Optimum design of unbraced rigid frames

Saka, M.P.; Kameshki, E. S.

doi:10.1016/s0045-7949(98)00117-5

Cited by 30 publications

(17 citation statements)

References 10 publications

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“…Saka [24] optimized a frame design with tapered members thereby firstly computing the member responses under external static loadings and combining them by Lagrange multipliers to generate depth variables. Saka and Kameshki [25] used OCM for design optimization of unbraced rigid frames considering constraints imposed by sway deflections and member stresses.…”

Section: Preliminary Studiesmentioning

confidence: 99%

“…In order to investigate the relation between two parameters, the first parameter values are specified as "50, 20, 20, and 25," while the values of second parameter are fixed by "20, 50, 20, and 15." Thus, four parameter sets, namely (50,20), (20,50), (20,20), and (25,15), are devised for design tests.…”

Section: Design Detailsmentioning

confidence: 99%

“…(iii) The best optimal designations are obtained when using parameters NGGES and NSBS set (25,15), (40,15), and (40,5) for each design example.…”

Section: Consideration Of Variation In the Values Of Evolutionarymentioning

confidence: 99%

“…The parameters NSBS and NSAS are indicative of the activated frequency of gradual exploration strategy (Tables 3, 7, and 10). Considering parameter value sets with better performance, the value set of output NSAS corresponding to the value set of parameter NGGES (25,40,40) are (1,2,1). frequency of the gradual exploration strategy.…”

Section: Consideration Of Variation In the Values Of Evolutionarymentioning

confidence: 99%

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A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Talaslioglu

2009

Modelling and Simulation in Engineering

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A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

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Section: Preliminary Studiesmentioning

confidence: 99%

Section: Design Detailsmentioning

confidence: 99%

“…(iii) The best optimal designations are obtained when using parameters NGGES and NSBS set (25,15), (40,15), and (40,5) for each design example.…”

Section: Consideration Of Variation In the Values Of Evolutionarymentioning

confidence: 99%

Section: Consideration Of Variation In the Values Of Evolutionarymentioning

confidence: 99%

See 2 more Smart Citations

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Talaslioglu

2009

Modelling and Simulation in Engineering

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“…The OC methods were developed on the basis of the contributions by several researchers in the 1960s and early 1970s such as Barnett [2], Prager and Shield [3] and Venkayya et al [4]. In the recent years, the OC approach to the optimization of steel frames was proposed by Chan et al [5], Soegiarso and Adeli [6], Saka and Kameshki [7]. While the Karush-Kuhn-Tucker conditions ensure the requirements for the optimal solution, the Lagrange multipliers are applied to comprise the constraints.…”

Section: Introductionmentioning

confidence: 99%

Minlp Optimization of Steel Frames

Klanšek¹,

Žula²,

Kravanja³

et al. 2007

Volume 3 Number 3

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ABSTRACT:The paper presents the discrete dimension optimization of unbraced rigid steel plane frames. The optimization of steel frames was carried out by the Mixed-Integer Non-linear Programming (MINLP) approach. The MINLP is a combined discrete-continuous optimization technique. It performs the discrete optimization of discrete decisions simultaneously with the continuous optimization of continuous parameters. The task of the optimization is to minimize the mass of the frame structure and to find the optimal discrete sizes of standard steel sections for frame members. The finite element equations are defined as the equality constraints for the second-order elastic structural analysis. The design constraints for the steel members were formulated according to Eurocode 3. The Modified Outer-Approximation/ Equality-Relaxation algorithm and a two-phase MINLP optimization approach were applied for the optimization. The latter starts with the continuous optimization of the frame, while the standard dimensions are temporarily relaxed into continuous parameters. When the optimal continuous solution is found, standard sizes of cross-sections are re-established and the simultaneous continuous and discrete dimension optimization by MINLP is then continued until the optimal solution is found. A numerical example of the optimization of a steel frame is presented at the end of the paper to show the suitability of the proposed approach.

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Optimum Design of Structures with Group Search Optimizer Algorithm

Li¹,

Liu²

2011

Adaptation, Learning, and Optimization

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Optimum design of unbraced rigid frames

Cited by 30 publications

References 10 publications

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Minlp Optimization of Steel Frames

Optimum Design of Structures with Group Search Optimizer Algorithm

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