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Showing 47 results for Genetic Algorithm

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.
M. Shahrouzi,
Volume 1, Issue 1 (3-2011)
Abstract

Earthquake time history records are required to perform dynamic nonlinear analyses. In order to provide a suitable set of such records, they are scaled to match a target spectrum as introduced in the well-known design codes. Corresponding scaling factors are taken similar in practice however, optimizing them reduces extra-ordinary economic charge for the seismic design. In the present work a new hybrid meta-heuristic is developed combining key features from genotypic search and particle swarm optimization. The method is applied to an illustrative example via a parametric study to evaluate its effectiveness and less probability of premature convergence compared with the standard particle swarm optimization.
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.
S. Shojaee, S. Hasheminasab,
Volume 1, Issue 2 (6-2011)
Abstract

Although Genetic algorithm (GA), Ant colony (AC) and Particle swarm optimization algorithm (PSO) have already been extended to various types of engineering problems, the effects of initial sampling beside constraints in the efficiency of algorithms, is still an interesting field. In this paper we show that, initial sampling with a special series of constraints play an important role in the convergence and robustness of a metaheuristic algorithm. Random initial sampling, Latin Hypercube Design, Sobol sequence, Hammersley and Halton sequences are employed for approximating initial design. Comparative studies demonstrate that well distributed initial sampling speeds up the convergence to near optimal design and reduce the required computational cost of purely random sampling methodologies. In addition different penalty functions that define the Augmented Lagrangian methods considered in this paper to improve the algorithms. Some examples presented to show these applications.
M.h. Afshar, I. Motaei,
Volume 1, Issue 2 (6-2011)
Abstract

A constrained version of the Big Bang-Big Crunch algorithm for the efficient solution of the optimal reservoir operation problems is proposed in this paper. Big Bang-Big Crunch (BB-BC) algorithm is a new meta-heuristic population-based algorithm that relies on one of the theories of the evolution of universe namely, the Big Bang and Big Crunch theory. An improved formulation of the algorithm named Constrained Big Bang-Big Crunch (CBB-BC) is proposed here and used to solve the problems of reservoir operation. In the CBB-BC algorithm, all the problems constraints are explicitly satisfied during the solution construction leading to an algorithm exploring only the feasible region of the original search space. The proposed algorithm is used to optimally solve the water supply and hydro-power operation of “Dez” reservoir in Iran over three different operation periods and the results are presented and compared with those obtained by the basic algorithm referred to here as Unconstrained Big Bang–Big Crunch (UBB–BC) algorithm and other optimization algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) and those obtained by Non-Linear Programming (NLP) technique. The results demonstrate the efficiency and robustness of the proposed method to solve reservoir operation problems compared to alternative algorithms.
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.
S. Kazemzadeh Azad , S. Kazemzadeh Azad, A. Jayant Kulkarni,
Volume 2, Issue 1 (3-2012)
Abstract

The present study is an attempt to propose a mutation-based real-coded genetic algorithm (MBRCGA) for sizing and layout optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in the design space. The standard deviation of design variables is used as a key factor in the adaptation of mutation operators. The reliability of the proposed algorithm is investigated in typical sizing and layout optimization problems with both discrete and continuous design variables. The numerical results clearly indicated the competitiveness of MBRCGA in comparison with previously presented methods in the literature.
P. Valli, C. Antony Jeyasehar,
Volume 2, Issue 2 (6-2012)
Abstract

Equipment selection is a key factor in modern construction industry. As it is a complex factor, current models offered by literatures fail to provide adequate solutions for major issues like systematic evaluation of soft factors and weighting of soft benefits in comparison with costs. This paper aims at making a comparative study between GA and AHP by utilising MATLAB as a tool. It is a convenient tool offering an orderly methodical thinking. It guides them in making consistent decisions and provides a facility for all necessary computation.
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.
S. Gerist, S.s. Naseralavi , E. Salajegheh,
Volume 2, Issue 2 (6-2012)
Abstract

In damage detection the number of elements is generally more than the number of measured frequencies. Consequently, the corresponding damage detection equation is undetermined and thus has infinite solutions. Since in the damaged structures most of their elements remain healthy, the sparsest solution for the damage detection equation is mostly the actual damage. In the proposed method, the damage equation is first linearized in various ways using random finite difference increments. The sparsest solutions for created linear system of equations are derived using basis pursuit. These solutions are considered as the first population for a continuous genetic algorithm to obtain the damage solution. For investigation of the proposed method three case studies are considered. Simulation results confirm the efficiency of the proposed method compared to those found in the literature.
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. Rajabi Bahaabadi, A. Shariat Mohaymany, M. Babaei,
Volume 2, Issue 4 (10-2012)
Abstract

Crossover operator plays a crucial role in the efficiency of genetic algorithm (GA). Several crossover operators have been proposed for solving the travelling salesman problem (TSP) in the literature. These operators have paid less attention to the characteristics of the traveling salesman problem, and majority of these operators can only generate feasible solutions. In this paper, a crossover operator is presented that has the capability of generating solutions based on a logical reasoning. In other words, the solution space is explored by the proposed method purposefully. Numerical results based on 26 benchmark instances demonstrate the efficiency of the proposed method compared with the previous meta-heuristic methods.
M. Shahrouzi , A. Yousefi,
Volume 3, Issue 1 (3-2013)
Abstract

Meta-heuristics have already received considerable attention in various engineering optimization fields. As one of the most rewarding tasks, eigenvalue optimization of truss structures is concerned in this study. In the proposed problem formulation the fundamental eigenvalue is to be maximized for a constant structural weight. The optimum is searched using Particle Swarm Optimization, PSO and its variant PSOPC with Passive Congregation as a recent meta-heuristic. In order to make further improvement an additional hybrid PSO with genetic algorithm is also proposed as PSOGA with the idea of taking benefit of various movement types in the search space. A number of benchmark examples are then treated by the algorithms. Consequently, PSOGA stood superior to the others in effectiveness giving the best results while PSOPC had more efficiency and the least fit ones belonged to the Standard PSO.
K. Shakeri,
Volume 3, Issue 2 (6-2013)
Abstract

In recent years some multi-mode pushover procedures taking into account higher mode effects, have been proposed. The responses of considered modes are combined by the quadratic combination rules, while using the elastic modal combination rules in the inelastic phases is not valid. Here, an optimum weighted mode combination method for nonlinear static analysis is presented. Genetic algorithm is used for optimization of the modal weight. The proposed procedure is applied for a sample building. The results show that the resulted response from the proposed method has minimal error in comparison with the response of the nonlinear time history analysis.
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.
S. Gholizadeh, R. Kamyab , H. Dadashi,
Volume 3, Issue 2 (6-2013)
Abstract

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative process needed to meet code requirements. In the PBDO procedure, the metaheuristics minimize the structural weight subjected to performance constraints on inter-story drift ratios at various performance levels. Two numerical examples are presented demonstrating the superiority of the PSO to the GA, ACO and HS metaheuristic algorithms.
H. Fattahi, S. Shojaee, M A. Ebrahimi Farsangi, H. Mansouri,
Volume 3, Issue 3 (9-2013)
Abstract

The excavation damaged zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin hypercube sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.
F. Zahedi Tajrishi, A. R. Mirza Goltabar Roshan,
Volume 4, Issue 1 (3-2014)
Abstract

This paper is concerned with the determination of optimal sensor locations for structural modal identification in a strap-braced cold formed steel frame based on an improved genetic algorithm (IGA). Six different optimal sensor placement performance indices have been taken as the fitness functions two based on modal assurance criterion (MAC), two based on maximization of the determinant of a Fisher information matrix (FIM), one aim on the maximization of the modal energy and the last is a combination of two aforementioned indices. The decimal two-dimension array coding method instead of binary coding method is applied to code the solution. Forced mutation operator is applied whenever the identical genes produce via the crossover procedure. An improvement is also introduced to mutation operator of the IGA. A verified computational simulation of a strap-braced cold formed steel frame model has been implemented to demonstrate the effectiveness and application of the proposed method. The obtained optimal sensor placements using IGA are compared with those gained by the conventional methods based on several criteria such as norms of FIM and minimum in off-diagonal terms of MAC. The results showed that the proposed IGA can provide sensor locations as well as the conventional methods. More important, based on the criteria, four of the six fitness functions, can identify the vibration characteristics of the frame model accurately. It is shown through the example that in comparison with the MAC-based performance indices, the use of the FIM-based fitness functions results in more acceptable and reasonable configurations.
M. Mohebbi, S. Moradpour , Y. Ghanbarpour,
Volume 4, Issue 1 (3-2014)
Abstract

In this research, optimal design and assessment of multiple tuned mass dampers (MTMDs) capability in mitigating the damage of nonlinear steel structures subjected to earthquake excitation has been studied. Optimal parameters of TMDs on nonlinear multi-degree-of-freedom (MDOF) structures have been determined based on minimizing the maximum relative displacement (drift) of structure where for solving the optimization problem the genetic algorithm (GA) has been used successfully. For numerical analysis, three and nine storey 2-D moment resisting nonlinear steel frames subjected to far-field and near-field earthquakes and optimal MTMDs has been designed for different values of mass ratio and TMDs number. According to the results of numerical simulations, it can be said that MTMDs mechanism could reduce the damage of nonlinear steel structures where the effectiveness increases by increasing TMDs mass ratio. Also the performance of MTMDs depends on earthquake characteristics, mass ratio and TMDs configuration where in this research the effective case has been locating TMDs on top floor in parallel configuration.
M. Mohebbi , A. Bagherkhani,
Volume 4, Issue 3 (9-2014)
Abstract

In the area of semi-active control of civil structures, Magneto-Rheological (MR) damper has been an efficient mechanism for reducing the seismic response of structures. In this paper, an effective method based on defining an optimization problem for designing MR dampers has been proposed. In the proposed method, the parameters of semi-active control system are determined so that the maximum response of structure is minimized. To solve the optimization problem, the Genetic algorithm (GA) has been utilized. The modified Bouc-Wen model has been used to represent the dynamic behavior of MR damper while to determine the input voltage at any time step, the clipped optimal control algorithm with LQR controller has been applied. To evaluate the performance of the proposed method, a ten-storey shear frame subjected to the El-Centro excitation and for two different kinds of objective functions, optimal MR dampers have been designed. Then the performance of optimal MR damper has been tested under different excitations. The results of the numerical simulations have shown the effectiveness of the proposed method in designing optimal MR dampers that have the capability of reducing the response of the structures up to a significant level. In addition, the effect of selecting a proper objective function to achieve the best performance of MR dampers in decreasing different responses of structure has been shown.

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