一种基于区间数多指标信息的FCM聚类算法

针对一类具有不确定性区间数多指标信息的聚类分析问题，依据传统的基于数值信息的FCM聚类算法的思路，提出了一种新的聚类分析算法。章首先描述了具有区间数多指标信息的聚类分析问题；其次给出了基于区间数多指标信息的关于最优划分和最优聚类中心确定的两个定理；然后给出了基于区间数多指标信息的FCM聚类算法的计算步骤。该算法的特点是聚类中心的表现形式为精确的数值，给出的两个定理说明了该聚类算法的收敛性。最后，通过给出一个算例说明了本给出的聚类算法。     

基于AHP和区间数排序可能度法的工程项目综合评标法研究

分析了建设项目现有评标方法,根据已有文献资料和评标办法建立了综合评估法评标指标体系.引入区间数排序可能度法的基本原理,建立了工程项目综合评估法模型.首先运用AHP法确定各指标的权重,根据各评标专家给出的区间数建立初始决策矩阵;然后按照区间数运算法则确定各投标文件的综合评标值,并由投标文件可能度矩阵的排序向量确定中标人.最后,举例应用了该模型,表明了模型的实用性和可操作性.     

指标排序条件下的多指标区间决策

提出了决策指标的权重不能完全确定而只能对权重进行大小排序,并且决策矩阵中的元素包含区间数的多指标决策问题.分4种类型给出区间数决策矩阵的规范化方法,给出了决策方案综合评价值区间的计算模型及算法:给出了区间数比较大小的可能度的概念及可能度的性质;给出了优序数的概念及有关定理.在此基础上,给出了一种简易且具有保序性的方案排序方法.最后应用实例对方法进行说明.     

基于可能度的区间粗糙数排序方法

针对属性权重信息未知或属性权重信息不完全且属性值为区间粗糙数的多属性决策问题,给出一种基于可能度的区间粗糙数排序方法.首先引进和补充了区间粗糙数的一些运算法则及集成算子.然后首次给出了区间粗糙数的可能度定义公式,并研究了该公式所具有的一些良好性质,随后,建立了基于投影思想的极小-极大优化模型来确定各属性权重,同时给出基于可能度矩阵的区间粗糙数排序算法.最后通过实例说明该方法的有效性和可行性.     

基于直觉模糊多属性决策的公路工程评标方法

公路工程评标定标问题的实质是多属性决策问题,专家对参评标书给出了各指标的区间直觉模糊属性值和属性权重的部分信息后,先定义了区间直觉模糊数的得分函数及标准得分差,进而提出了一种基于线性规划模型的区间直觉模糊多属性决策方法,最后通过实例对该决策途径的详细过程及有效性进行了说明.     

学术论文评审的同行评议专家共识问题研究

根据多属性群决策原则,本文提出了一种提高学术论文同行评议的共识度方法——同行专家相互评判的评审方法。即在每位同行评议专家单独评审的基础上,由编辑部将每位同行评议的评审意见和结果反馈到其他同行评议专家的手中,并进行相互逆判,以此得到同行评议专家的群体认可度(据此,可以计算专家权重)。本文将同行评议专家的评判偏好以区间数形式表示,并将其映射成二维坐标平面上的点集,通过引入Weber点对同行评议专家的评判偏好点进行加权集结。本文采用模拟植物生长算法(PGSA),求取集结点,并将其作为评判的依据。最后,本文通过学术论文评审的实例分析以及与其他方法精度比较,说明了该方法的有效性和可行性。     

需求区间型运输问题的求解算法

为了便于建立与需求区间型运输问题有关的决策支持系统，本给出了一个求解需求区间型运输问题的数值算法，证明了算法的理论依据，并举例说明算法的应用，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，大量数值实验表明该算法有较高的计算效率。     

基于可能度排序法的区间信息指派方法

针对具有区间数信息的多目标指派问题,利用区间数可能度排序方法,给出了一种新的指派方法.该方法充分利用实际所给的区间信息进行求解,克服了以往这类指派问题最后由多目标问题转换为单目标问题时权数确定主观性大的缺陷.最后给出了该方法的一个算例.     

基金评审中的归一化分析方法与应用

本以国家自然科学基金评审为主要研究对象，提出平均非共识度和最大非共识度的概念，针对项目评分的最大非共识度分布不一致的问题，应用优化理论、归一化方法等，建立了确定平均非共识度和最大非共识度的优化模型与算法，克服了因最大非共识度的不同影响项目评审公正性的弊端，经国家自然科学基金委员会部分学科的应用，收到很好效果。     

项目评价双层规划、性质及应用

针对基金项目评审、职称（教授，副教授等）评审、奖学金、科研成果奖等评审中常出现的难于处理的各等级之间边界划分问题，提出了非识度等概念，依此建立双层规划模型及算法，论述了相应的数学性质、并应用于面上基金项目的评审中。     

A new nonlinear interval programming method for uncertain problems with dependent interval variables

This paper proposes a new nonlinear interval programming method that can be used to handle uncertain optimization problems when there are dependencies among the interval variables. The uncertain domain is modeled using a multidimensional parallelepiped interval model. The model depicts single-variable uncertainty using a marginal interval and depicts the degree of dependencies among the interval variables using correlation angles and correlation coefficients. Based on the order relation of interval and the possibility degree of interval, the uncertain optimization problem is converted to a deterministic two-layer nesting optimization problem. The affine coordinate is then introduced to convert the uncertain domain of a multidimensional parallelepiped interval model to a standard interval uncertain domain. A highly efficient iterative algorithm is formulated to generate an efficient solution for the multi-layer nesting optimization problem after the conversion. Three computational examples are given to verify the effectiveness of the proposed method.     

Optimal convergence in fame game with familiarity

A modified fame game is proposed by introducing familiarity between agents in the evolution process. We assume that since an agent is recommended, the forgotten probability by the taker decreases with the times that it has been recommended. At the same time, a tunable parameter is introduced to character the correlation between interaction strength and the familiarity. We find that there exists an optimal parameter value leading to the fastest convergence. We attribute this optimal phenomenon to the balance between the recommendation efficiency and the success rate of recommendation. Moreover, some other relevant characters, such as the time interval of forgetting each agent and the success rate of recommendation, are also investigated to understand the dynamics of the modified fame game.     

不完备信息下的灰色粗糙集成决策模型侯亚林

In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.     

拟马尔可夫过程的磨光优化算法及应用

通过对磨光法及马尔可夫过程的研究,马氏过程作为区间预测的一种方法,在很大程度上约束了它预测的科学性,另外,磨光法本身也是一种迭代的方法,对于拟合的精度还是难于控制,通过拟马尔可夫矩阵与磨光法相结合及优化工具,得到拟马尔可夫过程的磨光优化算法,实例表明:拟马尔可夫过程的磨光优化算法使修正磨光后的值逼近原数据值的程度较其它算法更好,而且,拟马尔可夫矩阵反应了从一种状态到另一种状态的转移程度,并且这种算法具有更好的推广和应用。     

A direct solution framework for structural optimization problems with interval uncertainties

A direct solution framework based on multi-objective evolutionary algorithm is developed to solve the structural optimization problems with interval uncertainties. The midpoint and radius of the uncertain original objective are treated as two equally important objectives, which are solved by a multi-objective evolutionary algorithm. The satisfaction value of interval possibility degree model is utilized to deal with nonlinear uncertain constraints and then the degree of constraint violation based on this model is calculated to judge the design vector individuals which one is feasible or infeasible. Subsequently, a selection strategy based on interval constrained-domination rule is utilized to realize the ranking of different design vectors. Finally, two numerical examples and the structural design of augmented reality glasses are investigated to verify the applicability and effectiveness of the proposed method.     

A heuristic verification of the degree of the approximate GCD of two univariate polynomials

We consider the problem of computing verified real interval perturbations of the coefficients of two univariate polynomials such that there exist corresponding perturbed polynomials which have an exact greatest common divisor (GCD) of a given degree k. Based on the certification of the rank deficiency of a submatrix of the Bezout matrix of two univariate polynomials, we propose an algorithm to compute verified real perturbations. Numerical experiments show the performance of our algorithm.     

An optimally convergent adaptive mixed finite element method

We prove convergence and optimal complexity of an adaptive mixed finite element algorithm, based on the lowest-order Raviart–Thomas finite element space. In each step of the algorithm, the local refinement is either performed using simple edge residuals or a data oscillation term, depending on an adaptive marking strategy. The inexact solution of the discrete system is controlled by an adaptive stopping criterion related to the estimator.     

Interval differential evolution with dimension-reduction interval analysis method for uncertain optimization problems

A constrained nonlinear interval optimization method under the framework of differential evolution algorithm is developed to solve the uncertain structural optimization problems with interval uncertainties. The proposed method is a direct optimization method based on the interval differential evolution and dimension-reduction interval analysis. The interval preferential rule based on the satisfaction value of interval possibility degree model is used to realize the direct interval ranking of different design vectors. At each evolutionary generation, the outer optimizer by differential evolution optimizer searches for the best solution within the design space. The dimension-reduction interval analysis is employed to calculate the intervals of objective and constraints for each design vector in the inner layer. This operation transforms the original nesting optimization problem into a single loop one which improves the computational efficiency of the proposed method. Finally, the effectiveness of the presented direct method is verified by two numerical examples and an engineering application.     

带概率判断和模糊区间判断的一种排序算法

对于 AHP中一类判断为模糊、不确定性问题 ,用随机变量和模糊区间描述其判断 ,采用 0 .1～ 0 .9标度 ,建立模糊互补判断矩阵 ,利用数学变换得到模糊一致性判断矩阵 ,给出排序向量算法及公式 ,便于实际应用     

Research on the vehicle routing problem with interval demands

In this paper, a vehicle routing problem with interval demands is investigated based on the motivation of dispatching vehicles to deliver perishable products in practice. A nonlinear interval-based programming method is used to build a model for the vehicle routing problem with interval demands, which assumes that demands of customers are uncertain but fall in given intervals and actual demand of a customer becomes known only when the vehicle visited the customer. A vehicle-coordinated strategy was designed to solve the service failure problem. A hybrid algorithm based on the artificial immune system is also proposed to solve the model for vehicle routing problem with interval demands. The validity of methods and sensitivity analysis are illustrated by conducting some numerical examples. We find that the tolerant possibility degree of interval number has significant impacts on the distances. The planned distance strictly increased, while the additional distance strictly decreased and the total distance after coordinated transport has a U-typed relationship with the tolerant possibility degree of interval number.