of each sequential tableau appearing during the process is interpreted. It is shown that each tableau contains the generators of the dual cone of a given cone and that the algorithm updates the dual cone when new generators are incorporated. This algorithm, which is based on the duality concept, allows one to solve many problems in linear algebra, such as determining whether or not a vector belongs to a cone, obtaining the minimal representations of a cone in terms of a linear space and an acute cone, obtaining the intersection of two cones, discussing the compatibility of linear systems of inequalities, solving systems of linear inequalities, etc. The applications are illustrated with examples.     

一个新的单纯形类算法

提出了一个求解线性规划的新单纯形类算法。它不仅无须引入人工变量，而且在第一阶段中采用无比检验。因此新算法比Arsham最近提出的push-to—pull算法效率更高。此外，本算法的数值稳定性也优于push—to—pull算法。     

单纯形法检验数的新计算方法

单纯形法仍然是求解线性规划最具竞争力的算法之一,改进它的计算效率仍具有理论和现实意义.本文通过改进检验数的计算方式,提出了一种实施单纯形法新的计算方式.这种计算方式方便简单,无论采用单纯形表还是采用数值迭代计算都可以提高计算效率.     

A specialized network simplex algorithm for the constrained maximum flow problem

The constrained maximum flow problem is to send the maximum flow from a source to a sink in a directed capacitated network where each arc has a cost and the total cost of the flow cannot exceed a budget. This problem is similar to some variants of classical problems such as the constrained shortest path problem, constrained transportation problem, or constrained assignment problem, all of which have important applications in practice. The constrained maximum flow problem itself has important applications, such as in logistics, telecommunications and computer networks. In this research, we present an efficient specialized network simplex algorithm that significantly outperforms the two widely used LP solvers: CPLEX and lp_solve. We report CPU times of an average of 27 times faster than CPLEX (with its dual simplex algorithm), the closest competitor of our algorithm.     

有界变量线性规划的一种简易解法

本文在[1]的基础上，较系统地叙述了有界变量线性规划一种简易解法的基本思路、方法步骤、理论分析和应用举例。指出，因变量有界所引起的种种麻烦在这里通过单纯形表的小小变动便加以解决了。     

On the Equivalence of the Simplex Methods and a Multiplier-Alike Method for Linear Programming

In linear programming, the simplex method has been viewed for a long time as an efficient tool. Interior methods have attracted a lot of attention since they were proposed recently. It seems plausible intuitively that there is no reason why a good linear programming algorithm should not be allowed to cross the boundary of the feasible region when necessary. However, such an algorithm is seldom studied. In this paper, we will develop first a framework of a multiplier-alike algorithm for linear programming which allows its trajectory to move across the boundary of the feasible region. Second, we illustrate that such a framework has the potential to perform as well as the simplex method by showing that these methods are equivalent in a well-defined sense, even though they look so different.     

Computational aspects of simplex and MBU-simplex algorithms using different anti-cycling pivot rules

Abstract

Several variations of index selection rules for simplex-type algorithms for linear programming, like the Last-In-First-Out or the Most-Often-Selected-Variable are rules not only theoretically finite, but also provide significant flexibility in choosing a pivot element. Based on an implementation of the primal simplex and the monotonic build-up (MBU) simplex method, the practical benefit of the flexibility of these anti-cycling pivot rules is evaluated using public benchmark LP test sets. Our results also provide numerical evidence that the MBU-simplex algorithm is a viable alternative to the traditional simplex algorithm.     

An infeasible (exterior point) simplex algorithm for assignment problems

The so called Modified Hung—Rom Algorithm, based upon theoretical considerations of Hirsch-paths, seems to be one of the most efficient algorithms for assignment problems. Since any two basic feasible solutions to a linear problem can always be connected with a short simplex path passing through the infeasible region, development of algorithms based upon theoretical considerations on infeasible paths seems to be of great practical interest. This paper presents an algorithm of this kind for assignment problems.     

线性规划的符号跟踪算法

分析了只含一个约束条件的线性规划最优基变量的特征，将其运用到搜寻含m个约束条件的线性规划的最优基变量，从而提出了线性规划的符号跟踪算法，为线性规划求解提供了新途径。     

二分单纯形算法中子规划问题的进一步研究

本对二分单纯形算法的子规划问题作进一步研究,提出一个新的子规划问题来改善问题的不可行性,并确定了相应的主元旋转规则,并编制了相应于新子规划的新二分算法,并对94个线性规划问题进行了数值实验,实验结果表明,新二分算法是一种改进的二分算法。