状态变权向量的等效分析以及一类均衡函数的构造

进一步讨论变权综合理论的基本性质和均衡函数的构造方法。首先证明了一个状态变权向量等效的充要条件。然后，以两个反例分别说明变权向量不一定都能表示成因素常权向量与状态变权向量的归一化的Hadamard乘积；状态变权向量并非都是均衡函数的梯度向量。最后，引入因素状态向量离散度的定义，并由此得到了一类新的均衡函数。     

变权决策中确定状态变权向量的理想点法

利用因素状态向量构造正理想状态向量X+和负理想状态向量X-,然后由这两个理想状态向量分别构造两个极不均衡的状态向量RX+-和RX-+;根据状态变权向量对RX+-和RX-+的调权效果以及OWA算子中的主观偏好参数A-C(W)建立一个确定状态变权向量参数的数学模型,为解决变权综合过程中如何选用合适的状态变权向量提供了一个可操作性的方法.最后,通过一个实例对该方法的应用进行了分析、验证.     

等效均衡函数的性质及均衡函数的构造

从均衡函数的等效性出发研究均衡函数的性质,证明了等效的均衡函数之和、积以及数乘仍为均衡函数,并且得到的新均衡函数与原均衡函数等效,这些性质表明等效的均衡函数关于加法和乘法运算均具有半群的代数结构。另外,进一步讨论了构造均衡函数的方法,给出了两个构造均衡函数的定理,该方法具有一般性,现有文献中的均衡函数几乎都能由其构造得到。     

一种基于变权的人才选优决策模型

根据全面型人才和专长型人才的特点,利用惩罚型状态变权向量和激励型状态变权向量完成因素之间权重的转移,进而实现因素之间的均衡或突出某些因素的作用,由此建立了一个人才选优的综合决策模型.     

位势算子的带权向量值不等式

本文得到了某些积分算子的带权向量值不等式,这类算子是将 R~n 上函数映到 R_+~(n+1)上函数,利用这些结果,可得到 Poisson 积分的带权向量值不等式.     

基于几何均值的惩罚型状态变权向量的构造及应用

依据惩罚型状态变权向量的定义和对各因素间的均衡性要求,构造了含有几何平均值的惩罚型状态变权向量.给出了各因素的几何平均值相等和几何平均值不相等两种情况的构造方法,对多因素的变权综合提供了有效的理论支持.最后,用一个实例验证了该方法的可行性和有效性.     

AHP判断矩阵权向量的改进最小二乘求解

提出了基于最小二乘法计算判断矩阵权向量的新方法.固定AHP判断矩阵权向量中的一个值为常量,利用判断矩阵的上三角部分元素,设计了一种计算判断矩阵权向量的新算法,算法简单,计算容易,与特征向量排序方法导出标度相同,并且能够证明存在唯一解.实验表明该算法具有有效性和可行性.     

一般线性Lie超代数的微分算子表示

对一般线性Lie超代数的Verma模及向量相干态(vector-coherent-states)表示给出了以向量为系数的微分算子实现.对一般线性Lie超代数的单一非典型(singlyatypical)Kac模,我们具体写出了本原权向量,并对这种情形确切写出了用以刻画向量相干态表示的单子表示的微分方程.     

衡量互反矩阵一致性的一种新指标

本文针对ＡＨＰ中利用几何平均值来估计各因素权向量的方法提出了与之匹配的一种新的衡量互反矩阵一致性的指标，并且制定了相应的衡量标准，同时将其与利用最大特征值所建立的指标入标准进行了比较与讨论。     

关于AHP群体判断矩阵权向量优化计算方法

本提出的AHP科体判断矩阵权向量优化计算方法．是运用非线性规划方法求解与各判断矩阵列向量偏角相差综合最小的向量,实例证明．该方法是简便有效的。     

Multiobjective Search Algorithm with Subdivision Technique

This paper presents a multiobjective search algorithm with subdivision technique (MOSAST) for the global solution of multiobjective constrained optimization problems with possibly noncontinuous objective or constraint functions. This method is based on a random search method and a new version of the Graef-Younes algorithm and it uses a subdivision technique. Numerical results are given for bicriterial test problems.     

Robustness to uncertain optimization using scalarization techniques and relations to multiobjective optimization

In this paper, we propose two kinds of robustness concepts by virtue of the scalarization techniques (Benson’s method and elastic constraint method) in multiobjective optimization, which can be characterized as special cases of a general non-linear scalarizing approach. Moreover, we introduce both constrained and unconstrained multiobjective optimization problems and discuss their relations to scalar robust optimization problems. Particularly, optimal solutions of scalar robust optimization problems are weakly efficient solutions for the unconstrained multiobjective optimization problem, and these solutions are efficient under uniqueness assumptions. Two examples are employed to illustrate those results. Finally, the connections between robustness concepts and risk measures in investment decision problems are also revealed.     

多目标对策的弱Pareto-Nash平衡点集的稳定性研究

通过定义多目标对策的加权Nash平衡点集,得出它和对应对策的弱Pareto-Nash平衡点集之间的关系,证明了在一定条件下的多目标对策的弱Pareto-Nash平衡点集的稳定性.     

On optimality conditions for multiobjective optimization problems in topological vector space

In this paper, we are concerned with a differentiable multiobjective programming problem in topological vector spaces. An alternative theorem for generalized K subconvexlike mappings is given. This permits the establishment of optimality conditions in this context: several generalized Fritz John conditions, in line to those in Hu and Ling [Y. Hu, C. Ling, The generalized optimality conditions of multiobjective programming problem in topological vector space, J. Math. Anal. Appl. 290 (2004) 363-372] are obtained and, in the presence of the generalized Slater's constraint qualification, the Karush-Kuhn-Tucker necessary optimality conditions.     

Vector Ordinal Optimization

Ordinal optimization is a tool to reduce the computational burden in simulation-based optimization problems. So far, the major effort in this field focuses on single-objective optimization. In this paper, we extend this to multiobjective optimization and develop vector ordinal optimization, which is different from the one introduced in Ref. 1. Alignment probability and ordered performance curve (OPC) are redefined for multiobjective optimization. Our results lead to quantifiable subset selection sizes in the multiobjective case, which supplies guidance in solving practical problems, as demonstrated by the examples in this paper.This paper was supported in part by Army Contract DAAD19-01-1-0610, AFOSR Contract F49620-01-1-0288, and a contract with United Technology Research Center (UTRC). The first author received additional funding from NSF of China Grants 60074012 and 60274011, Ministry of Education (China), and a Tsinghua University (Beijing, China) Fundamental Research Funding Grant, and the NCET program of China.The authors are grateful to and benefited from two rounds of reviews from three anonymous referees.     

Methods of variational analysis in multiobjective optimization

This article studies new applications of advanced methods of variational analysis and generalized differentiation to constrained problems of multiobjective/vector optimization. We pay most attention to general notions of optimal solutions for multiobjective problems that are induced by geometric concepts of extremality in variational analysis, while covering various notions of Pareto and other types of optimality/efficiency conventional in multiobjective optimization. Based on the extremal principles in variational analysis and on appropriate tools of generalized differentiation with well-developed calculus rules, we derive necessary optimality conditions for broad classes of constrained multiobjective problems in the framework of infinite-dimensional spaces. Applications of variational techniques in infinite dimensions require certain ‘normal compactness’ properties of sets and set-valued mappings, which play a crucial role in deriving the main results of this article.     

基于较优方案最大区别度的组合权重赋值法

现有多方案决策中的指标权重计算方法可能产生评价结果中较优方案不突出的难题,进而提出了一种新的方法——基于较优方案最大区别度的组合权重赋值法.方法可根据方案间区别度最大的原则将主观赋权法和客观赋权法中所确定的指标权重进行集结,综合考量待评方案,并建立多方案决策结果最大化和决策结果间方差最大化非线性优化模型,采用理想点法对具体的多目标规划问题进行求解.方法的运用有利于扩大备选方案之间的差距,进而突出较优方案,最后通过实例说明了该方法的优越性.     

优化和均衡的等价性

通过向量优化问题, 向量变分不等式问题以及向量变分原理来分析优化问题及均衡问题的一致性.从而显然, 可以用统一的观点来处理数值优化、向量优化以及博弈论等问题.进而为非线性分析提供了一个新的发展空间.     

Generalized asymptotic functions in nonconvex multiobjective optimization problems

In this paper, we use generalized asymptotic functions and second-order asymptotic cones to develop a general existence result for the nonemptiness of the proper efficient solution set and a sufficient condition for the domination property in nonconvex multiobjective optimization problems. A new necessary condition for a point to be efficient or weakly efficient solution is given without any convexity assumption. We also provide a finer outer estimate for the asymptotic cone of the weakly efficient solution set in the quasiconvex case. Finally, we apply our results to the linear fractional multiobjective optimization problem.     

Vector-valued lagrangian and multiobjective fractional programming duality

A class of multiobjective fractional programming problems is considered and duality results are established in terms of properly efficient solutions of the primal and dual programs. Further a vector-valued ratio type Lagrangian is introduced and certain vector saddlepoint results are presented.