基于线性约束条件下全局优化问题的定边界模拟退火算法

针对线性约束条件下全局优化问题,提出了定边界模拟退火算法(DBSA,参数确定范围[0,1])。应用凸集理论,将线性约束条件转化为和为1的等式约束,可证明二若等价。针对新的约束条件,对模拟退火算法可能解的选取方法进行相应改进。其次从应用上验证了DBSA的可行性,对比结果表明定边界模拟退火算法具有较快的运算速度和精度。     

Reformulation of Global Constraints Based on Constraints Checkers

This article deals with global constraints for which the set of solutions can be recognized by an extended finite automaton whose size is bounded by a polynomial in n, where n is the number of variables of the corresponding global constraint. By reducing the automaton to a conjunction of signature and transition constraints we show how to systematically obtain an automaton reformulation. Under some restrictions on the signature and transition constraints, this reformulation maintains arc-consistency. An implementation based on some constraints as well as on the metaprogramming facilities of SICStus Prolog is available. For a restricted class of automata we provide an automaton reformulation for the relaxed case, where the violation cost is the minimum number of variables to unassign in order to get back to a solution.     

遴选算法全局条件解耦与物理优化实现

针对遴选算法处理信息量大、条件复杂、运行时间长等特性,研究算法准并行化的方法,运用全局约束量主动推送和全局记录加锁策略对全局条件进行解耦,在此基础上建立一种基于客户端/服务器模式的多线程算法结构,并对2种解决并行同步问题的加锁策略进行比较分析.实验结果表明,优化实现后算法的运行速度有明显提升.     

Cost-Based Arc Consistency for Global Cardinality Constraints

A global cardinality constraint (gcc) is specified in terms of a set of variables X={x ₁,...,x _p} which take their values in a subset of V={v ₁,...,v _d}. It constrains the number of times each value v _iV is assigned to a variable in X to be in an interval [l _i,u _i]. A gcc with costs (costgcc) is a generalization of a gcc in which a cost is associated with each value of each variable. Then, each solution of the underlying gcc is associated with a global cost equal to the sum of the costs associated with the assigned values of the solution. A costgcc constrains the global cost to be less than a given value. Cardinality constraints with costs have proved very useful in many real-life problems, such as traveling salesman problems, scheduling, rostering, or resource allocation. For instance, they are useful for expressing preferences or for defining constraints such as a constraint on the sum of all different variables. In this paper, we present an efficient way of implementing arc consistency for a costgcc. We also study the incremental behavior of the proposed algorithm.     

求解全局优化问题的混合智能算法

把序列二次规划作为遗传算法的一个局部搜索算子,嵌入到实数编码遗传算法中,构成一种基于序列二次规划和实数编码遗传算法的高效的混合智能算法。该方法充分利用序列二次规划法的强局部搜索能力和遗传算法的全局收敛性,使得混合算法的全局收敛性得到改善并且减少了计算量。数值实验结果表明,混合算法是高效可靠的。     

Cost-based Filtering for Shorter Path Constraints

Many real world problems, e.g. personnel scheduling and transportation planning, can be modeled naturally as Constrained Shortest Path Problems (CSPPs), i.e., as Shortest Path Problems with additional constraints. A well studied problem in this class is the Resource Constrained Shortest Path Problem. Reduction techniques are vital ingredients of solvers for the CSPP, that is frequently NP-hard, depending on the nature of the additional constraints. Viewed as heuristics, these techniques have not been studied theoretically with respect to their efficiency, i.e., with respect to the relation of filtering power and running time. Using the concepts of Constraint Programming, we provide a theoretical study of cost-based filtering for shorter path constraints on acyclic, on undirected, and on directed graphs that do not contain negative cycles. We then show empirically how reasoning about path-substructures in combination with CP-based Lagrangian relaxation can help to improve significantly over previously developed problem-tailored filtering algorithms for the resource constrained shortest path problem and investigate the impact of required-edge detection, undirected versus directed filtering, and the choice of the algorithm optimizing the Lagrangian dual.     

二次背包问题的一种快速解法

在分析了二次背包问题(QKP)精确算法的计算效率随利润矩阵密度下降的原因的基础上,提出了不受密度影响的QKP快速解法——利润欺骗法．在线性化QKP的目标上界估计中,利润欺骗法通过引进一适当正常数对称扩展Lagrangian乘子的变化范围,亚梯度优化算法能较快地找到-Lagrangian乘子矩阵,使对偶问题的解逼近线性化QKP问题的等式约束条件．通过提高目标函数的估计精度,利润欺骗法可以提高变量约简效率,降低分支决策深度．实例计算表明,快速算法的效率远高于精确算法,而且计算精度并不降低．     

An Efficient Bounds Consistency Algorithm for the Global Cardinality Constraint

Previous studies have demonstrated that designing special purpose constraint propagators can significantly improve the efficiency of a constraint programming approach. In this paper we present an efficient algorithm for bounds consistency propagation of the generalized cardinality constraint (gcc). Using a variety of benchmark and random problems, we show that on some problems our bounds consistency algorithm can dramatically outperform existing state-of-the-art commercial implementations of constraint propagators for the gcc. We also present a new algorithm for domain consistency propagation of the gcc which improves on the worst-case performance of the best previous algorithm for problems that occur often in applications.     

基于序列二次规划的非线性不等式状态约束滤波算法

针对非线性不等式状态约束滤波问题,提出一种基于序列二次规划的迭代不敏卡尔曼滤波算法。在迭代不敏卡尔曼滤波的基础上,采用序列二次规划优化法求解非线性不等式约束条件下的最优解。通过对每一次迭代求解二次规划子问题来确定下降方向,重复该步骤直到求得原问题的解,利用效益函数对目标函数最小化和不等式约束条件进行权衡,以保证算法的收敛性,利用正定矩阵近似海森矩阵降低时间复杂度。对具有约束的航路跟踪系统进行实验仿真,结果表明,该算法在处理非线性不等式状态约束滤波问题时,能够有效地提高状态估计精度,获得较高的滤波精度,且时间复杂度较低。     

二次分配问题的粒子群算法求解

文章采用了一种新的算法,即粒子群算法(PSO)去解决二次分配问题(QAP),构造了该问题的粒子表达方法,建立了此问题的粒子群算法模型,并对不同的二次分配问题算例进行了实验,结果表明:粒子群算法可以快速、有效地求得二次分配问题的优化解,是求解二次分配问题的一个较好方案。PSO算法在很多连续优化问题中已经得到较成功的应用,而在离散域上的研究和应用还很少。文章应用PSO算法解决QAP问题是一种崭新的尝试,它对于将PSO算法应用于离散问题,特别是组合优化问题无疑具有启发性,并为进一步深入研究奠定了基础。     

一种基于二次互信息的双聚类算法

双聚类模型有助于聚类存在相关性的局部模式。论文提出了一种可识别多种相关模式的双聚类算法,以二次互信息作为相关性标准,并以Parzen窗口法有效估算高维变量之间的互信息;同时提出了最大相关维簇的概念。算法以多个最大相关维簇为种子,通过迭代细化聚类,可有效地发现高维数据环境内相关的长模式。真实基因表达数据的实验证明了算法的有效性。     

融合环境信息和运动约束的改进A*算法研究

标准A*算法存在着无法考虑移动机器人运动特性及处理后的路径不利于移动机器人运动等问题。针对这一问题提出了一种新改进A*算法,通过环境信息引入障碍物权重系数来改进算法的启发函数并进行全局路径规划;优化搜索节点的选取方式和设定障碍物与路径之间的安全距离;基于对移动机器人的运动特性的考虑优化其路径,并在不同环境地图中与其他算法进行仿真实验对比分析。相关实验表明：基于新改进A*算法规划的路径始终与障碍物保持一定的安全距离;改进A*算法在时间上相比标准A*算法平均减少了80%,路径长度平均减少了2%,路径转角平均降低了82%。改进后算法相比其他算法在时间、搜索节点以及平滑度上有很大的改进,融合机器人环境信息和运动特性的规划路径算法可为移动机器人的路径规划提供一种新的方法。     

全局优化的混合禁忌搜索算法

针对多峰函数的全局优化问题,提出了混合禁忌搜索的全局优化方法,通过实例验证了所提出的算法的可行性、有效性并且收敛速度较快.     

速度自适应约束卡尔曼滤波方法

为解决动态目标定位过程中使用传统卡尔曼滤波算法不能利用先验信息提高定位精度、而使用速度约束卡尔曼滤波算法易发散等问题,论文提出一种基于速度先验信息自适应约束卡尔曼滤波方法。该方法以速度观测值和定速误差统计特性建立变异系数,实时判断速度观测值是否满足约束滤波的要求,从而自适应地在速度约束滤波和传统卡尔曼滤波间选择。实验结果表明,与传统卡尔曼滤波和速度约束滤波相比,本文算法的定位精度提高了cm级至m级,既克服了速度约束卡尔曼滤波易受定速误差影响的缺点,也能利用合理的速度观测值来提高定位精度。     

DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints

Recently, constraint-based mining of itemsets for questions like find all frequent itemsets whose total price is at least $50 has attracted much attention. Two classes of constraints, monotone and antimonotone, have been very useful in this area. There exist algorithms that efficiently take advantage of either one of these two classes, but no previous algorithms can efficiently handle both types of constraints simultaneously. In this paper, we present DualMiner, the first algorithm that efficiently prunes its search space using both monotone and antimonotone constraints. We complement a theoretical analysis and proof of correctness of DualMiner with an experimental study that shows the efficacy of DualMiner compared to previous work.     

一种IP与ATM网络基于多服务质量约束的路由算法

1 引言随着多媒体技术的飞速发展,网络上诸如数字视频和音频的各种多媒体应用通常都有严格的服务质量(QoS)要求。网络为了提供性能保证,只有采用资源预留和实行网络控制。近年来在ATM与Internet上的QoS要求己受到人们的极大重视。传统的数据传输网中路由选择主要与连通性有关。各种路由协议通常用诸如节点计数或延迟等单一的度量(metric)来表示网络的特性,并用最短路径算法来进行路由计算。为了支持广泛的QoS要求,这些路由协议需要有更复杂的模型,用诸如开销(cost)、延迟、延迟变量、丢失概率和带宽等多度量来表示网络的特性。QoS路由寻址的基本问题是找一条满足一种或多种QoS约束条件、具有最小开销(或者最短距离)的     

Maintaining Global Integrity Constraints in Distributed Databases

Given some integrity constraints over a distributed database, we consider the problem of incrementally checking global consistency in response to updates made to the base relations but without accessing all these base relations. In many application areas such as collaborative design, mobile computing and enterprise information systems, total data availability cannot be assumed. Even if all the base data is available, some of it may incur such a high cost that its use should only be considered as a last resort. Without looking at all the relations that participate in the constraint, how can one meaningfully check a constraint for violation? When the constraint is known to be satisfied prior to the update, the state of the relations that are available (aka local) can in principle be used to infer something about the relations that are not available (aka remote). This observation is the basis for the existence of tests that guarantee that global consistency is preserved under a given update, without looking at all the base data. In order to make consistency maintenance practical, the challenge is to find those tests that are most general (we call Complete Local Tests) and that are efficient to generate and execute. This paper addresses the problem of finding efficient complete local tests for an important class of constraints that are very common in practice: constraints expressible as conjunctive queries with negated subgoals. For constraints where the predicates for the remote relations do not occur more than once, we present complete local tests under insertions and deletions to the local relations. These tests can be expressed as safe, nonrecursive Datalog ¬ queries against the local relations. These results also apply to other constraints with negation that are not conjunctive.     

一种新的移动机器人全局路径规划算法

提出了一种新的移动机器人全局路径规划算法．该算法不需要对环境中的障碍物特征做任何假设,也不需要建立障碍物的连通图模型,有效地克服了传统路径规划算法因为搜索而带来的计算复杂性问题,提高了算法的适应性和实时性．仿真结果证明了算法的有效性．     

二次插值的粒子群优化算法

为了克服粒子群优化算法容易早熟的问题,提出了一种新的粒子群优化算法。算法在进行速度和位置更新后,随机选取两个个体历史最好位置（不含全局最好位置）与全局最好位置,利用二次插值产生新的位置,并与当前个体历史最好位置相比较,更新当前个体历史最好位置和全局历史最好位置。对6个经典测试函数进行数值实验,结果表明该算法提高了算法的寻优能力和收敛速度。