A Clustering Search metaheuristic for the Point-Feature Cartographic Label Placement Problem

Rabello, Rômulo Louzada; Mauri, Geraldo Regis; Ribeiro, Glaydston Mattos; Lorena, Luiz Antônio Nogueira

doi:10.1016/j.ejor.2013.10.021

Cited by 27 publications

(30 citation statements)

References 15 publications

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“…Mauri et al [33] developed ILP-based heuristics for the problem of labeling all points while maximizing a function that reflects both the sum of label-dependent profits and the total number of conflict-free labels. Also aiming to maximize the number of conflict-free labels, Rabello et al [34] presented a heuristic method. We argue, however, that certain conflicts in a map must not be tolerated.…”

Section: Related Workmentioning

confidence: 99%

Beyond Maximum Independent Set: An Extended Integer Programming Formulation for Point Labeling

Haunert

Wolff

2017

IJGI

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Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in a map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label-label intersections) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, the influences of labels on other labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate both for randomly generated point sets and an existing data set of cities. Moreover, a relaxation of our ILP allows for a simple and efficient heuristic, which yielded near-optimal solutions for our instances.

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Section: Related Workmentioning

confidence: 99%

Beyond Maximum Independent Set: An Extended Integer Programming Formulation for Point Labeling

Haunert

Wolff

2017

IJGI

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“…Although many different approximations and heuristics have been proposed for this problem, the method based on simulated annealing is preferred in this paper. As a high efficiency and flexible optimization method, simulated annealing is widely used to solve large optimization problems, including label placement [2,8,19,36,37]. The basic processes of simulated annealing are as follows [3]:…”

Section: Selection Of Label Positionsmentioning

confidence: 99%

“…Alvim et al [18] proposed a new point-feature labeling algorithm based on the POPMUSIC (Partial Optimization Metaheuristic under Special Intensification Conditions) frame. Rabello et al [8] presented a clustering search metaheuristic as a new alternative method to solve the point labeling problem.…”

Section: Introductionmentioning

confidence: 99%

“…The label placement is usually divided into three tasks: labeling points, labeling lines, and labeling areas. Because labeling points is a more fundamental problem, point-feature label placement (PFLP) has been extensively studied in recent years [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…And various parameters are used to control the annealing process, such as the initial temperature T o , threshold temperature T c , temperature cooling rate A, reposition times M for each annealing stage, and maximum accepted times K for an immediate temperature decrease. In our labeling process, these parameters are set similar to the values used in previous research [3,8]. The parameter values are shown in Table 2 (where N is the number of point features).…”

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confidence: 99%

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A Labeling Model Based on the Region of Movability for Point-Feature Label Placement

Lin

Zhang

Zhu

et al. 2016

IJGI

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Automatic point-feature label placement (PFLP) is a fundamental task for map visualization. As the dominant solutions to the PFLP problem, fixed-position and slider models have been widely studied in previous research. However, the candidate labels generated with these models are set to certain fixed positions or a specified track line for sliding. Thus, the whole surrounding space of a point feature is not sufficiently used for labeling. Hence, this paper proposes a novel label model based on the region of movability, which comes from plane collision detection theory. The model defines a complete conflict-free search space for label placement. On the premise of no conflict with the point, line, and area features, the proposed model utilizes the surrounding zone of the point feature to generate candidate label positions. By combining with heuristic search method, the model achieves high-quality label placement. In addition, the flexibility of the proposed model enables placing arbitrarily shaped labels.

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Visualizing data as objects by DC (difference of convex) optimization

2017

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In this paper we address the problem of visualizing in a bounded region a set of individuals, which has attached a dissimilarity measure and a statistical value, as convex objects. This problem, which extends the standard Multidimensional Scaling Analysis, is written as a global optimization problem whose objective is the difference of two convex functions (DC). Suitable DC decompositions allow us to use the Difference of Convex Algorithm (DCA) in a very efficient way. Our algorithmic approach is used to visualize two real-world datasets.

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A Clustering Search metaheuristic for the Point-Feature Cartographic Label Placement Problem

Cited by 27 publications

References 15 publications

Beyond Maximum Independent Set: An Extended Integer Programming Formulation for Point Labeling

Beyond Maximum Independent Set: An Extended Integer Programming Formulation for Point Labeling

A Labeling Model Based on the Region of Movability for Point-Feature Label Placement

Visualizing data as objects by DC (difference of convex) optimization

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