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.

我们提出了一种新的算法，称为非对称遗传算法（AGA），用于解决钢框架的优化问题。AGA由完善的惩罚功能组成，可帮助找到最合适的种群。目标功能是在美国钢结构学会（AISC）为钢结构建筑定义的极限载荷的约束下，使整个钢结构的重量最小。设计变量是从AISC提供的一组侧翼型钢截面中选择的元素（梁和柱）的横截面积。有限元方法（FEM）用于分析钢框架的性能。在这项研究中，AGA对15层高的三层钢平面框架进行了优化，之前已通过诸如粒子群优化（PSO），带有被动会聚的粒子群优化器（PSOPC），粒子群蚁群优化（HPSACO），帝国主义竞争算法（ICA）和收费系统搜索（CSS）之类的算法进行了优化。将AGA的结果（例如结构的总重量和分析次数）与这些算法的结果进行比较。与这些算法相比，AGA在总权重和分析次数方面表现更好。此外，通过AGA，遗传算法（GA）和SAP2000优化模块对五个数值示例进行了优化，并对其结果进行了比较。结果表明，AGA可以减少分析时间，分析次数和结构总重量。