A Hybrid of Differential Evolution and Genetic Algorithm for the Multiple Geographical Feature Label Placement Problem

Abstract

Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point, line and area features, less attention has been paid to the problem of jointly labeling all the different types of geographical features. In this paper, we refer to the labeling of all the graphic features as the multiple geographical feature label placement (MGFLP) problem. In the MGFLP problem, the overlapping and occlusion among labels and corresponding features produces poorly arranged labels, and results in a low-quality map. To solve the problem, a hybrid algorithm combining discrete differential evolution and the genetic algorithm (DDEGA) is proposed to search for an optimized placement that resolves the MGFLP problem. The quality of the proposed solution was evaluated using a weighted metric regarding a number of cartographical rules. Experiments were carried out to validate the performance of the proposed method in a set of cartographic tasks. The resulting label placement demonstrates the feasibility and the effectiveness of our method.

Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space

“…Three datasets were selected to verify the performance of the DDEGA algorithm for solving the problem of MGFLP, Lu et al [2]. To compare the advantages and disadvantages of the two algorithms, a real dataset was selected for the experiments.…”

“…This causes ambiguity for the readers and has a negative effect on the map visualization. Lu et al [2] proposed hybrid discrete differential evolution and genetic algorithm (DDEGA) to realize the cross-layer feature label placement. DDEGA algorithm can effectively solve the problem of multiple geographical feature label placement to a certain extent.…”

“…Considering the mentioned factors can comprehensively reflect the advantages and disadvantages of automatic label placement results. To compare the quality of each generated position, it is necessary to evaluate them based on the given score value, and we refer to the corresponding Scoring Rules developed by Lu et al [2]. Since label conflict and labelfeature conflict are more important on automatic label placement, the weights of the two factors were deliberately increased.…”

“…The second issue that needs to be considered is to choose an appropriate optimization algorithm to find the optimal candidate positions from large-scale solutions with reasonable execution time. Lu et al [2] proposed the DDEGA algorithm and achieved better results in cross-layer feature label placement. DDEGA algorithm is a hybrid of discrete differential evolution algorithm [36] and genetic algorithm [37].…”

“…Therefore, due to the lack of the insufficient degree of freedom space, all generated positions of each feature are potentially in conflict with features and other label positions while taking into account exclusively the reference position, in this case, the elimination of label conflict and label-feature conflict decreases. Lu et al [2] presented the DDEGA algorithm for multiple geographical feature label placement based on one degree of freedom space. However, the orientation of the label for area features and the problem of small-area features are not regarded which can cause significant ambiguity on the map.…”