Optimization of Large-Scale Frame Structures by Means of Improved Artificial Rabbits Optimization Algorithm

With the advancement of intelligent computing systems in recent decades, the optimization process of structures has been significantly improved. These systems provide accurate and fast analysis of complex structures and enable engineers to use more advanced and effective methods in the design and optimization of structures. This ability enables them to obtain optimal solutions in the shortest possible time by analyzing large and complex data. Thus, using intelligent computing systems accelerates and improves the accuracy of structural optimization. The main concern of this study is to investigate the applicability of the Artificial Rabbits Optimization (ARO) algorithm, as one of the recently developed metaheuristic algorithms, in the design optimization of large-scale frame structures. For numerical purposes, three frame structures are selected with different characteristics, an ۸-story, single bay frame structure; a ۱۵-story, ۳-bay benchmark frame structure; and a ۲۴-story, ۳-bay frame structure. In order to improve the overall computational performance of the standard ARO algorithm, an enhanced version of this algorithm is proposed as I-ARO by using the Diagonal Linear Uniform (DLU) initialization process instead of the conventional Brownian random initialization scheme. By comparing the results of I-ARO with those of other approaches in the literature, it can be concluded that the DLU process significantly upgrades the optimization capability of the standard ARO algorithm, such that the improved algorithm provides lower structural weight in the considered design examples.

کلیدواژه ها:

Optimum Design ، Improved Artificial Rabbits Optimization ، Diagonal Linear Uniform Initialization ، large-scale ، Frame structure