scholarly journals Weight optimization of composite cellular beam based on the differential evolution algorithm

2018 ◽  
Vol 12 (5) ◽  
pp. 28-38
Author(s):  
Nguyen Tran Hieu ◽  
Vu Anh Tuan
Keyword(s):  
Differential Evolution ◽  
Design Method ◽  
Minimum Weight ◽  
Differential Evolution Algorithm ◽  
Limit States ◽  
Web Opening ◽  
Design Variables ◽  
Optimal Spacing ◽  
Evolution Algorithm ◽  
Hot Rolled

In this study, the differential evolution algorithm is used for solving the optimum design problem of composite cellular beams. The design variables are hot rolled profile from which the cellular beam will be produced as well as opening size and its spacing. The objective function is the minimum weight of cellular beam while the design constraints include satisfying the ultimate limit states, the serviceability limit states and the geometric limitations. The design method adopted in this study is based on EN 1994-1-1. Furthermore, a parametric study is conducted to evaluate the influence of beams spacing to the weight of floor beam system. As a result, an optimal spacing of composite cellular beams is proposed. Keywords: composite beam; cellular beam; web opening; steel beam optimization; differential evolution.

scholarly journals Optimization of steel roof trusses using machine learning-assisted differential evolution

2021 ◽  
Vol 15 (4) ◽  
pp. 99-110
Author(s):  
Nguyen Tran Hieu ◽  
Nguyen Quoc Cuong ◽  
Vu Anh Tuan
Keyword(s):  
Differential Evolution ◽  
Computational Cost ◽  
Differential Evolution Algorithm ◽  
Truss Structures ◽  
De Algorithm ◽  
Design Variables ◽  
Evolution Algorithm ◽  
Structural Engineers ◽  
Steel Trusses ◽  
Steel Roof

A steel truss is a preferred solution in large-span roof structures due to its good attributes such as lightweight, durability. However, designing steel trusses is a challenging task for engineers due to a large number of design variables. Recently, optimization-based design approaches have demonstrated the great potential to effectively support structural engineers in finding the optimal designs of truss structures. This paper aims to use the AdaBoost-DE algorithm for optimizing steel roof trusses. The AdaBoost-DE employed in this study is a hybrid algorithm in which the AdaBoost classification technique is used to enhance the performance of the Differential Evolution algorithm by skipping unnecessary fitness evaluations during the optimization process. An example of a duo-pitch steel roof truss with a span of 24 meters is carried out. The result shows that the AdaBoost-DE achieves the same optimal design as the original DE algorithm, but reduces the computational cost by approximately 36%.

Modified differential evolution algorithm for semi-supervised fuzzy clustering

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO
Keyword(s):  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm
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