Advances in Computational Design

  • 제1권1호
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  • Pages.79-86
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  • 2016
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  • 2383-8477(pISSN)
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  • 2466-0523(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Examination of three meta-heuristic algorithms for optimal design of planar steel frames

  • Tejani, Ghanshyam G. (RK University) ;
  • Bhensdadia, Vishwesh H. (RK University) ;
  • Bureerat, Sujin (Sustainable Infrastructure Research and Development Center, Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University)
  • 투고 : 2015.10.30
  • 심사 : 2015.12.28
  • 발행 : 2016.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this study, the three different meta-heuristics namely the Grey Wolf Optimizer (GWO), Stochastic Fractal Search (SFS), and Adaptive Differential Evolution with Optional External Archive (JADE) algorithms are examined. This study considers optimization of the planer frame to minimize its weight subjected to the strength and displacement constraints as per the American Institute of Steel and Construction - Load and Resistance Factor Design (AISC-LRFD). The GWO algorithm is associated with grey wolves' activities in the social hierarchy. The SFS algorithm works on the natural phenomenon of growth. JADE on the other hand is a powerful self-adaptive version of a differential evolution algorithm. A one-bay ten-story planar steel frame problem is examined in the present work to investigate the design ability of the proposed algorithms. The frame design is produced by optimizing the W-shaped cross sections of beam and column members as per AISC-LRFD standard steel sections. The results of the algorithms are compared. In addition, these results are also mapped with other state-of-art algorithms.

키워드

과제정보

연구 과제 주관 기관 : Thailand Research Fund

참고문헌

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  2. Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems pp.1433-3058, 2018, https://doi.org/10.1007/s00521-018-3603-1
  3. Optimum Seismic Design of 3D Irregular Steel Frames Using Recently Developed Metaheuristic Algorithms vol.32, pp.3, 2018, https://doi.org/10.1061/(ASCE)CP.1943-5487.0000760