Underground space optimization of smart city based on information processing technology

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS

International Journal of Fuzzy Logic and Intelligent Systems Pub Date : 2021-04-26 DOI:10.3233/JIFS-189939

Zhiyong Li

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引用次数: 0

Abstract

This study explores the underground space optimization method of smart city based on information processing technology to save space optimization operation time. Taking coordinate of intersection point, radius of intersection point, mileage of vertical slope change point and elevation of slope change point as design variables, taking total cost of underground space construction of smart city as objective function and taking plane, vertical section, horizontal and vertical combination and environmental impact as constraint conditions, a multi-objective optimization mathematical model of underground space of smart city was constructed. Parete genetic algorithm in information processing technology was used to solve the multi-objective optimization mathematical model of the underground space of smart city, realize the automatic search for the optimal scheme of the underground space of smart city, and complete the intelligent optimization process of the underground space construction scheme of smart city. The results show that the method obtained from this study can effectively optimize the underground space of smart city and obtain the optimal subway laying line, save space and optimize operation time and improve the convergence of the method.

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基于信息处理技术的智慧城市地下空间优化

本研究探索基于信息处理技术的智慧城市地下空间优化方法，节省空间优化运行时间。以交点坐标、交点半径、垂直变坡点里程和变坡点标高为设计变量，以智慧城市地下空间建设总成本为目标函数，以平面、纵断面、水平和垂直组合以及环境影响为约束条件，构建了智慧城市地下空间多目标优化数学模型。利用信息处理技术中的Parete遗传算法求解智慧城市地下空间多目标优化数学模型，实现对智慧城市地下空间最优方案的自动搜索，完成智慧城市地下空间建设方案的智能优化过程。结果表明，本研究所得方法能够有效地优化智慧城市地下空间，获得最优地铁敷设线路，节省空间，优化运行时间，提高方法的收敛性。

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来源期刊

International Journal of Fuzzy Logic and Intelligent Systems COMPUTER SCIENCE, THEORY & METHODS-

CiteScore

2.80

自引率

23.10%

发文量

期刊介绍： The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.