基于时间窗的随机时变交通网络信号相位协调

交通网络是随机时变网络,用周期性时间窗模拟各路口信号灯控制,建立交通网络中路口相位差协调控制模型。时间窗的设定使只有规定行驶方向的车辆可以通行路口,其他车辆不可通行。为得到车辆在路口前等待状况,定义时间窗函数,该函数采用协调交通网络路口信号相位差的方法求得随机时变网络的最短期望路径。结合改进的SDOT算法和穷举法及遗传算法设计一种混合算法。对一个四路口小型交通网络进行了仿真研究,结果验证了求解算法的有效性。     

Variable neighborhood search for the cost constrained minimum label spanning tree and label constrained minimum spanning tree problems

Given an undirected graph whose edges are labeled or colored, edge weights indicating the cost of an edge, and a positive budget B, the goal of the cost constrained minimum label spanning tree (CCMLST) problem is to find a spanning tree that uses the minimum number of labels while ensuring its cost does not exceed B. The label constrained minimum spanning tree (LCMST) problem is closely related to the CCMLST problem. Here, we are given a threshold K on the number of labels. The goal is to find a minimum weight spanning tree that uses at most K distinct labels. Both of these problems are motivated from the design of telecommunication networks and are known to be NP-complete [15].In this paper, we present a variable neighborhood search (VNS) algorithm for the CCMLST problem. The VNS algorithm uses neighborhoods defined on the labels. We also adapt the VNS algorithm to the LCMST problem. We then test the VNS algorithm on existing data sets as well as a large-scale dataset based on TSPLIB [12] instances ranging in size from 500 to 1000 nodes. For the LCMST problem, we compare the VNS procedure to a genetic algorithm (GA) and two local search procedures suggested in [15]. For the CCMLST problem, the procedures suggested in [15] can be applied by means of a binary search procedure. Consequently, we compared our VNS algorithm to the GA and two local search procedures suggested in [15]. The overall results demonstrate that the proposed VNS algorithm is of high quality and computes solutions rapidly. On our test datasets, it obtains the optimal solution in all instances for which the optimal solution is known. Further, it significantly outperforms the GA and two local search procedures described in [15].     

利用Hash树生成频繁项目集的新方法

频繁项目集的生成是关联规则挖掘中的关键问题 .提出基于 Hash树的频繁项目集生成新方法 ,探讨了 Hash树中候选项目集的数据组织与建立算法 ,提出了利用 Hash树计算候选项目集支持数的算法 ,并用 Java语言实现了该算法 ,最后通过实验验证了利用 Hash树生成频繁项目集的有效性     

结合变邻域搜索的竞争Hopfield神经网络解决最大分散度问题

提出一种结合变邻域搜索的离散竞争Hopfield神经网络,用于求解最大分散度问题。为了克服神经网络易陷入局部最小值的问题,将变邻域搜索的思想引入到离散竞争Hopfield神经网络中,一旦网络陷入局部最小值,变邻域搜索能帮助神经网络动态改变搜索邻域,从而跳出局部最小值去搜寻更优的解。最后,针对最大分散度问题的实验结果表明,提出的算法具有良好的性能。     

一种基于频繁模式树的约束最大频繁项目集挖掘及其更新算法

目前已提出了许多快速的关联规则挖掘算法,实际上用户只关心部分关联规则,如他们仅想知道包含指定项目的规则．当这些约束被用于数据预处理或将它结合到数据挖掘算法中去时,可以显著减少算法的执行时间．为此,考虑了一类包含或不包含某些项目的布尔表达式约束条件,提出了一种快速的基于FP—tree的约束最大频繁项目集挖掘算法CMFIMA,并对其更新问题进行了研究,提出了一种增量式更新约束最大频繁项目集挖掘算法CMFIUA．     

频繁序列模式挖掘中关键技术的研究

如何确定候选频繁序列模式以及如何计算它们的支持数是序列模式挖掘中的两个关键问题。该文提出了一种基于二进制形式的候选频繁序列模式生成和相应的支持数计算方法，该方法只需对挖掘对象进行一些“或”、“与”、“异或”等逻辑运算操作，显著降低了算法的实现难度，将该方法与频繁序列模式挖掘及更新算法相结合，可以进一步提高算法的执行效率。     

矩阵约束下的频繁项集挖掘方法研究

数据挖掘中的关联分析技术旨在发现大量数据项集之间有趣的关联关系,其核心问题是寻找频繁项集。针对传统的基于矩阵的关联挖掘算法中矩阵规模和事务数据库大小相关,在处理超大型事务数据库时,仍会存在内存瓶颈的问题,提出了一个矩阵规模和事务数据库大小无关、通过矩阵约束预挖掘后验证的频繁项集发现算法。实验结果显示,该算法提高了频繁项集的挖掘速度。     

基于二进制的频繁项集挖掘新算法

频繁项集挖掘是数据挖掘中的一个经典的问题。然而,大部分算法需要扫描数据库多次,算法效率比较低。该文提出了一个效率比较好的挖掘频繁项集的新算法,在这个算法中,所有的事务都是以二进制的形式表示,所以挖掘极大频繁项集的任务就变成了从二进制集中发现频繁模式。而且,这种算法只需要扫描原始数据库一次。最后,利用试验来证明这种算法的效率和优势。     

基于二进制的频繁项集挖掘新算法

频繁项集挖掘是数据挖掘中的一个经典的问题。然而,大部分算法需要扫描数据库多次,算法效率比较低。该文提出了一个效率比较好的挖掘频繁项集的新算法,在这个算法中,所有的事务都是以二进制的形式表示,所以挖掘极大频繁项集的任务就变成了从二进制集中发现频繁模式。而且,这种算法只需要扫描原始数据库一次。最后,利用试验来证明这种算法的效率和优势。     

Two-machine flow shop scheduling problem with blocking,multi-task flexibility of the first machine,and preemption

Blocking flow shop scheduling problem has been extensively studied in recent years; however, some applications mentioned for this problem have some additional characteristics that have not been well considered. Multi-task flexibility of machines and preemption are two of such characteristics. Multi-task flexible machines are capable of processing the operations of at least one other machine in the system. In addition, if preemption is allowed, the solution space grows, and solutions that are more efficient may be obtained. In this study, the two-machine flow shop scheduling problem with blocking, multi-task flexibility of the first machine, and preemption is investigated by considering the minimization of makespan as criterion. It is proved that the complexity of the problem is strongly NP-hard. Because of preemption and multi-task flexibility, there are infinite schedules for each sequence; however, it is shown that a dominant schedule can be defined for each sequence. Two mathematical models are proposed for optimally solving the small-sized instances. Furthermore, a variable neighborhood search algorithm (VNS) and a new variant of it, namely, dynamic VNS (DVNS), are presented to find high quality solutions for large-sized instances. Unlike the VNS algorithm, the DVNS algorithm does not need tuning for the shaking phase. Nevertheless, computational results show that DVNS has even a slightly better performance. The VNS and DVNS algorithms are also compared with some of the best-performing metaheuristics already developed for the flow shop scheduling problem with blocking and minimization of makespan as criterion. Computational results reveal that both algorithms are superior to the others for large-sized instances.     

全局频繁闭项目集挖掘算法研究

频繁闭项目集挖掘是数据挖掘研究中的一个重要研究课题.目前已有的频繁闭项目集挖掘算法主要针对单机环境,有关分布式环境下的全局频繁闭项目集挖掘算法的研究尚不多见.为此,本文提出了一种快速挖掘全局频繁闭项目集算法,并对其更新问题进行了研究;提出了一种相应的频繁闭项目集增量式更新算法,该算法将充分利用先前的挖掘结果来节省发现新的全局频繁闭项目集的时间开销.实验结果表明算法是有效的.     

关联规则挖掘中若干关键技术的研究

Apriori类算法已经成为关联规则挖掘中的经典算法,其技术难点及运算量主要集中在以下两个方面：①如何确定候选频繁项目集和计算项目集的支持数;②如何减少候选频繁项目集的个数以及扫描数据库的次数．目前已提出了许多改进方法来解决第2个问题,并已取得了很好的效果．然而,对于第1个问题,仍沿用Apriori算法中的解决方案,其运算量是较大的．为此,提出了一种基于二进制形式的候选频繁项目集生成和相应的计算支持数算法,该算法只需对挖掘对象进行一些“或”、“与”、“异或”等逻辑运算操作,显著降低了算法的实现难度,将该算法与Apriori类算法相结合,可以进一步提高算法的执行效率,实验结果也表明算法是有效、快速的．     

一种基于MapReduce的频繁闭项集挖掘算法

频繁闭项集的挖掘是发现数据项之间关联规则的一种有效方式。当前以MapReduce模式为基础的云计算平台为解决海量数据中的关联规则挖掘问题提供新的解决思路。文中提出并实现一种基于Hadoop云计算平台的频繁闭项集的并行挖掘算法。该算法主要包括并行计数、构造全局频繁项表、并行挖掘局部频繁闭项集和并行筛选全局频繁闭项集四个步骤。在多个数据集上的实验表明,该方法能较大提高数据挖掘的效率,具有较好的加速比。     

关联规则挖掘中Apriori改进算法的研究

挖掘频繁项集是许多数据挖掘任务中的关键问题,也是关联规则挖掘算法的核心,提高频繁项集的生成效率一直是近几年数据挖掘领域研究的热点之一.在对关联规则挖 掘中基于Apriori算法的改进算法进行深入分析和研究后,本文根据Apriori算法的不足,提出了一种改进策略,从而得到一种优化的Apriori算法.最后,对频繁项集挖掘算法的发展方向进行了初步的探讨.     

变邻域搜索算法综述

变邻域搜索算法(Variable Neighborhood Search,VNS)作为一种新的元启发式算法,已初步成功地用于解决优化问题,尤其是对于大规模组合优化问题效果良好。对VNS的扩展研究层出不穷,并将其成功地应用到旅行商问题、车辆路径问题、调度、图着色等问题中。简述了经典的元启发式算法,并依次论述了优化问题,VNS算法起源,VNS算法原理,VNS算法分析,扩展的VNS分析,VNS在初始解构造、邻域结构构造、局部搜索和停止准则几个方面的改进方法,针对不同版本的VNS归纳了其在各种优化问题应用情况。基于对改进的VNS的分类,从算法自身研究角度和实际应用角度提出了未来研究方向。     

具有总能耗约束的柔性作业车间调度问题研究

针对具有总能耗约束的柔性作业车间调度问题（Flexible job shop scheduling problem,FJSP）,提出一种基于帝国竞争算法（Imperialist competitive algorithm,ICA）和变邻域搜索（Variable neighborhood search,VNS）的双阶段算法,该算法在总能耗不超过给定阈值的条件下最小化Makespan和总延迟时间.由于能耗约束不是总能满足且阈值往往难以事先给定,为此,第一阶段,首先,将原问题转化为具有Makespan、总延迟时间和总能耗的三目标FJSP,然后,利用初始帝国构建和帝国竞争的新策略设计一种ICA对问题求解,并根据ICA的结果确定总能耗阈值;第二阶段,应用解的比较新策略、非劣解集更新方法和当前解周期性更新,构建VNS对原问题求解.计算实验和结果分析表明,两阶段算法对于所研究的问题搜索能力强.     

A variable neighborhood search particle filter for bearings-only target tracking

In this paper a novel filtering procedure that uses a variant of the variable neighborhood search (VNS) algorithm for solving nonlinear global optimization problems is presented. The base of the new estimator is a particle filter enhanced by the VNS algorithm in resampling step. The VNS is used to mitigate degeneracy by iteratively moving weighted samples from starting positions into the parts of the state space where peaks and ridges of a posterior distribution are situated. For testing purposes, bearings-only tracking problem is used, with two static observers and two types of targets: non-maneuvering and maneuvering. Through numerous Monte Carlo simulations, we compared performance of the proposed filtering procedure with the performance of several standard estimation algorithms. The simulation results show that the algorithm mostly performed better than the other estimators used for comparison; it is robust and has fast initial convergence rate. Robustness to modeling errors of this filtering procedure is demonstrated through tracking of the maneuvering target. Moreover, in the paper it is shown that it is possible to combine the proposed algorithm with an interacted multiple model framework.     

A variable neighborhood search for minimizing single machine weighted earliness and tardiness with common due date

Although the concept of just-in-time (JIT) production systems has been proposed for over two decades, it is still important in real-world production systems. In this paper, we consider minimizing the total weighted earliness and tardiness with a restrictive common due date in a single machine environment, which has been proved as an NP-hard problem. Due to the complexity of the problem, metaheuristics, including simulated annealing, genetic algorithm, tabu search, among others, have been proposed for searching good solutions in reasonable computation times. In this paper, we propose a hybrid metaheuristic that uses tabu search within variable neighborhood search (VNS/TS). There are several distinctive features in the VNS/TS algorithm, including different ratio of the two neighborhoods, generating five points simultaneously in a neighborhood, implementation of the B/F local search, and combination of TS with VNS. By examining the 280 benchmark problem instances, the algorithm shows an excellent performance in not only the solution quality but also the computation time. The results obtained are better than those reported previously in the literature.     

A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem

Job shop scheduling problem (JSP) which is widespread in the real-world production system is one of the most general and important problems in various scheduling problems. Nowadays, the effective method for JSP is a hot topic in research area of manufacturing system. JSP is a typical NP-hard combinatorial optimization problem and has a broad engineering application background. Due to the large and complicated solution space and process constraints, JSP is very difficult to find an optimal solution within a reasonable time even for small instances. In this paper, a hybrid particle swarm optimization algorithm (PSO) based on variable neighborhood search (VNS) has been proposed to solve this problem. In order to overcome the blind selection of neighborhood structures during the hybrid algorithm design, a new neighborhood structure evaluation method based on logistic model has been developed to guide the neighborhood structures selection. This method is utilized to evaluate the performance of different neighborhood structures. Then the neighborhood structures which have good performance are selected as the main neighborhood structures in VNS. Finally, a set of benchmark instances have been conducted to evaluate the performance of proposed hybrid algorithm and the comparisons among some other state-of-art reported algorithms are also presented. The experimental results show that the proposed hybrid algorithm has achieved good improvement on the optimization of JSP, which also verifies the effectiveness and efficiency of the proposed neighborhood structure evaluation method.     

Variable neighborhood search for a planning problem with resource constraints in a health simulation center

Applied Intelligence - In this paper we propose the Variable Neighborhood Search (VNS) algorithm SimULS to solve a planning problem in the Health Simulation Center SimUSanté. This center...