Minimum Weight Design of Rigid Frames Subject to System Reliability by Genetic Algorithm

2009 ◽  
Author(s):  
Y. Kohama ◽  
T. Takada ◽  
N. Kozawa ◽  
A. Miyamura
Keyword(s):  
Genetic Algorithm ◽  
System Reliability ◽  
Minimum Weight ◽  
Minimum Weight Design ◽  
Weight Design ◽  
Rigid Frames

Genetic-algorithm-based minimum weight design of an outrigger system for high-rise buildings

2016 ◽  
Vol 117 ◽  
pp. 496-505 ◽  
Author(s):  
Hyo Seon Park ◽  
Eunseok Lee ◽  
Se Woon Choi ◽  
Byung Kwan Oh ◽  
Tongjun Cho ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Minimum Weight ◽  
Minimum Weight Design ◽  
High Rise ◽  
Weight Design

scholarly journals Resizing Technique-Based Hybrid Genetic Algorithm for Optimal Drift Design of Multistory Steel Frame Buildings

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hyo Seon Park ◽  
Eunmi Kwon ◽  
Yousok Kim ◽  
Se Woon Choi
Keyword(s):  
Genetic Algorithm ◽  
Minimum Weight ◽  
Hybrid Genetic Algorithm ◽  
Optimization Methods ◽  
Steel Frame ◽  
Convergence Properties ◽  
Minimum Weight Design ◽  
Frame Buildings ◽  
Steel Frame Buildings ◽  
Weight Design

Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.

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