Volume 14, Issue 3 (6-2024)
IJOCE 2024, 14(3): 461-487 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Biabani F, A. Dehghani A, Shojaee S, Hamzehei-Javaran S. SIZE AND GEOMETRY OPTIMIZATION OF TRUSS STRUCTURES USING HGPG ALGORITHM. IJOCE 2024; 14 (3) :461-487
URL: http://ijoce.iust.ac.ir/article-1-603-en.html
URL: http://ijoce.iust.ac.ir/article-1-603-en.html
1- Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract: (2876 Views)
Optimization has become increasingly significant and applicable in resolving numerous engineering challenges, particularly in the structural engineering field. As computer science has advanced, various population-based optimization algorithms have been developed to address these challenges. These methods are favored by most researchers because of the difficulty of calculations in classical optimization methods and achieving ideal solutions in a shorter time in metaheuristic technique methods. Recently, there has been a growing interest in optimizing truss structures. This interest stems from the widespread utilization of truss structures, which are known for their uncomplicated design and quick analysis process. In this paper, the weight of the truss, the cross-sectional area of the members as discrete variables, and the coordinates of the truss nodes as continuous variables are optimized using the HGPG algorithm, which is a combination of three metaheuristic algorithms, including the Gravity Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO). The presented formulation aims to improve the weaknesses of these methods while preserving their strengths. In this research, 15, 18, 25, and 47-member trusses were utilized to show the efficiency of the HGPG method, so the weight of these examples was optimized while their constraints such as stress limitations, displacement constraints, and Euler buckling were considered. The proposed HGPG algorithm operates in discrete and continuous modes to optimize the size and geometric configuration of truss structures, allowing for comprehensive structural optimization. The numerical results show the suitable performance of this process.
Keywords: HGPG algorithm, Structural Optimization, Metaheuristic Algorithm, Size Optimization, Geometry Optimization, Multi-objective Optimization, Truss Design
Type of Study: Research |
Subject:
Optimal design
Received: 2024/08/16 | Accepted: 2024/09/22 | Published: 2024/06/23
Received: 2024/08/16 | Accepted: 2024/09/22 | Published: 2024/06/23
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
