A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm

Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clar...

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Vydáno v:Applied sciences Ročník 10; číslo 23; s. 8441
Hlavní autoři: Li, Wende, Ai, Tinghua, Shen, Yilang, Yang, Wei, Wang, Weilin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2020
Témata:
building displacement
Buildings
cartographic generalization
Cluster analysis
conflict resolution
Evolution & development
Genetic algorithms
multipopulation genetic algorithm
Optimization
Population
ISSN:2076-3417, 2076-3417
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Shrnutí:Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel building displacement method based on multipopulation genetic algorithm (BDMPGA) is proposed to resolve spatial conflicts. This approach introduces multiple populations with different control parameters for simultaneous search optimization and adopts an immigration operation to connect different populations to realize coevolution. The optimal individuals of each population are selected and preserved in the elite population through manual selection operation to prevent the optimal individuals from being destroyed and lost in the evolutionary process. Meanwhile, the least preserving generation of the optimal individuals is used as the termination basis. To validate the proposed method, urban building data with a scale of 1:10,000 from Shenzhen, China are used. The experimental results indicate that the method proposed in this paper can effectively resolve spatial conflicts to obtain better results.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10238441