(∈, ∈ V q)—n-dimensional Convex Fuzzy Sets

利用n维模糊集截集理论和模糊点与n维模糊集的邻属关系,并利用n+1-值Lukasiewicz蕴涵,首先给出(α,β)-n维凸模糊集的定义,然后对(∈,∈)-n维凸模糊集和(∈,∈Vq)-n维凸模糊集这两种非常有意义的n维凸模糊集进行了讨论,最后得到了一些有意义的结果.这将为n维凸模糊分析理论研究打下基础.     

(∈,∈∨q)-n维凸模糊集

利用n维模糊集截集理论和模糊点与n维模糊集的邻属关系,并利用n+1-值Lukasiewicz蕴涵,首先给出(α,β)-n维凸模糊集的定义,然后对(∈,∈)-n维凸模糊集和(∈,∈∨q)-n维凸模糊集这两种非常有意义的n维凸模糊集进行了讨论,最后得到了一些有意义的结果。这将为n维凸模糊分析理论研究打下基础。     

n维模糊度量空间及其完备性

定义了n维模糊向量的模糊距离、n维模糊度量空间及其完备性的概念,实现了用R上的模糊数度量模糊向量间距离的目的,不仅使得模糊距离的度量更加合理、更加贴切,也创立一套独立于实数的模糊数学分析理论打下了基础。     

一种求解分组0-1背包问题的动态规划法

研究了分组0-1背包问题,提出了一种动态规划解决方法,在物品总数为n个和背包承重量为W时,递推过程的复杂度为O(nW),回溯过程的复杂度为O(n).计算实例表明利用该方法易于找到最优解.     

基于后件模糊集中心的Mamdani模糊系统的降维分解

模糊系统是通过模糊规则来处理不确定信息的一种重要工具,而Mamdani模糊系统则是最简单的一类高维系统模型.文章首先从理论上指出Mamdani模糊系统的传统分解存在错误,并分别针对二维和三维Mamdani模糊系统的实际分解公式进行了分析.其次,基于后件(输出)模糊集中心指标重新获得了n维Mamdani模糊系统的降维分解公式.最后,通过实例分析说明该分解公式不仅可以降低高维Mamdani模糊系统的空间维数,而且也可减少系统内部的模糊规则总数.     

一类模糊数系数矩阵的模糊线性方程组的迭代算法

讨论模糊线性方程组X=A~X U解的存在条件及其迭代算法(其中,A~是以模糊数为元素的n×n矩阵,未知量X和常量U都是以模糊数为元素的n维向量,并且其加法和乘法均由Zadeh的扩张原理定义)。首先研究解的存在条件,尔后探讨求解的迭代算法及误差估计。     

高维情形的Routh定理

本文研究了n维欧氏空间En中n维单形的体积有关问题.利用距离几何的理论与解析方法,建立了n维情形的Routh定理,作为其特例建立了n维情形的Ceva定理.     

n维模糊集的截集及其表示

在已有的n维模糊集和n维模糊集截集的概念基础上,发展出一种新的n维模糊集截集定义及其表示方法,并研究了n维模糊集截集的基本性质;同时结合模糊集的分解定理和表现定理,建立了n维模糊集的分解定理和表现定理。最后,应用该理论对n维模糊集的凸性问题进行了初步的研究和讨论。     

O型模糊数及其结构元表示方法

为研究平面或空间模糊几何问题的需要,在平面或空间模糊点的背景下,给出了O型模糊数的概念,它是一类二维实数域上的模糊集,同时给出了O型模糊数的二维模糊结构元表示方法.二维模糊数的结构元方法,可以使O型模糊数的运算变成普通实数与模糊结构元之间的运算,使得过去必须依赖扩张原理和表现定理来刻画的模糊数运算变得更加简单与直观,不仅仅为模糊分析计算的简化提供了工具,也为二维实数域上模糊分析理论与应用的研究开创了一条新的途径.     

球面空间中的Pedoe不等式与张-杨不等式及应用

本文应用距离几何的理论与方法,研究了n维球面空间中n维单形与有限点集的几何不等式问题,建立了球面空间中n维单形一种形式的Pedoe不等式与有限点集一种形式的张-杨不等式,并应用它获得n维球面空间中Veljan-Korchmaros型不等式与Finsler-Hadwinger型不等式.     

Using fuzzy numbers in knapsack problems

This paper investigates knapsack problems in which all of the weight coefficients are fuzzy numbers. This work is based on the assumption that each weight coefficient is imprecise due to the use of decimal truncation or rough estimation of the coefficients by the decision-maker. To deal with this kind of imprecise data, fuzzy sets provide a powerful tool to model and solve this problem. Our work intends to extend the original knapsack problem into a more generalized problem that would be useful in practical situations. As a result, our study shows that the fuzzy knapsack problem is an extension of the crisp knapsack problem, and that the crisp knapsack problem is a special case of the fuzzy knapsack problem.     

Solving the knapsack problem with imprecise weight coefficients using genetic algorithms

This paper investigates solving the knapsack problem with imprecise weight coefficients using genetic algorithms. This work is based on the assumption that each weight coefficient is imprecise due to decimal truncation or coefficient rough estimation by the decision-maker. To deal with this kind of imprecise data, fuzzy sets provide a powerful tool to model and solve this problem. We investigate the possibility of using genetic algorithms in solving the fuzzy knapsack problem without defining membership functions for each imprecise weight coefficient. The proposed approach simulates a fuzzy number by distributing it into some partition points. We use genetic algorithms to evolve the values in each partition point so that the final values represent the membership grade of a fuzzy number. The empirical results show that the proposed approach can obtain very good solutions within the given bound of each imprecise weight coefficient than the fuzzy knapsack approach. The fuzzy genetic algorithm concept approach is different, but gives better results than the traditional fuzzy approach.     

模糊人工蜂群算法的多选择多维背包问题求解

针对传统人工蜂群算法早熟收敛问题,基于模糊化处理和蜂群寻优的特点,提出一种模糊人工蜂群算法。将模糊输入输出机制引入到算法中来保持蜜源访问概率的动态更新。根据算法计算过程中的不同阶段对蜜源访问概率有效调整,避免算法陷入局部极值。通过对多选择多维背包问题的仿真实验和与其他算法的比较,表明本算法可行有效,有良好的鲁棒性。     

The 0-1 knapsack problem with fuzzy data

The 0-1 knapsack problem with imprecise profits and imprecise weights of items is considered. The imprecise parameters are modeled as fuzzy intervals. A method of choosing a solution under the uncertainty is proposed and two methods for solving the constructed models are provided.     

Analysis of maximum total return in the continuous knapsack problem with fuzzy object weights

This paper proposes a parametric programming approach to analyze the fuzzy maximum total return in the continuous knapsack problem with fuzzy objective weights, in that the membership function of the maximum total return is constructed. The idea is based on Zadeh’s extension principle, α-cut representation, and the duality theorem of linear programming. A pair of linear programs parameterized by possibility level α is formulated to calculate the lower and upper bounds of the fuzzy maximum total return at α, through which the membership function of the maximum total return is constructed. To demonstrate the validity of the proposed procedure, an example studied by the previous studies is investigated successfully. Since the fuzzy maximum total return is completely expressed by a membership function rather than by a crisp value reported in previous studies, the fuzziness of object weights is conserved completely, and more information is provided for making decisions in real-world resource allocation applications. The generalization of the proposed approach for other types of knapsack problems is also straightforward.     

Fractional knapsack problems

The fractional knapsack problem to obtain an integer solution that maximizes a linear fractional objective function under the constraint of one linear inequality is considered. A modification of the Dinkelbach's algorithm [3] is proposed to exploit the fact that good feasible solutions are easily obtained for both the fractional knapsack problem and the ordinary knapsack problem. An upper bound of the number of iterations is derived. In particular it is clarified how optimal solutions depend on the right hand side of the constraint; a fractional knapsack problem reduces to an ordinary knapsack problem if the right hand side exceeds a certain bound.     

A generalized knapsack problem with variable coefficients

The ordinary knapsack problem is to find the optimal combination of items to be packed in a knapsack under a single constraint on the total allowable resources, where all coefficients in the objective function and in the constraint are constant.In this paper, a generalized knapsack problem with coefficients depending on variable parameters is proposed and discussed. Developing an effective branch and bound algorithm for this problem, the concept of relaxation and the efficiency function introduced here will play important roles. Furthermore, a relation between the algorithm and the dynamic programming approach is discussed, and subsequently it will be shown that the ordinary 0–1 knapsack problem, the linear programming knapsack problem and the single constrained linear programming problem with upper-bounded variables are special cases of the interested problem. Finally, practical applications of the problem and its computational experiences will be shown.     

Heuristic and Exact Algorithms for the Precedence-Constrained Knapsack Problem

The knapsack problem (KP) is generalized taking into account a precedence relation between items. Such a relation can be represented by means of a directed acyclic graph, where nodes correspond to items in a one-to-one way. As in ordinary KPs, each item is associated with profit and weight, the knapsack has a fixed capacity, and the problem is to determine the set of items to be included in the knapsack. However, each item can be adopted only when all of its predecessors have been included in the knapsack. The knapsack problem with such an additional set of constraints is referred to as the precedence-constrained knapsack problem (PCKP). We present some dynamic programming algorithms that can solve small PCKPs to optimality, as well as a preprocessing method to reduce the size of the problem. Combining these, we are able to solve PCKPs with up to 2000 items in less than a few minutes of CPU time.