DHT P2P网络中基于RDF的高效资源查询算法

P2P网络中资源的标注和查询是一个NP问题,本文提出了一种从RDF描述和查询的路径表达式中提取哈希串的算法,建立了从RDF描述和查询到DHTP2P路由空间的映射,从而在DHTP2P网络上实现了对以RDF描述的资源进行概念查询的支持,有效地改善了DHTP2P网络的查询处理能力。     

“应召”搜索问题的最优搜索路线

解决了确定性二次搜索（即“应召”搜索）问题的最优搜索路线。在搜索时间期望值最小、最远可能航程最短、最长可能时间最短三个准则下分别找出了相应的最优搜索路线,并从理论上严格证明了三者的统一性。这个结论在军事领域具有很重要的应用价值     

Schuster性质在循环图上的实现

第四届国际图论会议上提出的十大未解决问题之一:确定所有满足=G~2的图G,本文提供了一种在循环图上搜索具有=G~2性质的图G的方法,由此得到: 在C_n,在C_n中仅有C_(14)<1,4>和C_(14)<1,6>具有性质=G~2。     

多重因素约束下的网格搜索对策问题研究

首次基于搜索成本及搜索资源等限制因素,构造局中人面向多重约束条件的可行策略集合,建立相应的搜索空间;在给定搜索点权值的基础上,考虑搜索成本与搜索成功概率等因素,构造相应的支付函数,建立多重因素约束下的网格搜索对策模型.为简化模型求解,将对策论问题转化为约束最优化问题,求解约束问题获得最优值,转化为模型的对策值,并给出双方最优混合策略.最后,给出军事想定实例,说明上述模型的实用性及方法的有效性.     

二次图构形的边搜索法及其Orlik-Solomon代数的上同调

本文研究了一类特殊的图构形,即二次图构形.首先,给出了弦图的边搜索法,用边搜索法可以决定弦图所对应的超平面构形中超平面的哪些排序使构形是二次均.其次,对二次图构形的Orlik-Solomon代数作为线性空间的维数进行了计算,得到了它的Poincare多项式的表达式.最后,对二次图构形Orlik-Solomon代数的上同调进行了研究,对边缘算子集中在秩为二的模元时,得到了二次图构形的Orlik-Solomon代数的上同调的维数计算公式.     

最佳的潜艇搜索模型

对直线式逃跑的潜艇被捕获的必然性进行了证明,并为驱逐舰给出了一个最佳的搜索模型.     

多目标线性规划的一种交互式单纯形算法

本文基于分析有效极点解的有效变量的特点以及在有效点处各个目标函数的数值来得到改进的搜索方向的研究思想,提出了求解目标函数和约束均为线性的多目标线性规划问题的一种交互式算法。该方法可以保证每一步得到的解均为有效极点解,且根据决策者的偏好不断得到改进,直至最终得到满意的最终解。     

地面搜索优化方案的模型建立

对一块长为11200 m,宽为7200m的平地矩形区域,运用最优化数学理论,寻求最佳搜索路径.若20人一组搜索,运用整数规划方法建立模型并求解出最短搜索时间为49.03 h;若要在48 h内完成搜索,运用线性规划方法改进模型并求得最少需22人.     

交互式数学课堂教学的功能

教学必然产生一定的功能,对交互式数学课堂教学的功能进行探讨,可以帮助我们认识交互式数学课堂教学的可能性和价值.教学的功能是指由一定教学结构决定的,在一定环境条件下的教学可能发挥的作用和能力[1].影响教学功能发挥的因素是外部的环境条件,决定教学功能性质的是教学自身的结构.因此,分析交互式数学课堂教学的功能,既必须认识交互式数学课堂教学功能释放的外部环境,也必须了解交互式数学课堂教学的结构(关于交互式数学课堂教学的基本要素和结构另文单独研究).     

基于遗传算法的对规避目标搜索模型

分析了在应召条件下对规避目标搜索行动的特点,然后采用遗传算法建立了可用于辅助搜索决策者制定协同搜索方案的模型,为分析应召搜索提供了新的方法,该方法克服了传统的运筹学搜索论在协同行动等复杂条件下寻求最优搜索方案的不足     

An Interactive Method for 0-1 Multiobjective Problems Using Simulated Annealing and Tabu Search

This paper presents an interactive method for solving general 0-1 multiobjective linear programs using Simulated Annealing and Tabu Search. The interactive protocol with the decision maker is based on the specification of reservation levels for the objective function values. These reservation levels narrow the scope of the search in each interaction in order to identify regions of major interest to the decision maker. Metaheuristic approaches are used to generate potentially nondominated solutions in the computational phases. Generic versions of Simulated Annealing and Tabu Search for 0-1 single objective linear problems were developed which include a general routine for repairing unfeasible solutions. This routine improves significantly the results of single objective problems and, consequently, the quality of the potentially nondominated solutions generated for the multiobjective problems. Computational results and examples are presented.     

A General Approach for Constraint Solving by Local Search

In this paper, we present a general approach for solving constraint problems by local search. The proposed approach is based on a set of high-level constraint primitives motivated by constraint programming systems. These constraints constitute the basic bricks to formulate a given combinatorial problem. A tabu search engine ensures the resolution of the problem so formulated. Experimental results are shown to validate the proposed approach. This revised version was published online in August 2006 with corrections to the Cover Date.     

Interactive multidimensional programming models for locational decisions

The paper is devoted to an extension of traditional location theory in two directions. First, the usual assumption of a single cost function will be abandoned by introducing multiple objectives. This gives rise to a multidimensional programming framework for the traditional location models. The paper provides a solution algorithm for the latter problem.Next, the assumption of a uniform space will be tackled by taking account of discrete candidate-locations. This problem can be solved by means of an adjusted multiciteria analysis.The solution algorithms for both extensions are based on an interactive strategy, so that the decision-maker may identify the most favourable location in a finite number of steps.     

Cooperative Parallel Tabu Search for Capacitated Network Design

We present a cooperative parallel tabu search method for the fixed charge, capacitated, multicommodity network design problem. Several communication strategies are analyzed and compared. The resulting parallel procedure displays excellent performances in terms of solution quality and solution times. The experiments show that parallel implementations find better solutions than sequential ones. They also show that, when properly designed and implemented, cooperative search outperforms independent search strategies, at least on the class of problems of interest here.     

A Filter-Based Pattern Search Method for Unconstrained Optimization

We discuss a filter-based pattern search method for unconstrained optimization in this paper. For the purpose to broaden the search range we use both filter technique and frames, which are fragments of grids, to provide a new criterion of iterate acceptance. The convergence can be ensured under some conditions. The numerical result shows that this method is practical and efficient.     

Global Optimization by Multilevel Coordinate Search

Inspired by a method by Jones et al. (1993), we present a global optimization algorithm based on multilevel coordinate search. It is guaranteed to converge if the function is continuous in the neighborhood of a global minimizer. By starting a local search from certain good points, an improved convergence result is obtained. We discuss implementation details and give some numerical results.     

Improved Local Search for CP Toolkits

Constraint programming and local search are two well known optimization technologies. In recent years, methods for combining these two technologies have been put forward, one of which advocates the use of constraint programming for searching the local neighborhood of the current solution. We present a search technique which improves on the performance of this constraint programming-based local search method and perform experiments on a variety of both simple and more complex combinatorial problems. We also demonstrate the benefit of combining local and complete search methods.     

Recovering Beam Search: Enhancing the Beam Search Approach for Combinatorial Optimization Problems

A hybrid heuristic method for combinatorial optimization problems is proposed that combines different classical techniques such as tree search procedures, bounding schemes and local search. The proposed method enhances the classic beam search approach by applying to each partial solution corresponding to a node selected by the beam, a further test that checks whether the current partial solution is dominated by another partial solution at the same level of the search tree. If this is the case, the latter solution becomes the new current partial solution. This step allows to partially recover from previous wrong decisions of the beam search procedure and can be seen as a local search step on the partial solution. We present here the application to two well known combinatorial optimization problems: the two-machine total completion time flow shop scheduling problem and the uncapacitated p-median location problem. In both cases the method strongly improves the performances with respect to the basic beam search approach and is competitive with the state of the art heuristics.     

Scatter Search for Network Design Problem

A fixed charge capacitated multicommodity network design problem on undirected networks is addressed. At the present time, there exists no algorithm that can solve large instances, common in several applications, in a reasonable period of time. This paper presents an efficient procedure using a scatter search framework. Computational experiments on a large set of randomly generated problems show that this procedure is capable of finding good solutions to large-scale problems within a reasonable amount of time.