Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel f
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RESEARCH ARTICLE
Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel frames Mohammad Sadegh ES-HAGHIa, Aydin SHISHEGARANb, Timon RABCZUKc,d* a
School of Civil Engineering, Khajeh Nasir Toosi University of Technology, Tehran 19697-64499, Iran School of Civil Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran c Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Vietnam d Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam b
*
Corresponding author. E-mail: [email protected]
© Higher Education Press 2020
ABSTRACT We propose a new algorithm, named Asymmetric Genetic Algorithm (AGA), for solving optimization problems of steel frames. The AGA consists of a developed penalty function, which helps to find the best generation of the population. The objective function is to minimize the weight of the whole steel structure under the constraint of ultimate loads defined for structural steel buildings by the American Institute of Steel Construction (AISC). Design variables are the cross-sectional areas of elements (beams and columns) that are selected from the sets of side-flange shape steel sections provided by the AISC. The finite element method (FEM) is utilized for analyzing the behavior of steel frames. A 15-storey three-bay steel planar frame is optimized by AGA in this study, which was previously optimized by algorithms such as Particle Swarm Optimization (PSO), Particle Swarm Optimizer with Passive Congregation (PSOPC), Particle Swarm Ant Colony Optimization (HPSACO), Imperialist Competitive Algorithm (ICA), and Charged System Search (CSS). The results of AGA such as total weight of the structure and number of analyses are compared with the results of these algorithms. AGA performs better in comparison to these algorithms with respect to total weight and number of analyses. In addition, five numerical examples are optimized by AGA, Genetic Algorithm (GA), and optimization modules of SAP2000, and the results of them are compared. The results show that AGA can decrease the time of analyses, the number of analyses, and the total weight of the structure. AGA decreases the total weight of regular and irregular steel frame about 11.1% and 26.4% in comparing with the optimized results of SAP2000, respectively. KEYWORDS optimization, steel frame, Asymmetric Genetic Algorithm, constraints of ultimate load, constraints of serviceability limits, penalty function
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Introduction
Several new materials and structures have been developed in recent years, and they have been used in big cities such as Tehran. Today the steel frame structure plays an important role in structural and civil engineering because the steel frame structures have various advantages such as the high strength, easiness of shaping, and high ductility. They can help engineers to design tall buildings and costefficient structures [1]. Article history: Received Jul 1, 2019; Accepted Aug 19, 2019
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