A hybrid data mining heuristic to solve the point‐feature cartographic label placement problem

A clean map visualization requires the fewest possible overlaps and depends on how labels are attached to point features. In this paper, we address the cartographic label placement variant problem whose objective is to label a set of points maximizing the number of conflict‐free points. Thus, we propose a hybrid data mining heuristic to solve the point‐feature cartographic label placement problem based on a clustering search (CS) heuristic, a state‐of‐the‐art method for this problem. Although several works have investigated the combination of data mining and multistart metaheuristics, this is the first time data mining has been used to improve CS and simulated annealing based heuristics. Computational experiments showed that the proposed hybrid heuristic was able to reach better cost solutions than the original strategy, with the same time effort. The proposed heuristic also could find almost all known optimal solutions and improved most of the best results for the set of large instances reported so far in the literature.     

A greedy randomized adaptive search procedure for the point-feature cartographic label placement

The point-feature cartographic label placement problem (PFCLP) is an NP-hard problem, which appears during the production of maps. The labels must be placed in predefined places avoiding overlaps and considering cartographic preferences. Owing to its high complexity, several heuristics have been presented searching for approximated solutions. This paper proposes a greedy randomized adaptive search procedure (GRASP) for the PFCLP that is based on its associated conflict graph. The computational results show that this metaheuristic is a good strategy for PFCLP, generating better solutions than all those reported in the literature in reasonable computational times.     

Heuristics for cartographic label placement problems

The cartographic label placement problem is an important task in automated cartography and Geographical Information Systems. Positioning the texts requires that overlap among texts should be avoided, that cartographic conventions and preference should be obeyed. This paper examines the point-feature cartographic label placement problem (PFCLP) as an optimization problem. We formulate the PFCLP considering the minimization of existing overlaps and labeling of all points on a map. This objective improves legibility when all points must be placed even if overlaps are inevitable. A new mathematical formulation of binary integer linear programming that allows labeling of all points is presented, followed by some Lagrangean relaxation heuristics. The computational tests considered instances proposed in the literature up to 1000 points, and the relaxations provided good lower and upper bounds.     

Real-time map labelling for mobile applications

It is essential to label roads, landmarks, and other important features on maps for mobile applications to help users to understand their location and the environment. This paper aims to examine real-time map labelling methods suitable for the small screen on mobile devices. A slider method with a continuous search space was proposed to sequentially label both line and point features. The method starts with defining a range box for possible locations of the label. Then a search is performed, and the range box is reduced, if there are any cartographic objects that overlap the range box. Finally, the label is placed, at the best possible position in the reduced range box according to normal cartographic preferences, where it does not obscure any cartographic object. We implemented this method in a Java environment using the open source library JTS Topology Suite. A case study showed sound cartographic results of the labelling and acceptable computational efficiency.     

Practical Extensions of Point Labeling in the Slider Model*

This paper extends research by the authors together with Alexander Wolff on point label placement using a model where labels can be placed at any position that touches the point (the slider model). Such models have been shown to perform better than methods that allow only a fixed number of positions per label. The novelties in this paper include respecting other map features that must be avoided by the labels, and incorporating labels with different height. The result is an efficient and simple O((n+m)log(n+m)) time algorithm with a performance guarantee for label placement in the slider model. Here n is the number of points to be labeled and m is the combinatorial complexity of the map features that must be avoided. Due to its efficiency, the algorithm can be used in interactive and on-line mapping.     

Dispersion for the point-feature cartographic label placement problem

The point-feature cartographic label placement problem (PFCLP) is a NP-Hard problem arising in design of maps and other graphic objects. For the sake of a better map legibility it is important to avoid overlaps in the process of labeling. This paper examines the PFCLP in the legibility context and proposes a dispersion approach for the problem. It is considered that when all points must to be labeled and overlaps are inevitable, the map can be more readable if overlapping labels are placed more distant from each other. The PFCLP is modeled as a dispersion problem on two mathematical formulations based on binary integer linear programming. Computational tests have provided good results on several generated instances.     

地图文本注记问题的遗传算法求解

针对地理信息系统中对图形进行文本注记难,大多数已知工具都产生一定数量的注记重叠并且需要人工调节的问题,采用遗传算法解决注记冲突.综合注记信息与线状图形信息的特征,提出了以重叠注记构造位串编码,采用沿折线随机游走的方式构造变异算子,并考虑两个目标,一个是在注记中避免重叠,另一个是提高注记放置的美术性,采用梯形模糊数实现美学度量.初步测试表明,所提算法在快速文本注记和提高美学度上非常有效.     

A new mathematical model and a Lagrangean decomposition for the point-feature cartographic label placement problem

This paper proposes a 0-1 integer linear programming model for the point-feature cartographic label placement problem based on labeling of the largest number of free labels. In addition, one non-trivial valid inequality is presented to strengthen this proposed model. Even with the strengthened model, a commercial solver was not able to solve a representative sample of known instances presented in the literature. Thus, we also present a Lagrangean decomposition technique based on graph partitioning. Our added approaches established optimal solutions for practically all the used instances and the results significantly improved the ones presented in recent studies concerning the problem.     

Lagrangean relaxation with clusters for point-feature cartographic label placement problems

This paper presents two new mathematical formulations for the point-feature cartographic label placement problem (PFCLP) and a new Lagrangean relaxation with clusters (LagClus) to provide bounds to these formulations. The PFCLP can be represented by a conflict graph and the relaxation divides the graph in small subproblems (clusters) that are easily solved. The edges connecting clusters are relaxed in a Lagrangean way and a subgradient algorithm improves the bounds. The LagClus was successfully applied to a set of instances up to 1000 points providing the best results of those reported in the literature.     

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.     

Incremental labeling in closed-2PM model

We consider an incremental optimal label placement in a closed-2PM map containing points each attached with a label. Labels are assumed to be axis-parallel square-shaped and have to be pairwise disjoint with maximum possible length each attached to its corresponding point on one of its horizontal edges. Such a labeling is denoted as optimal labeling. Our goal is to efficiently generate a new optimal labeling for all points after each new point being inserted in the map. Inserting each point may require several labels to flip or all labels to shrink. We present an algorithm that generates each new optimal labeling in O(lgn+k) time where k is the number of required label flips, if there is no need to shrink the label lengths, or in O(n) time when we have to shrink the labels and flip some of them. The algorithm uses O(n) space in both cases. This is a new result on this problem.     

A tabu search/path relinking algorithm to solve the job shop scheduling problem

We present an algorithm that incorporates a tabu search procedure into the framework of path relinking to generate solutions to the job shop scheduling problem (JSP). This tabu search/path relinking (TS/PR) algorithm comprises several distinguishing features, such as a specific relinking procedure to effectively construct a path linking the initiating solution and the guiding solution, and a reference solution determination mechanism based on two kinds of improvement methods. We evaluate the performance of TS/PR on almost all of the benchmark JSP instances available in the literature. The test results show that TS/PR obtains competitive results compared with state-of-the-art algorithms for JSP in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, TS/PR is able to improve the upper bounds for 49 out of the 205 tested instances and it solves a challenging instance that has remained unsolved for over 20 years.     

Particle-based labeling: Fast point-feature labeling without obscuring other visual features

In many information visualization techniques, labels are an essential part to communicate the visualized data. To preserve the expressiveness of the visual representation, a placed label should neither occlude other labels nor visual representatives (e.g., icons, lines) that communicate crucial information. Optimal, non-overlapping labeling is an NP-hard problem. Thus, only a few approaches achieve a fast non-overlapping labeling in highly interactive scenarios like information visualization. These approaches generally target the point-feature label placement (PFLP) problem, solving only label-label conflicts. This paper presents a new, fast, solid and flexible 2D labeling approach for the PFLP problem that additionally respects other visual elements and the visual extent of labeled features. The results (number of placed labels, processing time) of our particle-based method compare favorably to those of existing techniques. Although the esthetic quality of non-real-time approaches may not be achieved with our method, it complies with practical demands and thus supports the interactive exploration of information spaces. In contrast to the known adjacent techniques, the flexibility of our technique enables labeling of dense point clouds by the use of nonoccluding distant labels. Our approach is independent of the underlying visualization technique, which enables us to demonstrate the application of our labeling method within different information visualization scenarios.     

Cartographic name placement with Prolog

A major problem in computer cartography is how to place names on maps so they are clearly associated with the features they annotate, while avoiding overlap with other names and features. The logic programming language, Prolog, can be used to express the name-placement problem as a set of rules, referring primarily to the identification of free space, the generation of trial label positions, and the resolution of conflict between these positions. Cartographic features can be specified either explicitly as facts in the Prolog database or implicitly by presenting Prolog with the results of a prior analysis of potential label positions. The Prolog inference mechanism can then determine whether there is a combination of label positions that satisfies the rules of placement     

基于条件随机域的词性标注模型

词性标注主要面临兼类词消歧以及未知词标注的难题,传统隐马尔科夫方法不易融合新特征,而最大熵马尔科夫模型存在标注偏置等问题。本文引入条件随机域建立词性标注模型,易于融合新的特征,并能解决标注偏置的问题。此外,又引入长距离特征有效地标注复杂兼类词,以及应用后缀词与命名实体识别等方法提高未知词的标注精度。在条件随机域模型框架下,本文进一步探讨了融合模型的方法及性能。词性标注开放实验表明,条件随机域模型获得了96.10%的标注精度。     

粒子群遗传混合算法在点状注记配置中的应用

通过重新定义粒子位置、速度以及其相应的运算规则,本文将粒子群遗传混合算法应用到点状注记配置中。通过借鉴遗传算法中的变异操作的思想,本文使用变异算子对粒子进行变异操作,提高了粒子群的粒子多样性,避免了局部收敛和粒子搜索能力的下降。最后使用注记密度分别为12%和35%的地图数据对本算法进行测试。测试结果表明,本算法具有良好的稳定性,能够解决点状注记配置问题。     

Genetic algorithm with competitive image labelling and least square

A multi-modal genetic algorithm using a dynamic population concept is introduced. Each image point is assigned a label and for a chromosome to survive, it must have at least one image point with its label. In this way, the genetic algorithm dynamically segments the scene into one or more objects and the background noise. A Repeated Least Square technique is applied to enhance the convergence performance. The integrated algorithm is tested using a 6 degrees of freedom template matching problem, and it is applied to some images that are challenging for genetic algorithm applications.     

基于类属特征和实例相关性的多标签分类算法

学习类属特征方法为每个标签选择特有特征并考虑成对标签的相关性以降低维度,可有效解决多标签分类遇到的维度过大问题,但缺乏对实例相关性的考虑.针对此问题,文中提出基于类属特征和实例相关性的多标签分类算法,不仅考虑标签相关性还考虑实例特征的相关性.通过构建相似性图,学习实例特征空间的相似性.在8个数据集上的实验表明,文中算法可有效提取类属特征,具有较好的分类性能.