Optimal Building Frame Column Design Based on the Genetic Algorithm

Shen, Tao; Gao, Yukari Nagai and Chan

doi:10.32604/cmc.2019.04885

Cited by 3 publications

(2 citation statements)

References 10 publications

(10 reference statements)

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“…The design of the FF for each solution is critical to the performance of the algorithm [Shen, Nagai and Gao (2019)]. In the proposed method, the FF depends on a weighting scheme.…”

Section: Fitness Functionmentioning

confidence: 99%

Genetic–Frog-leaping Algorithm for Text Document Clustering

Alhenak¹,

Hosny²

2019

Computers, Materials &Amp; Continua

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In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of a document in terms of relevance. Nevertheless, as documents usually contain a large number of words, some of them may be irrelevant to the topic under consideration or redundant. This can confuse and complicate the clustering process and make it less accurate. Accordingly, feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features. In this study, we developed a text document clustering optimization model using a novel genetic frogleaping algorithm that efficiently clusters text documents based on selected features. The proposed approach is based on two metaheuristic algorithms: a genetic algorithm (GA) and a shuffled frog-leaping algorithm (SFLA). The GA performs feature selection, and the SFLA performs clustering. To evaluate its effectiveness, the proposed approach was tested on a well-known text document dataset: the "20Newsgroup" dataset from the University of California Irvine Machine Learning Repository. Overall, after multiple experiments were compared and analyzed, it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering, compared with classical K-means clustering. Nevertheless, this improvement requires longer computational time.

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“…The design of the FF for each solution is critical to the performance of the algorithm [Shen, Nagai and Gao (2019)]. In the proposed method, the FF depends on a weighting scheme.…”

Section: Fitness Functionmentioning

confidence: 99%

Genetic–Frog-leaping Algorithm for Text Document Clustering

Alhenak¹,

Hosny²

2019

Computers, Materials &Amp; Continua

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“…Kripakaran et al [ 21 ] presented a computational approach based on genetic algorithms to evaluate the cost of connections in the optimization of moment‐resisting steel frames. Shen et al [ 22 ] used the GA to optimize the building frame column, in which a population initialization module is proposed to improve the optimization efficiency of the genetic algorithm. Baradaran and Madhkhan [ 23 ] presented an improved GA for the optimization of steel frame designs, in which the rate of convergence to the optimal solution was increased by splitting the population generation process to two stages.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of steel–concrete hybrid wind turbine tower based on improved genetic algorithm

Chen

2020

Structural Design Tall Build

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Summary The steel–concrete hybrid wind turbine tower is characterized by the lower part of the traditional steel tubular tower replaced with the concrete segment. The lateral stiffness will be improved obviously, and then, the excessive vibration of the steel tower can be solved effectively. Based on the improved genetic algorithm, an optimization program is built to consider the influence of materials, labor, machinery, and transportation on the construction cost of a steel–concrete hybrid tower for a 2.0‐MW wind turbine with a hub height of 120 m, in which the initial height of the concrete segment is 32 m. During the optimization process, design requirements of relevant specifications and industry standards are used as the constraints. The optimization variables include the bottom and top diameters of the tower, the wall thickness of each segment, the height of the concrete segment, and the area of the prestressed steel strand. By comparing the results of construction cost and structural capacity before and after optimization, it can be found that the steel–concrete hybrid wind turbine tower after optimization has the better structural stiffness and lower construction cost. The proposed optimization program can meet the design requirements and significantly improve the economic performance of the tower.

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Quantitative Evaluation of Promotion of Traditional National Sports Events Based on Probity Multiple Regression Analysis

Yang

2020

Communications in Computer and Information Science

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Optimal Building Frame Column Design Based on the Genetic Algorithm

Cited by 3 publications

References 10 publications

Genetic–Frog-leaping Algorithm for Text Document Clustering

Genetic–Frog-leaping Algorithm for Text Document Clustering

Design optimization of steel–concrete hybrid wind turbine tower based on improved genetic algorithm

Quantitative Evaluation of Promotion of Traditional National Sports Events Based on Probity Multiple Regression Analysis

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