Genetic algorithm representations for laminate layups

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.

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“…However, w^ is not 0 even for balanced symmetric laminates. The non-zero values of W^ produce the coupling terms, D 16 and D 26…”

Section: Buckling Load Of Composite Platesmentioning

confidence: 99%

“…While the branch and bound method has been used occasionally for solution (e.g. [1]), genetic algorithms have been very popular for solving such problems (e.g., [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]). …”

Section: Introductionmentioning

confidence: 99%

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

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“…However, the mathematical models use information on the function derivatives to climb towards the optimal result. Grefenstette (1986) 1.0 0.01 50 Wang (1991) 100 0.001 100 500 Janson & Frenzel (1993) 0.3 0.006 180 Ball et al (1993) 100 20 0.6 0.01 Koumousis & Georgiou 100 (1994) 30-200 0.5-1.0 0.001-0.05 (Not mentioned) Srinivas & Patnail (1994) 1.00 0.03 300 3000 Croce et al (1995) 200 Dorndorf & Pesch (1995) 0.65 0.001 300 0.25 0.75 2000 Tate & Smith (1995) 100 0.8 0.005 100 10 Keane (1995) 0.5-0.7 0.1 (Not mentioned) Suresh et al (1996) 40-60 0.6 0.01-0.005 100-200 20 Carrol (1996a) 50 Carrol (1996b) 0.5 0.02-0.04 12 0.5 0.01 200 150 Roston & Sturges (1996) (Not mentioned) Furuta et al (1996) 0.25 0.01 (Not mentioned) 0.8 0.01 100 Liu et al (1997) 50…”

Section: Mutationmentioning

confidence: 99%

Using genetic algorithms to solve optimization problems in construction

Al‐Tabtabai¹,

Alex²

1999

Eng Const Arch Manage

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Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution space in an intelligent manner to evolve better solutions. The algorithm does not need any specific programming efforts but requires encoding the solution as strings of parameters. The field of application of genetic algorithms has increased dramatically in the last few years. A large variety of possible GA application tools now exist for non‐computer specialists. Complicated problems in a specific optimization domain can be tackled effectively with a very modest knowledge of the theory behind genetic algorithms. This paper reviews the technique briefly and applies it to solve some of the optimization problems addressed in construction management literature. The lessons learned from the application of GA to these problems are discussed. The result of this review is an indication of how the GA can contribute in solving construction‐related optimization problems. A summary of general guidelines to develop solutions using this optimization technique concludes the paper.

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“…In addition to compression, Nagendra et al 10 took a shear load into account. Ball et al 1 solved the laminate stacking sequence design problem subject to buckling and strength constraints.…”

Section: Treatment Of Objectivesmentioning

confidence: 99%

A Genetic Algorithm–Based System for the Optimal Design of Laminates

Savić

Evans

Silberhorn

1999

Computer aided Civil Eng

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A new software tool, GACOMP, for the optimal design of general and symmetric/balanced laminates (or sandwich panels) with specified mechanical properties has been developed. The approach taken relies on the analysis of a given laminate, of known materials and stacking sequence, and on an efficient genetic algorithm-based optimization procedure to come up with the best design with respect to single or multiple objectives and constraints. Required engineering constants or their ratios, weight, strength, anisotropy, isotropy, or twist, considered individually or in any combination, can be used as optimization objectives. Several improvements to the genetic algorithms were introduced to provide faster convergence and the ability to identify multiple optima in a single run. Two practical examples are presented to show the effective performance of GACOMP. The first tackles the design of a composite laminate with specified in-plane and flexural engineering constants, while the second deals with the design of a laminate with specified stiffness ratios, minimum twisting under the load considered, and maximum strength.

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Genetic algorithm representations for laminate layups

Cited by 47 publications

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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

Using genetic algorithms to solve optimization problems in construction

A Genetic Algorithm–Based System for the Optimal Design of Laminates

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