面向词计算的模糊约束重译

对词计算第三步模糊约束重译进行了研究.指出了候选词汇对应的模糊子集和结果约束中的模糊子集之间的贴近度和包容度在获取可信重译结果中的重要性.给出了分离算子的概念并设计了一个满足要求的分离算子.在此基础上定义了包容度的概念并给出了重译指数的计算公式.提出了一种基于重译指数计算的模糊约束重译方法.最后用实例说明了该方法的有效性.     

关于模糊拟阵的独立子集套和独立集函数

本文的研究方法主要是将模糊拟阵问题转化为普通拟阵问题来研究的方法。本文首先建立独立子集套概念,并使用这个概念和独立集函数概念构建了闭模糊拟阵的充要条件和模糊独立集的充要条件;然后,本文仔细分析了模糊基的性质,找到了一个使用独立子集套和独立集函数来描述的模糊基的充要条件;最后,利用模糊基的这个充要条件提出并证明了闭正规模糊拟阵的充要条件。     

L-保序算子空间的ω-仿紧性

在L-保序算子空间中引入ω-仿紧性,证明它是L-好的推广,它对ω-闭子集遗传、ω-紧的L-保序算子空间和ω-仿紧空间的积空间是仿紧的.     

一种基于LPA集结算子的群体一致性程度的计算方法

针对基于语言评价信息的多指标群决策分析中群体一致性程度计算问题,依据近年来最新发展的二元语义及其算子和PA算子,给出了一种用于集结语言评价信息的L PA算子,并基于L PA算子,进一步给出了关于群体一致性程度的计算步骤.最后给出了一个算例.     

拟相似算子的本质谱的连通分支

设算子A和B拟相似,本文精细地刻划了算子A的各种本质谱的连通分支与算子B的本质谱的各种子集的相交关系,肯定地回答了Fialkow在文献[1]中提出的一个问题。     

一种基于子集测度的FCM聚类加权指数计算方法

在模糊C均值(Fuzzy C-Means,FCM)聚类应用过程中,针对目前模糊加权指数的确定缺乏理论依据和有效评价方法这一问题,提出了一种基于子集测度的模糊加权指数计算方法.首先根据子集测度理论定义了一个聚类有效性函数,然后依据该函数在聚类过程中通过循环进化迭代来计算聚类结果的有效性,并将其值反馈到模糊加权指数m的变化中,而使m收敛到一个稳定解,即得到最佳模糊加权指数.理论分析和实验表明,该算法是有效的,为模糊加权指数m的探讨研究提供了一种新的思路和途径.     

基于灰色关联法和HA算子的Pythagorean模糊群决策方法

针对Pythagorean模糊群决策问题,提出一种基于Pythagorean模糊混合平均算子的决策方法。首先,提出一种基于Pythagorean模糊信息及其运算法则的Pythagorean模糊混合平均算子;其次,构建一种基于最大熵模型的属性位置权重定权方法,同时根据灰色关联方法提出一种属性客观权重计算方法,进而获得Pythagorean模糊混合平均算子的定权方法;利用Pythagorean模糊混合平均算子对单决策者信息进行融合,通过Pythagorean模糊加权平均算子对各专家信息进行融合,并依据得分函数与精确函数进行排序择优;最后,通过一个算例说明该方法的有效性和可行性。     

基于投影算子的回归神经网络模型及其在最优化问题中的应用

研究了一种基于投影算子的神经网络模型．与以前研究投影算子的值域一般是n维欧氏空间中的紧凸子集不同,而是n维欧氏空间中未必有界的闭凸子集,同时目标函数也是一般的连续可微函数,未必为凸函数．证明了所研究的神经网络模型具有整体解轨道,以及当目标函数满足某些条件时解轨道的整体收敛性．此外,还将所研究的模型应用于闭凸约束极小化问题以及非线性互补问题和隐互补问题中,并通过数值模拟说明了该神经网络方法的有效性．     

子集系统决定的拓扑空间

对子集系统Z,引入了由Z所诱导的算子d_Z,讨论了由子集系统决定的拓扑空间,主要结果如下:(1)单点子集系统和有限子集系统决定的拓扑相同;(2)对于单点,有限,幂集和链四种子集系统Z, d_Z是拓扑算子,且d_Z~2=d_Z,从而对任意T_0的拓扑空间(X,τ), d_Z(τ)是细于τ的最粗的ZD拓扑.     

一类孤子方程族及其多个Hamilton结构

本文建立了一个含11个位势的新的等谱问题,得到了一组新的Lax对,由此得到一类新的孤子方程族.该族是Liouville可积的,具有4-Hamilton结构,且循环算子的共轭算子是一个遗传对称算子.另外,为确切说明所得方程族是一个4-Hamilton结构,在附录中证明了所得的4个Hamilton算子的线性组合恒为Hamilton算子.     

A genetic algorithm-based heuristic for solving the weighted maximum independent set and some equivalent problems

In this paper we present a genetic algorithm-based heuristic especially for the weighted maximum independent set problem (IS). The proposed approach treats also some equivalent combinatorial optimization problems. We introduce several modifications to the basic genetic algorithm, by (i) using a crossover called two-fusion operator which creates two new different children and (ii) replacing the mutation operator by the heuristic-feasibility operator tailored specifically for the weighted independent set. The performance of our algorithm was evaluated on several randomly generated problem instances for the weighted independent set and on some instances of the DIMACS Workshop for the particular case: the unweighted maximum clique problem. Computational results show that the proposed approach is able to produce high-quality solutions within reasonable computational times. This algorithm is easily parallelizable and this is one of its important features.     

Solving nonlinear multi-objective optimization problems with fuzzy relation inequality constraints regarding Archimedean triangular norm compositions

We propose an approach to solve a nonlinear multi-objective problem subject to fuzzy relation inequalities with max-Archimedean-t-norm composition by a genetic algorithm. The additive generator of Archimedean t-norms is utilized to reform the existent genetic algorithm to solve the constrained nonlinear multi-objective optimization problems. We consider thoroughly the feasible set of the fuzzy relation inequality systems in three possible cases, namely “≤”, “≥” and the combination of them. In general, their feasible sets are nonconvex which are completely determined by one vector as their maximum solution and a finite number of minimal solutions. The maximum and minimal solutions are formulated by using the additive generator. Additionally, we present some conditions for each case under which the problem can be reduced. Finally, each reduced problem is solved by the genetic algorithm and the efficiency of the proposed method is shown by some numerical examples.     

An alternative approach for unbalanced assignment problem via genetic algorithm

This paper presents an alternative approach using genetic algorithm to a new variant of the unbalanced assignment problem that dealing with an additional constraint on the maximum number of jobs that can be assigned to some agent(s). In this approach, genetic algorithm is also improved by introducing newly proposed initialization, crossover and mutation in such a way that the developed algorithm is capable to assign optimally all the jobs to agents. Computational results with comparative performance of the algorithm are reported for four test problems.     

On finding augmenting graphs

Method of augmenting graphs is a general approach to solve the maximum independent set problem. As the problem is generally NP-hard, no polynomial time algorithms are available to implement the method. However, when restricted to particular classes of graphs, the approach may lead to efficient solutions. A famous example of this type is the maximum matching algorithm: it finds a maximum matching in a graph G, which is equivalent to finding a maximum independent set in the line graph of G. In the particular case of line graphs, the method reduces to finding augmenting (alternating) chains. Recent investigations of more general classes of graphs revealed many more types of augmenting graphs. In the present paper we study the problem of finding augmenting graphs different from chains. To simplify this problem, we introduce the notion of a redundant set. This allows us to reduce the problem to finding some basic augmenting graphs. As a result, we obtain a polynomial time solution to the maximum independent set problem in a class of graphs which extends several previously studied classes including the line graphs.     

Using critical sets to solve the maximum independent set problem

A method that utilizes the polynomially solvable critical independent set problem for solving the maximum independent set problem on graphs with a nonempty critical independent set is developed. The effectiveness of the proposed approach on large graphs with large independence number is demonstrated through extensive numerical experiments.     

A polynomial algorithm to find an independent set of maximum weight in a fork-free graph

The class of fork-free graphs is an extension of claw-free graphs and their subclass of line graphs. The first polynomial-time solution to the maximum weight independent set problem in the class of line graphs, which is equivalent to the maximum matching problem in general graphs, has been proposed by Edmonds in 1965 and then extended to the entire class of claw-free graphs by Minty in 1980. Recently, Alekseev proposed a solution for the larger class of fork-free graphs, but only for the unweighted version of the problem, i.e., finding an independent set of maximum cardinality. In the present paper, we describe the first polynomial-time algorithm to solve the problem for weighted fork-free graphs.     

Maximum weight independent set in trees

Computing a maximum independent set, weighted or unweighted, isNP-hard for general as well as planar graphs. However, polynomial time algorithms do exist for solving this problem on special classes of graphs. In this paper we present an efficient algorithm for computing a maximum weight independent set in trees. A divide and conquer approach based on centroid decomposition of trees is used to compute a maximum weight independent set withinO(n logn) time, wheren is the number of vertices in the tree. We introduce a notion of analternating tree which is crucial in obtaining a new independent set from the previous one.     

A method for constructing a resolving control in an approach problem based on attraction to the feasibility set

The problem of the approach of a nonlinear control system to a compact target set in the phase space at a fixed time is studied. An algorithm for constructing a solution of this problem based on maximum attraction of the system’s motion to the feasibility set is proposed.     

A hybrid heuristic for the maximum clique problem

In this paper we present a heuristic based steady-state genetic algorithm for the maximum clique problem. The steady-state genetic algorithm generates cliques, which are then extended into maximal cliques by the heuristic. We compare our algorithm with three best evolutionary approaches and the overall best approach, which is non-evolutionary, for the maximum clique problem and find that our algorithm outperforms all the three evolutionary approaches in terms of best and average clique sizes found on majority of DIMACS benchmark instances. However, the obtained results are much inferior to those obtained with the best approach for the maximum clique problem.     

A hybrid genetic algorithm-heuristic for a two-dimensional orthogonal packing problem

In this paper we address a two-dimensional (2D) orthogonal packing problem, where a fixed set of small rectangles has to be placed on a larger stock rectangle in such a way that the amount of trim loss is minimized. The algorithm we propose hybridizes a placement procedure with a genetic algorithm based on random keys. The approach is tested on a set of instances taken from the literature and compared with other approaches. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm.