Wyniki wyszukiwania - BazTech

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Application of self-adaptive population RAO algorithms to optimization of steel grillage structures

Grzywiński Maksym, Dede Tayfun, Atmaca Barbaros

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 4

505--520

EN

The self-adaptive population Rao algorithms (SAP-Rao) are employed in this study to produce the optimal designs for steel grillage structures. The size variables in the optimization problem consist of the cross-sectional area of the discrete W-shapes of these beams. The LRFD-AISC design code was used to optimize the constrained size of this kind of structure. The solved problem’s primary goal is to determine the grillage structure’s minimum weight. As constraints, it is decided to use the maximum stress ratio and the maximum displacement at the inner point of the steel grillage structure. The finite element method (FEM) was employed to compute the moment and shear force of each member, as well as the joint displacement. A computer program for the study and design of grillage structures, as well as the optimization technique for SAP-Rao, was created in MATLAB. The outcomes of this study are compared to earlier efforts on grillage structures. The findings demonstrate that the optimal design of grillage structures can be successfully accomplished using the SAP-Rao method described in this paper.

2

Branch and bound algorithm for discrete multi- level linear fractional programming problem

Arora R., Gupta K.

Operations Research and Decisions

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2018

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Vol. 28, No. 2

5--21

EN

An algorithm is proposed to find an integer solution for bilevel linear fractional programming problem with discrete variables. The method develops a cut that removes the integer solutions which are not bilevel feasible. The proposed method is extended from bilevel to multilevel linear fractional programming problems with discrete variables. The solution procedure for both the algorithms is elucidated in the paper.

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Optymalizacja kratownicy z zastosowaniem algorytmu genetycznego

Grzywiński M.

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

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2018

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Vol. 7 Nr 2

117--122

PL

W artykule zaproponowano algorytm genetyczny do rozwiązania problemu minimalizacji masy płaskiej kratownicy, biorąc pod uwagę zmienność pola przekroju. Minimalna masa konstrukcji stalowej to też niska emisja CO2. Konstrukcja jest zoptymalizowana za pomocą wydajnego algorytmu zwanego Teaching Learning Based Optimization. Proces TLBO jest podzielony na dwie części: pierwsza składa się z "fazy nauczyciela", a druga składa się z "fazy ucznia". Obliczenia wykonywane są z pomocą programu metody elementów skończonych zakodowanym w MATLAB-ie.

EN

The article proposes a genetic algorithm for solving the problem of minimizing the mass of a plane truss, taking into account the variability of the cross-sectional area. The minimum mass of the steel structure is also low CO2 emission. The design is optimized using an efficient algorithm called Teaching Learning Based Optimization. The TLBO process is divided into two parts: the first consists of the "teacher phase" and the second consists of the "student phase". The calculations are performed with the help of the finite element method program coded in MATLAB.