Spatial-text tree clustering algorithm based on improved NSGA-Ⅲ

被引：0
|
作者：
Ma W. [1 ]
Wang R. [1 ]
Wu Y. [1 ]
Deng S. [1 ]
机构：
[1] Science and Technology on Information System Engineering Laboratory, National University of Defense Technology, Changsha
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2020年 / 48卷 / 05期
关键词：
Genetic algorithm; Inverted spatial-text index; Multi-objective optimization; Non-dominating sorting; Priori initial population strategy;
D O I：
10.13245/j.hust.200516
中图分类号：
学科分类号：
摘要：
A multi-objective model for inverted-file R-tree (IR-tree) was constructed, and by improving the solving process of the non-dominating sorting genetic algorithm-Ⅲ (NSGA-Ⅲ), a priori initial population strategy non-dominating sorting genetic algorithm-Ⅲ (PIPS-NSGA-Ⅲ) was proposed to optimize the clustering splitting of text space nodes in IR-Tree. The optimal solution of the minimum overlap and coverage area, the average distance and the semantic similarity between the minimum bounding rectangles (MBRs) of nodes were considered by PIPS-NSGA-Ⅲ. By comparing the evolutionary multi-objective algorithms such as PIPS-NSGA-Ⅲ, NSGA-Ⅱ, NSGA-Ⅲ, SPEA-Ⅱ, the advantages and disadvantages of the algorithms were evaluated from the node classification time, efficiency, query time and accuracy. Experimental results show that the spatial-text index based on PIPS-NSGA-Ⅲ is more efficient, and the average searching time is reduced by 24.8% with the average accuracy improved by 3.75%, compared with the traditional simple traditional R-tree (STR-tree) and R-Tree spatial index structure. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：86 / 92
页数：6
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