An MPI-based parallel genetic algorithm for multiple geographical feature label placement based on the hybrid of fixed-sliding models
Multiple Geographical Feature Label Placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. Moreover, the nature of label positioning has proven to be an Nondeterministic polynomial-time hard (NP-hard) problem. Although advances in computer technology an...
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| Published in | Geo-spatial information science Vol. 28; no. 2; pp. 761 - 779 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Wuhan
Taylor & Francis
04.03.2025
Taylor & Francis Ltd Taylor & Francis Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1009-5020 1993-5153 1993-5153 |
| DOI | 10.1080/10095020.2024.2313326 |
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| Abstract | Multiple Geographical Feature Label Placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. Moreover, the nature of label positioning has proven to be an Nondeterministic polynomial-time hard (NP-hard) problem. Although advances in computer technology and robust approaches have addressed the problem of label positioning, the lengthy running time of MGFLP has not been a major focus of recent studies. Based on a hybrid of the fixed-position and sliding models, a Message Passing Interface (MPI) parallel genetic algorithm is proposed in the present study for MGFLP to label mixed types of geographical features. To evaluate the quality of label placement, a quality function is defined based on four quality metrics: label-feature conflict; label-label conflict; label association with the corresponding feature; label position priority for all three types of features. The experimental results show that the proposed algorithm outperforms the DDEGA, DDEGA-NM, and Parallel-MS in both label placement quality and computation time efficiency. Across three datasets, compared to Parallel-MS, running times decreased from 118.45 to 8.34, 45.98 to 3.51, and 20.01 to 0.43 min, with further reductions in label-label and label-feature conflicts. |
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| AbstractList | Multiple Geographical Feature Label Placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. Moreover, the nature of label positioning has proven to be an Nondeterministic polynomial-time hard (NP-hard) problem. Although advances in computer technology and robust approaches have addressed the problem of label positioning, the lengthy running time of MGFLP has not been a major focus of recent studies. Based on a hybrid of the fixed-position and sliding models, a Message Passing Interface (MPI) parallel genetic algorithm is proposed in the present study for MGFLP to label mixed types of geographical features. To evaluate the quality of label placement, a quality function is defined based on four quality metrics: label-feature conflict; label-label conflict; label association with the corresponding feature; label position priority for all three types of features. The experimental results show that the proposed algorithm outperforms the DDEGA, DDEGA-NM, and Parallel-MS in both label placement quality and computation time efficiency. Across three datasets, compared to Parallel-MS, running times decreased from 118.45 to 8.34, 45.98 to 3.51, and 20.01 to 0.43 min, with further reductions in label-label and label-feature conflicts. |
| Author | Guo, Zhiyong Lessani, M. Naser Deng, Jiqiu Li, Zhenlong |
| Author_xml | – sequence: 1 givenname: M. Naser surname: Lessani fullname: Lessani, M. Naser organization: Pennsylvania State University – sequence: 2 givenname: Zhenlong orcidid: 0000-0002-8938-5466 surname: Li fullname: Li, Zhenlong email: zhenlong@psu.edu organization: Pennsylvania State University – sequence: 3 givenname: Jiqiu surname: Deng fullname: Deng, Jiqiu organization: Central South University – sequence: 4 givenname: Zhiyong surname: Guo fullname: Guo, Zhiyong organization: Central South University |
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| SubjectTerms | Algorithms fixed position Genetic algorithms geographical features Label placement Labels Message passing message passing interface parallel genetic algorithm Placement Polynomials Run time (computers) Scientific visualization Sliding |
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| Title | An MPI-based parallel genetic algorithm for multiple geographical feature label placement based on the hybrid of fixed-sliding models |
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