Discrete Optimization of Truss Structure Using Genetic Algorithm (original) (raw)
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Weight optimization of truss structures by using genetic algorithms
Rakenteiden Mekaniikka, 2022
Lightweight structures, especially trusses, have attracted a tremendous attention due to their extensive applications in the construction of infrastructures. Optimizing the shape and crosssectional topology of truss members is essential since the truss systems are widely used in engineering routines. These systems form the framework of structures like bridges, steel halls for industry and trade, and towers. For the scope of this research, genetic algorithms (GAs) were used for weight optimization of truss structures. This paper aims to optimize truss structures for finding optimal crosssectional area. To optimize the cross-sectional area, all members were selected as design variables, with the structure's weight being the objective function. The restrictions related to the change of the location of the nodes and the tension in the members were the looked-upon problems, the permissible values of which were determined under the circumstances of the problem. In addition, the resulting optimized model which masses for sizing, shape, and topology or their combinations, were compared.
Weight Minimization of Spatial Trusses with Genetic Algorithm
Quality Production Improvement - QPI
A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.
Optimization of Plane and Space Trusses Using Genetic Algorithms
2014
weight optimization of trusses is so important due to economic and sustainability considerations. Geometry, topology and sizing optimization is extensively found in literature. Applications found in literature uses the traditional deign variables containing node coordinates, elements connectivity and member cross sections. This paper presents an approach based on the genetic algorithm for optimum design of plane and space trusses subjected to specified set of constraints. The proposed approach defined innovative design variables in terms of node coordinates and displacements. Such limited design variables lead to the reduction of genotype length resulting in less execution time. Topology and cross sections are estimated after using strength criteria. The proposed approach was applied on benchmark problems repeated in literature, the proposed approach resulted in more optimized results with less mathematical effort.
Genetic algorithm for discrete-sizing optimal design of trusses using the force method
International Journal for Numerical Methods in Engineering, 2002
In the process of discrete-sizing optimal design of truss structures by Genetic Algorithm (GA), analysis should be performed several times. In this article, the force method is employed for the analysis. The advantage of using this method lies in the fact that the matrices corresponding to particular and complementary solutions are formed independently of the mechanical properties of members. These matrices are used several times in the process of the sequential analyses, increasing the speed of optimization. The second feature of the present method is the automatic nature of the prediction of the useful range of sections for a member from a list of profiles with a large number of cross-sections. The third feature consists of a contraction process developed to increase the efficiency of the GA by which an optimal design for the first sub-string associated with member cross-sections is obtained. Improved designs are achieved in subsequent cycles by reducing the length of sub-strings. Copyright © 2002 John Wiley & Sons, Ltd.
Multi-Objective Two-Dimensional Truss Optimization by using Genetic Algorithm
IPTEK The Journal for Technology and Science, 2011
During last three decade, many mathematical programming methods have been develop for solving optimization problems. However, no single method has been found to be entirely efficient and robust for the wide range of engineering optimization problems. Most design application in civil engineering involve selecting values for a set of design variables that best describe the behavior and performance of the particular problem while satisfying the requirements and specifications imposed by codes of practice. The introduction of Genetic Algorithm (GA) into the field of structural optimization has opened new avenues for research because they have been successful applied while traditional methods have failed. GAs is efficient and broadly applicable global search procedure based on stochastic approach which relies on "survival of the fittest" strategy. GAs are search algorithms that are based on the concepts of natural selection and natural genetics. On this research Multi-objective sizing and configuration optimization of the two-dimensional truss has been conducted using a genetic algorithm. Some preliminary runs of the GA were conducted to determine the best combinations of GA parameters such as population size and probability of mutation so as to get better scaling for rest of the runs. Comparing the results from sizing and sizing-configuration optimization, can obtained a significant reduction in the weight and deflection. Sizing-configuration optimization produces lighter weight and small displacement than sizing optimization. The results were obtained by using a GA with relative ease (computationally) and these results are very competitive compared to those obtained from other methods of truss optimization.
Engineering structures need to satisfy certain criteria such that it may function properly. This paper presents the results of a study on trusses which need to satisfy optimal conditions, i.e. lowest cost possible with maximal performance. The trusses considered were statically indeterminate steel structures with multi-system of loading. The cost is here represented by the material volume of the structure and the maximal performance is reflected by the high working stresses within allowable stress limits. The material strength was modeled as a random variable with a Log Normal distribution. Beside stresses, the structures are also required to meet a failure probability of P f =10 -3 , which may occur locally within the elements as well as globally on the structure as a whole. The complexity of optimization problems depends in general on the number of the considered variables. The larger the number of variables considered, the more complicated becomes the solution process. Therefore, cases of single variable elements as well as multi variables ones were considered in this study. Optimization problems are usually solved applying iterative procedures, frequently resorting to mathematical programming. In these procedures the process usually converges to unreliable solutions; it even may completely bogged down with no solution at all. To circumvent this problem, iteration was carried out applying Genetic Algorithms where the process proceeds in a stochastic manner. Genetic Algorithms usually deliver reliable solutions.
Optimization of Steel Truss Using Genetic Algorithm
Passer journal of basic and applied sciences, 2024
In this paper, an optimization study is presented, focusing on steel trusses. The main goal of this study is to reduce the weight of truss structures using a Genetic Algorithm (GA), which is a widely acknowledged evolutionary-based method known for its efficiency in solving intricate optimization problems. The design problem formulation takes into account various constraints, such as displacement, tensile stress, and minimum size requirements. These constraints are implemented in MATLAB, utilizing the ANSI/AISC 360-16 Specification as a guideline for designing tension and compression members. To determine the optimal design, the approach involves considering discrete design variables. This is achieved by selecting sections from a database containing all available steel sections specified in the AISC Steel Construction Manual, ensuring practical and feasible design solutions. The efficiency of the algorithm is validated through its application to several plane truss types. Through a comparison of the outcomes obtained from the proposed algorithm with the results generated by CSI-ETABS software, it is demonstrated that this approach consistently yields better weight optimization. Overall, the study showcases the effectiveness of the GA-based algorithm in optimizing the weight of steel trusses. The results and implications of the findings are thoroughly discussed in the paper; this study has the potential to make a substantial contribution to the field of structural optimization and design.
Sizing and Topology Optimization of Trusses Using Genetic Algorithm
Materials
Genetic algorithms are a robust method for a solution of wide variety optimization problems. It explores a big space of design variables in order to find the best solution. From the point of view of a user, the algorithm requires the encoding of design variables into the form of strings and the procedure of optimization uses them for optimization. Here, for the structural engineer, it is crucial to find the form of objective function including the constraints of the task and also to avoid critical states during the solution of structural responses. This paper presents the use of genetic algorithm for solving truss structures. The use of genetic algorithm approach is shown on three cases of truss structures.
Weight Optimization of Square Hollow Steel Trusses Using Genetic Algorithm
IOP Conference Series: Materials Science and Engineering
Conceptual design in structural engineering entails a large amount of trial and errors or extensive expertise to obtain the most economical and functional design solutions for large engineering projects. In this paper a modern optimization technique called Genetic algorithm, adopting its concept from genetic evolution is used to optimize the shape, size and topology of a plane truss structure with the aim of minimizing the total weight of the truss. A genetic algorithm developed in MATLAB was implemented in this paper to optimize the weight of plane truss structures. The objective function of the optimization problem is subjected to constraints such as stress limits, buckling constraints, tension and compression capacity according to British steel design code BS 5950. The plane trusses which were subject to point loads were tested in the genetic algorithm, the resulting optimized truss structures were then subject to real life loading to determine their feasibility to withstand real life loading. The optimized trusses presented by the algorithm were modelled in a structural analysis and design software called SAP 2000, where they were subjected to dead and live loads. After design the weight saving discovered between the original trusses and the optimized version was between 37-47%. The results show that the genetic algorithm implemented in this study is useful in optimizing the weight of a plane truss structure.
Multi-objective Topology Optimization of 2D Trusses using Genetic Algorithms
Topology optimization of truss structures is a strategic factor in several branches of the industry. The last developments with Genetic algorithms (GA) in order to achieve the minimum weight of the structures are encouraging. However, there have been problems: the high rate of unfeasible individuals, the computational high costs, and slow convergence. The most of these techniques follow two approaches: 1) Assuming a ground structure (a complete truss formed by all possible connections between nodes) and 2) Distributing material in the design domain through a binary representation (empty / full of material). In this paper we proposed a technique with a totally different approach from the representation; instead of nodes, bars or portions of matter in the space, we codify instructions of assemblage that are read by a "construction algorithm ", which based on pre-established rules, constructs the truss. Besides, this technique does not appeal to the formulation of maximum rigidity. We use the "Matrix Method of Calculation of Trusses with Articulated Nodes" instead of Finite Elements. The numeric experiments on test instances prove that the proposed method reduces computational costs and lessens weight of trusses.