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。     

一种带二次等式约束的卡尔曼滤波目标跟踪算法

目标跟踪是无线传感器网络的重要应用之一.研究目标运动轨迹满足一个二次等式约束(quadraticequality constraint)的目标跟踪问题.在实际应用中,当飞行器进行盘旋或者车辆沿弯道行使时,其轨迹均近似满足一个二次等式约束.考虑在卡尔曼滤波(Kalman filtering,简称KF)算法中引入二次等式约束以提高目标跟踪精度.所提出的算法在每个采样时刻首先利用新获取的观测量和无约束卡尔曼滤波算法更新目标运动状态估计,然后利用带二次等式约束的极大似然估计(maximum likelihood estimator,简称MLE)修正目标运动状态估计.在求解约束极大似然问题时,将其看作一类广义信赖域子问题(generalized trust region sub-problem,简称GTRS),以获得全局最优解.仿真结果表明,该算法与现有带二次等式约束的卡尔曼滤波算法相比具有更高的跟踪精度.     

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.     

离散时间系统的鲁棒稳定性分析--积分二次约束方法

利用新近发展起来的积分二次约束（ＩＱＣ）方法,对离散时间系统,给出了其鲁棒稳定性分析准则,使一大类古典稳定性结果可以统一和概括在积分二次约束的框架之下。并且,利用积分二次约束方法,讨论了一类混合摄动系统的鲁棒稳定性。     

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.     

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.     

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。     

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.     

求解线性约束二次优化问题的神经计算模型

本文提出了一种求解线性约束二次优化问题的神经模型 ,研究了该神经网络的稳定性和收敛性 ,给出了电路框图 ,并通过算例证明了该神经网络的可行性。     

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。     

带非线性约束的自适应高斯和卡尔曼滤波目标跟踪算法

无线传感网络中运动目标状态通常满足某种非线性状态约束,为了提高对传感网络中运动目标的跟踪精度,降低非高斯噪声对状态估计的影响,避免高斯项数在迭代过程中的冗余累积,提出一种带非线性约束的权值自适应高斯和卡尔曼滤波算法.算法在每个时刻计算目标当前状态的高斯子项集合,并对每个高斯子项分别以无迹卡尔曼滤波进行状态估计.设计了一种高斯子项权值自适应策略动态调节子项权值,以实现无约束状态下的全局估计.将目标的非线性状态约束引入滤波器结构中时,考虑将其看作一类无约束状态估计的约束投影问题,通过状态约束信息先验来修正运动目标的状态估计.仿真结果表明,该算法与目前的非线性约束卡尔曼滤波相比具有更高的跟踪精度.     

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.