基于NSGA-II算法的钢筋混凝土框架多目标优化

M. Babaei, Masoud Mollayi
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引用次数: 15

摘要

摘要近几十年来,利用遗传算法(GA)进行结构优化在混凝土和钢结构重量优化研究中备受关注。然而,对于钢筋混凝土和钢结构的多目标优化来说,如何确定目标函数之间的权衡关系并获得帕累托前面一直是一个挑战。在基于遗传算法的多目标优化方法中,非支配排序遗传算法II (non - dominant Sorting genetic Algorithm II, NSGA II)是最受欢迎的算法之一。本文介绍并研究了考虑成本和位移两个目标函数的钢筋混凝土抗弯矩框架结构多目标优化问题。采用NSGA-II算法对三种设计模型进行了优化。详细讨论了最优解的评定和算法过程。梁和柱的截面被认为是设计变量,美国混凝土规范…
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective optimization of reinforced concrete frames using NSGA-II algorithm
AbstractIn recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concret...
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