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Showing 7 results for Genetic Algorithms

Hossein Rahami, Ali Kaveh, M. Aslani, R. Najian Asl,
Volume 1, Issue 1 (3-2011)
Abstract

In this paper a hybrid algorithm based on exploration power of the Genetic algorithms and exploitation capability of Nelder Mead simplex is presented for global optimization of multi-variable functions. Some modifications are imposed on genetic algorithm to improve its capability and efficiency while being hybridized with Simplex method. Benchmark test examples of structural optimization with a large number of variables and constraints are chosen to show the robustness of the algorithm.
Y. Arfiadi, M.n.s. Hadi,
Volume 1, Issue 1 (3-2011)
Abstract

Tuned mass dampers (TMDs) systems are one of the vibration controlled devices used to reduce the response of buildings subject to lateral loadings such as wind and earthquake loadings. Although TMDs system has received much attention from researchers due to their simplicity, the optimization of properties and placement of TMDs is a challenging task. Most research studies consider optimization of TMDs properties. However, the placement of TMDs in a building is also important. This paper considers optimum placement as well as properties of TMDs. Genetic algorithms (GAs) is used to optimize the location and properties of TMDs. Because the location of TMDs at a particular floor of a building is a discrete number, it is represented by binary coded genetic algorithm (BCGA), whereas the properties of TMDS are best suited to be represented by using real coded genetic algorithm (RCGA). The combination of these optimization tools represents a hybrid coded genetic algorithm (HCGA) that optimizes discrete and real values of design variables in one arrangement. It is shown that the optimization tool presented in this paper is stable and has the ability to explore an unknown domain of interest of the design variables, especially in the case of real coding parts. The simulation of the optimized TMDs subject to earthquake ground accelerations shows that the present approaches are comparable and/or outperform the available methods.
A. Bagheria, G. Ghodrati Amirib, M. Khorasanib , J. Haghdoust,
Volume 1, Issue 4 (12-2011)
Abstract

The main objective of this study is to present new method on the basis of genetic algorithms for attenuation relationship determination of horizontal peak ground acceleration and spectral acceleration. The proposed method employs the optimization capabilities of genetic algorithm to determine the coefficients of attenuation relationships of peak ground and spectral accelerations. This method has been applied to 361 Iranian earthquake records with magnitudes between 4.5 and 7.4 obtained from two seismic zones, namely Zagros and Alborz-Central Iran. The obtained results indicated that the proposed method can be characterized as a powerful tool for prediction horizontal peak ground and spectral accelerations.
D.a. de Souza Junior, F.a.r. Gesualdo , Lívia M. P. Ribeiro,
Volume 2, Issue 2 (6-2012)
Abstract

This paper presents the study of the optimized bi-dimensional wood structures, truss type, applying the method of genetic algorithms. Assessment is performed by means of a computer program called OPS (Optimization of Plane Structures). The purpose is to meet the optimum geometric configuration taking into account the volume reduction. Different strategies are considered for the positioning of diagonals and struts in the upper chord. It is concluded that the trussed system efficiency depends on the dimensions and the position of the members, where the purlin’s location is not mandatory for struts and diagonal positions.
V. C. Castilho, M.c.v. Lima,
Volume 2, Issue 3 (7-2012)
Abstract

In the precast structures, optimization of structural elements is of great interest mainly due to a more rationalized way that elements are produced. There are several elements of precast prestressed concrete that are objects of study in optimization processes, as the prestressed joist applied in buildings slabs. This article inquires into cost minimization of continuous and simply supported slabs, formed by unialveolar beams and prestressed joist, using Genetic Algorithms (GAs). Comparative analyses of the final costs were made for these two precast elements, previously investigated in Castilho [1] and Castilho [2]. Furthermore, parcels of cost function were analyzed for the cases of prestressed joist and unialveolar beam, and the results show that the production stage of the element matches the largest part of the cost function. Also, although the prestressed joist is more economical, unialveolar beam reaches the market to compete with the other precast elements for slabs.
M. Mohebbi,
Volume 3, Issue 2 (6-2013)
Abstract

Tuned mass damper (TMD) have been studied and installed in structures extensively to protect the structures against lateral loads. Multiple tuned mass dampers (MTMDs) which include a number of TMDs with different parameters have been proposed for improving the performance of single TMDs. When the structural system is considered as multiple degrees of freedom (MDOF) and implemented with MTMDs, there is no effective closed-form solution to determine the optimal parameters of MTMDs. On the other hand designing optimal MTMDs include a large number of variables. For optimal design of MTMDs, in this research an effective method has been proposed in which the parameters of TMDs are determined based on minimizing the Hankel’s norm of structure. Since the optimization procedure includes a large number of variables, hence it has been decided to use Genetic Algorithms (GAs) for determining the variables. For numerical simulation, the method has been utilized on an eight-storey shear frame modeled as MDOF, and optimal MTMDs have been designed. The results show that using the Hankel’s norm of structure as objective function has led to design effective MTMDs which could be effective in reducing the response of structure, especially the average value, under different far-field and near-field earthquakes. Also it has been found that the method is effective regarding its simplicity and convergence in solving complex optimization problem. Through extensive numerical analysis the effect of MTMDs mass ratio and TMDs number in MTMDs has been studied.
A. Afshar , H.r. Zolfaghar Dolabi,
Volume 4, Issue 4 (11-2014)
Abstract

Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand time-cost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall safety risk (OSR). The present GA-based optimization model possesses significant features of Pareto ranking as selection criterion, elite archiving and adaptive mutation rate. In order to facilitate safety risk assessment in the planning phase, a qualitative activity-based safety risk (QASR) method is also developed. An automated system is codded as an Excel add-in program to facilitate the use of the model for practitioners and researchers. The model has been implemented and verified on a case study successfully. Results indicate that integration of safety risk assessment methods into multi-objective TCO problem improves OSR of nondominated solutions. The robustness of the present optimization model has also been proved by its great ability to prevent genetic drift as well as the improvement in the bicriteria among generations.

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