Structural optimization using evolutionary algorithms (original) (raw)
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1. Abstract In structural sizing optimization problems the aim is to minimize an objective function under certain constraints. The design variables of the optimization problem are generally discrete and belong to certain discrete sets. In the context of the present work the design set is implemented and efficiently treated as a database that contains the required geometric properties for all design variables considered in the optimization process. The proposed computational procedure, which is termed as multi-database cascade optimization, is implemented using an evolutionary optimization algorithm and the results prove that it can be an effective tool for handling large design spaces in the context of sizing optimization problems. 2. Keywords: evolutionary algorithms, sizing optimization, cascade, discrete databases.
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In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.
Advances in structural optimization
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Computer Methods in Applied Mechanics and Engineering, 2005
In discrete sizing optimization of truss and frame structures the design variables take values from databases, which are usually populated with a relatively small number of cross-section types and sizes. The aim of this work is to allow the use of large-size databases in discrete structural sizing optimization problems, in order to enrich the set of design variable options and increase the potential of achieving high-quality optimal designs. For this purpose, the concept of coarse database is introduced, according to which smaller-size versions of an appropriately ordered large database can be constructed. This concept is combined with the idea of cascading, which allows a single optimization problem to be tackled with a number of autonomous optimization stages. Under this context, several coarse versions of the same full-size database are formed, in order to utilize a different database in each cascade stage executed with an evolutionary optimization algorithm. The first optimization stages of the resulting multi-database cascade procedure make use of the coarsest database versions available and serve the purpose of basic design space exploration. The last stages exploit finer databases (including the original full-size database) and aim in fine tuning the achieved optimal solution. Based on the reported numerical results, multi-database cascading proves to be an effective tool for the handling of large databases and corresponding extensive design spaces in the framework of discrete structural sizing optimization applications. Ó 2005 Published by Elsevier B.V.
Structural Design Optimisation Using Genetic Algorithms and Neural Networks
This paper relates to the optimisation of structural design using Genetic Algorithms (GAs) and presents an improved method for determining the fitness of genetic codes that represent possible design solutions. Two significant problems that often hinder design optimisation using genetic algorithms are expensive fitness evaluation and high epistasis. Expensive fitness evaluation results in slow evolution and occurs when it is computationally expensive to test the effectiveness of possible design solutions using an objective function. High epistasis occurs when certain genes lose their significance or value when other genes change. Consequently, when a fit genetic code has an important gene changed this can have a dramatic effect on the fitness of that genetic code. Often the reduction in fitness results in failure of the genetic code being selected for reproduction and inclusion in the next generation. This loss of evolved genetic information can result in the solution taking consider...