Optimum design of building structures using Tribe-Interior Search Algorithm. (December 2020)

• Record Type: Journal Article
• Authors: Talatahari, Siamak; Azizi, Mahdi
• Is Part Of: Structures. Volume 28(2021)
• Page Start: 1616
• Page End: 1633
• Publication Date: 2020-12
• Subjects: Optimum design -- Building structure -- Tribe-Interior Search Algorithm -- Metaheuristic -- Optimization; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2020.09.075
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: Many different optimization algorithms have been proposed for specific applications and each of them has some advantages and disadvantages. The Interior Search Algorithm is one of the recently proposed metaheuristic algorithms which is conceptualized based on aesthetic techniques utilized in the interior decoration and design. Based on the multiple applications of this algorithm in different optimization fields, there has been a growing attention in improving the general performance of this algorithm. In this paper, the Tribe-Interior Search Algorithm is proposed in which the searching phase of the standard algorithm is divided into three different phases. These phases which are considered as tribes cause the algorithm to focus on global searching in the early iterations, while the local searching is dealt with in the later iterations. By means of these modifications, the exploitation and exploration rates of the standard algorithm are enhanced. For performance evaluation of the proposed algorithm, two benchmark frame optimization problems with 15- and 24-stories alongside a 10-story steel building structure with 1026 structural members and a 60-story structure with 8272 members are considered as design examples. The overall performance of the proposed Tribe-Interior Search-based algorithm is compared to the standard Interior Search Algorithm and some other metaheuristic approaches. Based on the results, the proposed algorithm has the ability of providing better results for the benchmark and real-size building structures than the other metaheuristics.