Vol. 22, No. 1, pp. 64-77 (2026)
APPLICATION OF IMPROVED DUAL-POPULATION OPTIMIZATION
FRAMEWORK IN DESIGN OF GRID STRUCTURE
Xu-Chen Xu 2, *, Hong-Bo Liu 1, 3, Zhi-Hua Chen 1, 2 and Ting Zhou 4
1 State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
2 Department of Civil Engineering, Tianjin University, Tianjin 300072, China
3 Department of Civil Engineering, Hebei University of Engineering, Handan 056000, China
4 School of Architecture, Tianjin University, Tianjin 300072, China
*(Corresponding author: E-mail:This email address is being protected from spambots. You need JavaScript enabled to view it.)
Received: 21 February 2025; Revised: 3 June 2025; Accepted: 4 June 2025
DOI:10.18057/IJASC.2026.22.1.6
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ABSTRACT
With the advantages of simple form, beautiful appearance, uniform force and strong span ability, grid structure has been widely used in long-span space structure. The traditional design often relies on the experience of engineers with low efficiency and using heuristic algorithms to drive design is a very good way. Simple genetic algorithm is a typical heuristic algorithm, the concept is clear, but easy to "precocious" or even not convergence. Based on the idea of "dual-population evolution", this paper introduces a series of strategies, and constructs an improved dual-population genetic algorithm (IDPGA). Two subpopulations evolve independently and exchange some individuals to prevent falling into local optima and expand searching capabilities. Then, combined with ABAQUS script, two acceleration strategies are adopted to form an intelligent optimization framework for solving the grid structure design problems, including static and dynamic optimization problems. The results show that the algorithm is effective, reliable, robust and accurate. In addition, in practical application, a satisfactory engineering solution can be found without fully exerting the optimization ability of the algorithm.
KEYWORDS
Grid structure, Dual-population, Genetic algorithm, Optimization, Design
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