Petri网模型与程序流程图的比较及应用研究

petri网是一种描述具有分布、并发、异步特点的信息流的系统模型，巳得到了广泛的应用；程序流程图是依据算法描述问题求解过程的图解表示，是使用数字计算机求解问题的必要步骤。如何进行相互转换是应用中所关心的一个突出问题。文中对二者先做形式上的比较研究，分析它们的特点和异同，从中找出一些规律，用以归纳相互转换的方法要点。     

一种求解图着色问题的优化组合遗传算法①

图着色算法是一种典型的NP-完全问题。在逆序算子、对偶算子和矩阵遗传算子的性能研究基础上,采用自然数与二进制相互转换的编码方案,应用图着色问题的约束条件建立适应度评价函数,将具有良好局部搜索性能的矩阵遗传算子与具有良好局部搜索性能的逆序与对偶组合算子优化组合应用,构造了一种用于求解图着色问题的优化组合遗传算法,保证了算法的全局收敛性。与基本遗传算法相比较,实验结果表明,该算法对图着色问题有较好的求解性能。     

一种基于ORM的OWL本体与关系数据库映射模型

如何使现有的关系数据库具有语义特征,如何对面向对象中的对象模型进行语义描述,是语义网应用过程中的两个重要问题。提出了一种Ontology-Object-Relational映射模型,提出了相关的映射规则及算法,基于映射元数据实现了本本、对象以及关系数据库间的映射与相互转换。实现了基于该模型的实验系统,并对关系数据库与本体实例间的相互转换进行了实验,实验结果表明本文所提出的映射模型、映射规则及算法是正确与可行的。     

蚁群算法求解旅行商问题综述*

蚁群优化是一种元启发式的随机搜索技术,是目前解决组合优化问题最有效的工具之一。旅行商问题（TSP）是一个典型的组合优化问题,易于描述却难于求解。在介绍了求解旅行商问题的三种经典的蚁群算法的基本原理后,着重分析了蚁群算法的发展现状,总结出蚁群算法发展的五个方向,即基于局部优化算法的蚁群算法、对路径上的信息素更新方法进行改进、蚁群算法与其他算法的融合、对蚁群算法的控制参数进行优化和并行蚁群算法。而且这五个方向有相互融合的趋势。     

突发灾害下可靠路径搜索模型与算法

在分析突发灾害爆发时可靠路径搜索问题特点的基础上,提出了一种在不确定网络中不依赖于弧的旅行时间概率分布的可靠路径搜索方法。该方法通过场景集描述网络旅行时间的不确定性,应用Minimax理论构建求解所有场景下可靠路径的数学模型,并设计了问题求解算法,分析了算法的时间复杂性,最后通过典型算例对算法进行了验证。     

基于整数规划的层次式FPGA布线算法

为了避免由于布线线序处理不当而导致无法布通的问题,提出一种基于整数规划的层次式FPGA布线算法.该算法使用一种全局优化处理的方式对布线问题进行求解,通过分析层次式FPGA的结构特点和整数规划的算法特点,导出了FPGA布线算法问题与整数规划之间的关系;然后具体描述了如何将FPGA布线问题转化成二进制整数规划问题及其相应的求解过程,其中利用层次式FPGA的结构特点对得到的整数规划问题进行简化.与可满足性布线算法进行实验比较的结果表明,文中算法具有求解速度更快、求解规模更大以及求解质量更高等方面的优势.     

基于TSP的蚁群算法研究

介绍蚁群算法的研究现状并对蚁群算法的逻辑结构进行分析,根据旅行商问题的描述,建立求解TSP的Ant Cycle蚁群算法模型,对该算法的步骤进行描述以及实现,对该算法复杂度进行分析研究,并对该算法的特点作以总结.     

启发式算法求解最大团问题研究

最大团问题(maximum clique problem,MCP)是图论中的一个经典组合优化问题,也是一类NP完全问题,在国际上已有广泛地研究,国内研究刚刚起步.给出了最大团问题的基本定义和其数学描述;阐述了该问题的研究进展;分析和研究了求解该问题的各种典型启发式算法,包括算法的介绍、算法求解最大团问题的基本思路、特点及性能;最后介绍了测试这些启发式算法性能的测试基准图.     

联盟运输调度问题模型结构与算法研究

联盟运输调度问题是在基本运输调度问题基础上衍生出的最具现实意义的一类组合优化难题,是近年来物流控制优化领域的研究热点。依据运输调度问题分类方法,描述了联盟运输调度问题的结构;通过分析遗传算法、模拟退火算法、禁忌搜索算法、蚁群算法、粒子群算法的特点及其求解运输调度问题的现状,讨论了它们求解联盟运输调度问题的可能性;展望了联盟运输调度问题发展的前景,指出改进原算法、提出新算法、并行算法是解决联盟运输调度问题的重要手段。     

基于Graphplan的ARBAC策略安全分析方法

策略安全分析是访问控制系统保持安全状态的重要机制.针对具有角色继承层次和角色静态互斥特征的分布式访问控制系统,文中采用智能规划技术进行策略安全分析.首先,提出了策略安全分析问题向规划问题转换的整体思路,定义"虚动作"模型以描述角色继承关系,使用领域互斥表述静态互斥角色,引入领域公理处理ARBAC策略的开放世界假设问题和前提条件中的负谓词问题.其后,运用图规划(Graphplan)算法求解转换而来的规划问题,重点分析了领域公理对规划图中部分NooP动作的剪枝作用,提出了领域公理在规划图扩展阶段的应用方式以及据此改进的图规划算法,介绍了已开发的面向ARBAC策略安全分析实验型规划系统.最后,进行了应用示例说明.     

Collocation Methods for General Caputo Two-Point Boundary Value Problems

A general class of two-point boundary value problems involving Caputo fractional-order derivatives is considered. Such problems have been solved numerically in recent papers by Pedas and Tamme, and by Kopteva and Stynes, by transforming them to integral equations then solving these by piecewise-polynomial collocation. Here a general theory for this approach is developed, which encompasses the use of a variety of transformations to Volterra integral equations of the second kind. These integral equations have kernels comprising a sum of weakly singular terms; the general structure of solutions to such problems is analysed fully. Then a piecewise-polynomial collocation method for their solution is investigated and its convergence properties are derived, for both the basic collocation method and its iterated variant. From these results, an optimal choice can be made for the transformation to use in any given problem. Numerical results show that our theoretical convergence bounds are often sharp.     

Dynamic Programming via Static Incrementalization

Dynamic programming is an important algorithm design technique. It is used for problems whose solutions involve recursively solving subproblems that share subsubproblems. While a straightforward recursive program solves common subsubproblems repeatedly, a dynamic programming algorithm solves every subsubproblem just once, saves the result, and reuses it when the subsubproblem is encountered again. This can reduce the time complexity from exponential to polynomial. This paper describes a systematic method for transforming programs written as straightforward recursions into programs that use dynamic programming. The method extends the original program to cache all possibly computed values, incrementalizes the extended program with respect to an input increment to use and maintain all cached results, prunes out cached results that are not used in the incremental computation, and uses the resulting incremental program to form an optimized new program. Incrementalization statically exploits semantics of both control structures and data structures and maintains as invariants equalities characterizing cached results. It provides the basis of a general method for achieving drastic program speedups. Compared with previous methods that perform memoization or tabulation, the method based on incrementalization is more powerful and systematic. It has been implemented in a prototype system CACHET and applied to numerous problems and succeeded on all of them.     

旅行商问题优化解之间关系的分析

旅行商问题是经典的组合优化NP难题之一,学术界一直致力于建立在合理的计算时间内精确或近似求解问题的算法.近似算法常求得的高质量近似优化解与全局最优解之间边交集不为空,建立了两者之间及与全局最优解之间的特定关系,通过数学分析建立量化关系模型,利用实验确立模型中相关参数的先验概率.据此建立的随机TSP裁减过程大幅度裁减问题的求解规模;在求解过程中亦能高概率确定属于全局最优解的边,以提高问题求解效率和质量.     

小容量网络上的最大流算法

最大流问题是一类经典的组合优化问题。描述了一种小容量网络,这种网络有强的实际应用背景,同时给出了专门解这种网络上最大流问题的算法。该算法比通用的算法快。它已经突破了最大流问题的O（mn)时间障碍,具有较强的理论意义,也为解决许多实际应用问题提供了更有效的算法。同时,由于判断一个网络是否为小容量网络非常简单,因此该算法也具有普遍意义。     

Nogood-FC for solving partitionable constraint satisfaction problems

Many real problems can be naturally modelled as constraint satisfaction problems (CSPs). However, some of these problems are of a distributed nature, which requires problems of this kind to be modelled as distributed constraint satisfaction problems (DCSPs). In this work, we present a distributed model for solving CSPs. Our technique carries out a partition over the constraint network using a graph partitioning software; after partitioning, each sub-CSP is arranged into a DFS-tree CSP structure that is used as a hierarchy of communication by our distributed algorithm. We show that our distributed algorithm outperforms well-known centralized algorithms solving partitionable CSPs.     

求解多目标优化问题的改进蚁群算法

蚁群算法是一种模拟蚂蚁行为进行优化的启发式优化算法,该算法在许多领域已经得到应用.针对多目标优化问题优化与求解较困难的问题,提出一种嵌入变尺度算法的改进蚁群算法用于求解,为蚁群算法在连续空间中的应用提供了怂一个可行的方案.给出了该算法的详细定义及实现步骤,实例仿真表明,该算法能加快收敛速率,对连续空间的蚁群算法研究具有重要的意义.     

多智能体分层协作规划及在RoboCup中的应用

为了更好地解决一类通讯受限环境中多智能体任务协作规划问题,提出了基于MAXQ-OP的多智能体在线规划方法,并在RoboCup仿真2D足球比赛的人墙站位和多球员传球问题中对算法进行了实验.实验结果表明,这个方法使智能体在需要协作配合的环境中的表现比传统方法有了明显提升.     

一类非线性递推的变距二分算法

提出了一种有效求解一类非线性递推问题的并行算法。比较求解此类问题的奇偶二分法，变距二分算法显著节省了总运算量；比较约简二分法，变距二分算法仅含消元过程，算法结构简单。     

蚁群算法及其在求解旅行商问题中的应用

蚁群算法是一种求解组合优化问题较好的方法。在蚁群算法的基本原理基础上,以旅行商问题为例,介绍了该算法求解TSP的数学模型及具体步骤,并通过仿真实验与粒子群优化算法等方法比较分析,表明了该算法在求解组合优化问题方面具有良好的性能。     

On Optimization and Parallelization of the Little Algorithm for Solving the Travelling Salesman Problem

The paper describes some ways to speed up solution of the NP-complete Traveling Salesman Problem. The classic Little algorithm, belonging to the class of branch-and-bound methods, can solve it both for directed and undirected graphs. For undirected graphs, however, its speed can be increased by eliminating the branches examined earlier from further consideration. We propose changes to be made in the key operations of the algorithm to speed up execution. In addition, we describe the results of an experiment with a significant increase in the speed of solving of the problem by the advanced algorithm. Another way to speed up the solution procedure is to parallelize the algorithm. For problems of this kind, it is difficult to decompose the task into a sufficient number of subtasks that have comparable complexity. Their parallelism arises dynamically during the execution. For such problems, it seems reasonable to use parallel-recursive algorithms. In our case, the RPM_ParLib library developed by the author is a good approach, enabling the development of high-performance applications for parallel computing on a local network using any.NET-compatible programming language. We selected C# as the programming language. Parallel applications were developed to implement the basic and modified algorithms, as well as to compare them in terms of speed. Experiments were performed for the graphs with up to 45 vertexes and up to 16 network computers. We also investigated the speed increase that can be achieved by parallelizing the basic Little algorithm for directed graphs. The results of these experiments are also presented in the paper.