Optimum design of planar steel frames using the Search Group Algorithm

Carraro, Felipe; López, Rosalía Rodríguez; Miguel, Leandro Fleck Fadel

doi:10.1007/s40430-016-0628-1

Cited by 12 publications

(7 citation statements)

References 37 publications

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“…The maximum interaction ratio and interstory drift of the optimum design obtained by JA are 0.95 and 0.479 in (1.33 cm). Although SGA [18] and EHBMO [6] found lighter designs weighing of 194508 lb (88227 kg) and 188640 lb (85565 kg) respectively, these designs violate constraints at high rates as %34 and %1766.…”

Section: Three-bay Twenty-four Story Framementioning

confidence: 94%

“…3. The frame was originally designed by Davison and Adams [31], later optimized by PSO [13], HS [14], SGA [18], HBMO and EHBMO [6], WOA and EWOA [8] and SBO [19]. The material modulus of elasticity is 29782 ksi (205340 MPa) and the yield stress is taken as 33.4 ksi (230.3 MPa).…”

Section: Three-bay Twenty-four Story Framementioning

confidence: 99%

“…al. [18] implemented a search group algorithm (SGA) to three steel frame examples in order to investigate the efficiency of the method. The results demonstrated that the proposed method achieved competitive performance.…”

Section: Introductionmentioning

confidence: 99%

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Jaya algorithm for design optimization of planar steel frames

Ugur¹,

Degertekin²

2021

DÜMF Mühendislik Dergisi

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An efficient metaheuristic optimization method called Jaya Algorithm (JA) has gained wide acceptance among optimization researchers in various engineering problems recently. The main feature of JA is that it does not use algorithm-specific parameters and has a very simple formulation based on the concept of approaching the best solution and moving away from the worst solution. This study presents the JA formulation for design optimization of planar steel frames under strength and displacement constraints. The validity of JA is investigated by solving two benchmark design examples. The results demonstrated the superiority of JA over other state-of-the-art metaheuristic optimization methods in terms of optimized weight, number of structural analyses and several statistical parameters.

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Section: Three-bay Twenty-four Story Framementioning

confidence: 94%

Section: Three-bay Twenty-four Story Framementioning

confidence: 99%

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Jaya algorithm for design optimization of planar steel frames

Ugur¹,

Degertekin²

2021

DÜMF Mühendislik Dergisi

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“…Notably, SGA has the advantage of striking a good balance between exploitation and exploration, providing powerful searchability for finding the optimum solution. Several recent studies have been done to verify the SGA applicability for various optimization problems such as truss optimal voltage regulation in power systems [45], automatic generation control [46], networked control system [47], steel frames optimization [48], and structure optimization [49]. Moreover, SGA has not been applied to deal with the MOOPF problem with the stochastic RESs.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Multiobjective Optimal Power Flow-Based Renewable Energy Sources Using Intelligent Algorithm

Huy¹,

Nguyen

Nor

et al. 2022

IEEE Access

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“…However, the drawbacks of metaheuristic algorithms are approximate and non-deterministic, they do not guarantee for (leaving alone global one) optimum solution in views of the intrinsic non-convex and non-smooth optimization problems. Numerous alternative meta-heuristic techniques have employed for solving the optimization of steel frames, some of the well-known methods being a genetic algorithm (GA) (Pezeshk, Camp, & Chen, 2000), ant colony optimization (ACO) (C. V. Camp, Bichon, & Stovall, 2005), harmony search (HS) algorithm (Degertekin, 2008), teaching learning-based optimization (TLBO) (Toğan, 2012), particle swarm optimization (PSO) (Doğan & Saka, 2012), charge system search (A Kaveh & Talatahari, 2012), cuckoo search (CS) algorithm (A Kaveh & Bakhshpoori, 2013), firefly algorithm (FFA) (Carbas, 2016), search group algorithm (SGA) (Carraro, Lopez, & Miguel, 2017), a school-based optimization (SBO) (Farshchin, Maniat, Camp, & Pezeshk, 2018).…”

Section: List Of Tablesmentioning

confidence: 99%

Analysis and optimum design of cold-formed steel arch structures

Van

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This research presents the development of a unified framework for cold-formed steel warehouse design at variations of clear roof spans. The work involves the advanced finite element analyses of nonlinear arch-shape warehouse structures, namely one that considers 2nd-order nonlinear geometry effects-sufficiently accurate approximation of large deformation responses. An optimum design procedure of 3D arch steel warehouse structures is conducted with the help of a novel algorithm, called mixed ESO-PSO approach. The generic idea is based on the implementation of a well-known evolutionary structural optimization algorithm (ESO), and further integrates a mapping, underpinning particle swarm optimization (PSO), technique of design variables to the closest upper or lower integer numbers listed in the available steel sections. In essence, the use of ESO algorithm during the preliminary design provides good initial points by eliminating those ineffective or even infeasible design domains, and hence reduces the sizes of discrete variable entries prior to performing the PSO searches in the final step. The objective function is to minimize suitable arch geometry that yields minimum cold-formed steel hollow sections employed specifically in warehouse applications. Both ultimate strength and serviceability criteria of designed structures comply with AISC-LRFD and AISI-LRFD specifications. The algorithm and various constraints were coded within a Microsoft Visual Basic environment interfaced with the commercial SAP2000 structural analysis codes by direct OAPI communication. Whilst there is still no guarantee for (leaving alone the global one) optimal solution in view of the intrinsic non-convex and non-smooth optimization problems, a number of successfully solved benchmarks highlighted efficiency and accuracy of the present design method in steel structure applications. The optimum procedure is automatically performed as an iterative process, and incorporated advanced analysis until the minimum cross-sections obtained without violating its given constraints.

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Optimum design of planar steel frames using the Search Group Algorithm

Cited by 12 publications

References 37 publications

Jaya algorithm for design optimization of planar steel frames

Jaya algorithm for design optimization of planar steel frames

Performance Improvement of Multiobjective Optimal Power Flow-Based Renewable Energy Sources Using Intelligent Algorithm

Analysis and optimum design of cold-formed steel arch structures

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