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Open AccessArticle

Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm

1
School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
2
Wuhan Geomatics Institute, Wuhan 430079, China
3
School of Geosciences, Yangtze University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Shih-Lung Shaw, Qingquan Li, Yang Yue and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(5), 127; https://doi.org/10.3390/ijgi6050127
Received: 9 December 2016 / Revised: 23 April 2017 / Accepted: 23 April 2017 / Published: 26 April 2017
(This article belongs to the Special Issue Intelligent Spatial Decision Support)
In map generalization, road symbolization and map scale reduction may create spatial conflicts between roads and neighboring buildings. To resolve these conflicts, cartographers often displace the buildings. However, because such displacement sometimes produces secondary spatial conflicts, it is necessary to solve the spatial conflicts iteratively. In this paper, we apply the immune genetic algorithm (IGA) and improved particle swarm optimization (PSO) to building displacement to solve conflicts. The dual-inheritance framework from the cultural algorithm is adopted in the PSO algorithm to optimize the topologic structure of particles. We generate Pareto optimal displacement solutions using the niche Pareto competition mechanism. The results of experiments comparing IGA and the improved PSO show that the improved PSO outperforms IGA; the improved PSO results in fewer graphic conflicts and smaller movements that better satisfy the movement precision requirements. View Full-Text
Keywords: graphic conflicts; displacement; PSO algorithm graphic conflicts; displacement; PSO algorithm
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Huang, H.; Guo, Q.; Sun, Y.; Liu, Y. Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm. ISPRS Int. J. Geo-Inf. 2017, 6, 127.

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