Placing Text Labels on Maps and Diagrams using Genetic Algorithms with Masking

Verner, Oleg V.; Wainwright, Roger L.; Schoenefeld, Dale A.

doi:10.1287/ijoc.9.3.266

Cited by 36 publications

(16 citation statements)

References 12 publications

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“…In accord with [5,6,8,9,[16][17][18] and others, the cartographic preferences were not considered (w i,j ¼1) for all candidate positions and the total number of candidate positions was equal to 4 (P i ¼{1,2,3,4}8i¼1,y,N). The reduction heuristic proposed by Wagner et al [21] was used to reduce the initial conflict graph, i.e., the conflict graph (problem) was reduced before we apply the METIS heuristic.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Christensen et al [8,15] presented a good review about the PFCLP and proposed a local search technique based on a discrete form of the gradient descent and a simulated annealing algorithm. Verner et al [16] applied a genetic algorithm with mask such that if a label has a conflict, the changing of positions is allowed by crossover operators. Yamamoto et al [13] proposed a Tabu search algorithm.…”

Section: Article In Pressmentioning

confidence: 99%

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

Mauri

Ribeiro

Lorena

2010

Computers & Operations Research

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a b s t r a c tThis 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.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Article In Pressmentioning

confidence: 99%

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

Mauri

Ribeiro

Lorena

2010

Computers & Operations Research

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“…The CPLEX processing times reported in Table 1 do not include the time for finding the maximal cliques. As considered by several authors, see Alvim andTaillard (2009), Christensen et al (1995), Ribeiro and Lorena (2008a, b), Verner et al (1997), Yamamoto and Lorena (2005) and Zoraster (1990), we used the four-position labeling model with four candidate position for each point (L i = {1, 2, 3, 4}, for i ¼ 1; . .…”

Section: Computational Resultsmentioning

confidence: 99%

“…Christensen et al (1994Christensen et al ( , 1995 presented a good review about the point-labeling problem and proposed a local search technique based on a discrete form of the gradient descent and a Simulated Annealing algorithm. Verner et al (1997) applied a Genetic Algorithm with masking so that if a label is in conflict, the changing of positions is allowed by crossover operators. Yamamoto et al (2002) proposed a Tabu Search algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A lagrangean decomposition for the maximum independent set problem applied to map labeling

2010

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The Maximum Independent Set (MIS) problem is a well-known problem where the aim is to find the maximum cardinality independent set in an associated graph. Map labeling problems can often be modeled as a MIS problem in a conflict graph, where labels are selected to be placed near graphical features not allowing overlaps (conflicts) between labels or between labels and features. However, the MIS problem is NP-hard and exact techniques present difficulties for solving some instances. Thus, this paper presents a Lagrangean decomposition to solve a map labeling problem, the Point-Feature Label Placement problem. We treated the problem by a conflict graph that is partitioned into small sub-problems and copies of some decision variables are done. These copies are used in the subproblems constraints and in some constraints to ensure the equality between the original variables and their copies. After these steps, we relax these copy constraints in a Lagrangean way. Using instances proposed in the literature, our approach was able to prove the optimality for all of them, except one, and the results were better than the ones provided by a commercial solver.

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“…The idea of using the simulated annealing algo- (Kirkpatrick, Gelatt, and Vecchi 1983) to solve the PFLP problem was pursued by Christensen, Marks, and Shieber (1995) and Zoraster (1997). Further, a genetic algorithm for the PFLP problem was proposed by Verner, Wainwright, and Schoenefeld (1997). Yamamoto, Camara, and Lorena (2002) applied a tabu search heuristic to cartographic label placement.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Multi-criteria Model for High Cartographic Quality Point-Feature Label Placement

Rylov

Reimer

2014

Cartographica

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The lettering process, including assigning names to point features, is an essential part of map production. While there have been numerous and varied research efforts to automate point-feature label placement (PFLP), none of them seems to have taken into account the many well-established cartographic precepts for point-feature annotation used by human cartographers. As a result, current fully automated solutions are limited in their expressive power. The PFLP problem is still vital, therefore, and solving it is a compelling challenge. This article presents a comprehensive multi-criterion model that complies with almost all well-defined cartographic placement principles and requirements for PFLP, allowing for a significant increase in toponym density without affecting legibility. The proposed model, expressed as a qualityevaluation function, can be used by any mathematical optimization algorithm to resolve the automated label-placement problem. Through an application of the proposed model tested on volunteered geographic (VGI) data and the creation of sample parameter settings, the article illustrates that a high level of cartographic quality for PFLP can be achieved through the integrated approach, comparable to the lettering produced by an expert cartographer. RÉSUMÉLe processus de lettrage, y compris l'attribution de noms à des caractéristiques ponctuelles, fait partie essentielle de la production de cartes. De nombreux efforts de recherche variés ont visé à automatiser la mise en place d'étiquettes de caractéristiques ponctuelles (MPECP), mais aucun de ces efforts ne semble avoir tenu compte des nombreux préceptes cartographiques bien établis portant sur l'annotation des caractéristiques ponctuelles utilisées par les cartographes humains. Il s'ensuit que les solutions entièrement automatisées courantes ont un pouvoir expressif limité. Le problème de la MPECP demeure donc vital et sa solution constitue un défi difficile à relever. Cet article présente un modèle multicritériel détaillé qui se conforme à presque tous les principes bien définis de mise en place cartographique et aux exigences de la MPECP, ce qui permet d'augmenter considérablement la densité toponymique sans que cela ait d'effet sur la lisibilité. Le modèle proposé, exprimé sous forme d'une fonction qualité-évaluation, peut être utilisé par n'importe quel algorithme d'optimisation mathématique pour régler le problème de la mise en place automatisée des étiquettes. En appliquant le modèle proposé mis à l'essai sur des données géographiques fournies volontairement et en créant des contextes paramétriques témoins, l'article démontre qu'il est possible d'obtenir une grande qualité cartographique pour la MPECP grâce à l'approche intégrée, comparable au lettrage produit par un cartographe expert.Mots clés : cartographie automatisée, mise en place automatisée des étiquettes, MPECP, optimisation combinatoire, cartographie SIG

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Placing Text Labels on Maps and Diagrams using Genetic Algorithms with Masking

Cited by 36 publications

References 12 publications

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

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

A lagrangean decomposition for the maximum independent set problem applied to map labeling

A Comprehensive Multi-criteria Model for High Cartographic Quality Point-Feature Label Placement

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