Optimal sizing, geometrical and topological design using a genetic algorithm

Grierson, Donald E.; Pak, Wooguil

doi:10.1007/bf01743506

Cited by 114 publications

(37 citation statements)

References 6 publications

(5 reference statements)

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“…One good example is structural design optimization [84], [110], [120], [145], [149], [156], [188]. In aerodynamic design optimization, it is often necessary to carry out computational fluid dynamics (CFDs) simulations to evaluate the performance of a given structure.…”

Section: A Motivationsmentioning

confidence: 99%

“…If we assume the prediction of the meta-model is better than random guess, which is a very weak assumption on the quality of the meta-model, it is natural to choose the best individuals, that is, the more promising ones, to be reevaluated using the real fitness function [42], [84], [108], [111]. Usually, the number of individuals to be reevaluated is predefined and fixed during the evolution.…”

Section: ) Individual-based Evolution Controlmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Optimization in Uncertain Environments—A Survey

Jin

Branke

2005

IEEE Trans. Evol. Computat.

1,382

759

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Abstract-Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.

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Section: A Motivationsmentioning

confidence: 99%

Section: ) Individual-based Evolution Controlmentioning

confidence: 99%

Evolutionary Optimization in Uncertain Environments—A Survey

Jin

Branke

2005

IEEE Trans. Evol. Computat.

1,382

759

View full text Add to dashboard Cite

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“…Considering the non-linearity, the non-convexity and the discontinuity of this optimization problem, we have chosen to use a stochastic method that is a standard genetic algorithm (GA) described in Reference [5]. GAs are now well known and one will ÿnd all the details about this optimization method in the abundant literature [5][6][7][8][9]. The research strategy already used in Reference [2] consists in substituting, for ÿnite element calculations in the optimization process, an approximate response of a neural network, or an approximate response from a Ritz method.…”

Section: Genetic Algorithms; Neural Network and Ritz Methodsmentioning

confidence: 99%

Genetic optimization of stiffened plates without the FE mesh support

Marcelin

2002

Numerical Meth Engineering

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SUMMARYThis paper deals with the optimization of beam sti eners on plates by varying their positions in order to minimize the maximum de ection of the plate. Genetic algorithms are used. The research strategy consists in substituting, for ÿnite element calculations in the optimization process, an approximate response of a neural network, or an approximate response from a Ritz method. This paper shows how it is possible to deal with more important problems and to envisage more general baseline design for sti ened plates by adding the orientation angle of each sti ener as a design variable. This paper also shows how one can free oneself from the FE mesh, i.e. how one no longer needs the support of the mesh for the sti eners.

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“…In the case of shape optimization of truss structures, discrete TOD methods using the ground structure have been extended to include optimization of the nodal point locations for a given number and connectivity of nodal points [240]. Initial applications of EC methods to discrete SO problems have been conducted by Grierson and Pak [227,260] in the context of truss structures. Soh and Yang [261] applied fuzzy controlled GAs to optimize the shape of planar and spatial truss structures.…”

Section: Shape Optimizationmentioning

confidence: 99%

Evolutionary Computation in Structural Design

Murawski

Arciszewski

Jong

2000

EWC

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Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems' representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented. KeywordsEvolutionary computation, Structural design, Conceptual design, Multiobjective analysis, Optimization, Constraints, Computer aided design IntroductionThe new Millennium witnesses the emergence of Information Technology as the driving force behind the progress in civil engineering, particularly in the area of computation as related to design. The growing sophistication of computer programs, their availability, increased speed of computations and their everdecreasing costs have already had a significant impact on civil engineering, and that can be considered a paradigm change.Up to very recently, computers in structural design were used mostly for the analytical purposes in the detailed design stages. Nowadays, their role is becoming more and more versatile. They are being applied to all stages of the design process, from the generation of design concepts (design topologies, or layouts), through preliminary design (design shape specification), and finally in the detailed design process (sizing of structural members). That requires a new intellectual and computational framework to fully benefit from the progress in Information Technology. Among computational paradigms, evolutionary computation (EC) is now recognized as particularly appropriate for various traditional and novel computational applications in structural engineering.The major objective of this paper is to present a comprehensive survey of the recent developments of evolutionary-based methods in structural engineering as well as to provide their histor...

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Optimal sizing, geometrical and topological design using a genetic algorithm

Cited by 114 publications

References 6 publications

Evolutionary Optimization in Uncertain Environments—A Survey

Evolutionary Optimization in Uncertain Environments—A Survey

Genetic optimization of stiffened plates without the FE mesh support

Evolutionary Computation in Structural Design

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