Optimum structural design of full-scale steel buildings using drift-tribe-charged system search

被引:5
|
作者
Talatahari, Siamak [1 ,2 ]
Veladi, Hedayat [1 ]
Azizi, Mahdi [1 ]
Moutabi-Alavi, Ali [1 ]
Rahnema, Salar [1 ]
机构
[1] Univ Tabriz, Dept Civil Engn, Tabriz 51666, Iran
[2] Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia
关键词
drift-tribe-charged system search; optimum structural design; full-scale steel building; statistical analysis; PLANE-STRESS SYSTEMS; TRUSS STRUCTURES; OPTIMIZATION ALGORITHM; TOPOLOGY OPTIMIZATION; COST OPTIMIZATION; DAMPERS; IDENTIFICATION; GRAVITY; FRAMES; BEAMS;
D O I
10.1007/s11803-022-2116-1
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this paper, the potential of utilizing improved metaheuristic approaches in optimal design of building structures is concerned. In this regard, the drift-tribe-charged system search algorithm is proposed that the position and velocity updating processes of the charged system search is developed by implementing the mathematical presentation of the free-electron model utilized for metal conductors. In addition, the searching phase of the developed algorithm is also divided into three separate phases in order to improve the convergence capability of the algorithm. By means of these modifications, the exploitation and exploration rates of the standard algorithm are enhanced. In order to determine the ability of the proposed improved metaheuristic method considering some complex optimization problems, a 10-story steel building structure with 1026 structural members alongside a 60-story structure with 8272 members are utilized as numerical examples. The overall capability of the developed metaheuristic approach is compared with other metaheuristics. A total number of 30 independent runs have been conducted for each of the standard and proposed methods while a statistical analysis is also conducted for comparative purposes. The obtained optimum results demonstrated that the proposed metaheuristic approach is capable of preparing better outcomes than other metaheuristics.
引用
收藏
页码:825 / 842
页数:18
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