Optimized seismic design of planar and spatial steel frames using the hybrid learning based jaya algorithm. (September 2022)

• Record Type: Journal Article
• Authors: Degertekin, Sadik Ozgur; Tutar, Hikmet
• Is Part Of: Advances in engineering software. Volume 171(2022)
• Publication Date: 2022-09
• Subjects: Seismic design -- Structural optimization -- Planar/spatial steel frames -- Hybrid learning based jaya algorithm; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103172
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Three powerful metaheuristic algorithms are developed for optimum seismic design of steel frames. The optimization algorithms tested on three benchmark problems including large-scale spatial steel frames. Hybrid learning based jaya algorithm is the best optimizer in terms of minimum structural weight and computational cost. The performance of proposed algorithms is insensitive to randomly generated initial populations. Abstract: In this paper, three powerful metaheuristic algorithms, modified teaching learning based optimization, modified jaya algorithm and hybrid learning based jaya algorithm are presented for optimized seismic design of planar/spatial steel frames. The hybrid learning based jaya algorithm combines the learning phase of teaching learning based optimization and jaya algorithm. The goal is to design steel frames of minimum weight under strength, interstory drift and geometric-size constraints. One planar and two spatial steel frames previously designed for minimum weight by different metaheuristic optimization methods are taken as design examples to test the proposed methods. The methods presented in this paper are compared with other referenced algorithms in terms of optimized weight and computational cost. It is found that the hybrid learning based jaya algorithm outperforms modified teaching learning based optimization, modified jaya algorithm and its other competitors in all test problems.