Derivative based approximation for predicting the effect of changes in laminate stacking sequence

Nagendra, S.; Haftka, Raphael T.; Gürdal, Zafer; Watson, Layne T.

doi:10.1007/bf01197039

Cited by 9 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each design is then assigned a proportion Si (i= l,...,iip: where Hp is the population size) of the total area of a roulette wheel defined as .-. (21) r< _…”

Section: Fitness Functionmentioning

confidence: 99%

“…We define the relative loss of the performance Al™, is defined as follow. (21) where, I^is the buckling load at the optimum point obtained by using response surface and /ie*is the exact buckling load at the optimum point obtained by the exact analysis. For 8-ply case, there is no difference between the optima obtained with the exact analysis and the approximate ones.…”

Section: Ga Optimizations Of Stacking Sequencementioning

confidence: 99%

“…First, they reduce considerably the number of design variables, and allow easy use of approximation for changes in stacking sequence (e.g. Nagendra et al [21]). Second, problems formulated with lamination parameters suffer much less from multiplicity of local optima with nearly the same performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lamination parameters for efficient genetic optimization of the stacking sequences of composite panels

Todoroki

Haftka²

1998

7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

View full text Add to dashboard Cite

Stacking sequence optimization in composite panels is a combinatorial problem often handled by genetic algorithms (GA). However GAs often require large computational resources. To overcome this problem, this paper proposes the use of lamination parameters in a two-stage approach to the optimization process. First, lamination parameters are used in continuous optimization to identify the neighbourhood of the optimum design. Next, a response surface approximation is obtained in that neighbourhood, and the GA is applied to this approximation. The effectiveness of this approach is demonstrated for buckling load maximization of a composite plate.

show abstract

“…Each design is then assigned a proportion Si (i= l,...,iip: where Hp is the population size) of the total area of a roulette wheel defined as .-. (21) r< _…”

Section: Fitness Functionmentioning

confidence: 99%

Section: Ga Optimizations Of Stacking Sequencementioning

confidence: 99%

See 1 more Smart Citation

Lamination parameters for efficient genetic optimization of the stacking sequences of composite panels

Todoroki

Haftka²

1998

7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

View full text Add to dashboard Cite

show abstract

“…These penalty functions are subtle, and must be chosen carefully so that an infeasible design is not more fit than a near optimal feasible design. Such details are discussed in [17], [18]. …”

Section: Ga Performance Analysismentioning

confidence: 99%

A Distributed Genetic Algorithm With Migration for the Design of Composite Laminate Structures

Mathew

Layne

2000

Parallel Algorithms and Applications

View full text Add to dashboard Cite

(ABSTRACT)This thesis describes the development of a general Fortran 90 framework for the solution of composite laminate design problems using a genetic algorithm (GA). The initial Fortran 90 module and package of operators result in a standard genetic algorithm (sGA). The sGA is extended to operate on a parallel processor, and a migration algorithm is introduced.These extensions result in the distributed genetic algorithm with migration (dGA).The performance of the dGA in terms of cost and reliability is studied and compared to a sGA baseline, using two types of composite laminate design problems. The nondeterminism of GAs and the migration and dynamic load balancing algorithm used in this work result in a changed (diminished) workload, so conventional measures of parallelizability are not meaningful. Thus, a set of experiments is devised to characterize the run time performance of the dGA.The migration algorithm is found to diminish the normalized cost and improve the reliability of a GA optimization run. An effective linear speedup for constant work is achieved, and the dynamic load balancing algorithm with distributed control and token ring termination detection yield improved run time performance.ACKNOWLEDGEMENTS.

show abstract

“…These variables have proven their value in composite laminate optimization (Nagendra et al [9]). Because they capture global stiffness properties of a laminate, they are a natural choice for representing coupling between ply orientation variables.…”

Section: Methodsmentioning

confidence: 99%

Statistical Optimization of Composite Laminates: Introduction of Coupling via a Change of Variables

Grosset¹,

Riche²,

Haftka³

2002

9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

View full text Add to dashboard Cite

The purpose of this paper is to improve the efficiency of a statistical optimization algorithm similar to Baluja's PBIL in two simple ways. First, the influence of the selection method is investigated and a selection scheme is proposed. Then a strategy for adding coupling in the probabilistic model at low computational cost is proposed. The idea is to use statistical information on intermediate variables (the lamination parameters of composite models) to complement the gene (fiber angle) probability distribution. The algorithms are applied to a composite laminate design.

show abstract

Derivative based approximation for predicting the effect of changes in laminate stacking sequence

Cited by 9 publications

References 19 publications

Lamination parameters for efficient genetic optimization of the stacking sequences of composite panels

Lamination parameters for efficient genetic optimization of the stacking sequences of composite panels

A Distributed Genetic Algorithm With Migration for the Design of Composite Laminate Structures

Statistical Optimization of Composite Laminates: Introduction of Coupling via a Change of Variables

Contact Info

Product

Resources

About