Design of laminate composite layups using genetic algorithms

Sargent, Philip; Ige, David O.; Ball, Nigel R.

doi:10.1007/bf01312200

Cited by 19 publications

(6 citation statements)

References 3 publications

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“…Genetic algorithms (GA), the most popular evolutionary algorithm, mimic the mechanics of natural genetics for artificial systems based on operation that are the counterparts of natural ones. In the last decade, different GA based approaches for ply orientations or stacking sequence optimization for different functional purposes have been devised and reported in literature [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization approach for multi-objective composite box-beam design

Sundaram

Sujit

Rao

2007

Composite Structures

114

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Abstract:-This paper presents a multi-agent search technique to design an optimal composite box-beam helicopter rotor blade. The search technique is called particle swarm optimization ('inspired by the choreography of a bird flock'). The continuous geometry parameters (cross-sectional dimensions) and discrete ply angles of the box-beams are considered as design variables. The objective of the design problem is to achieve a) specified stiffness value and b) maximum elastic coupling. The presence of maximum elastic coupling in the composite box-beam increases the aeroelastic stability of the helicopter rotor blade. The multi-objective design problem is formulated as a combinatorial optimization problem and solved collectively using particle swarm optimization technique. The optimal geometry and ply angles are obtained for a composite box-beam design with ply angle discretizations of 10• and 45 •. The performance and computational efficiency of the proposed particle swarm optimization approach is compared with various genetic algorithm based design approaches. The simulation results clearly show that the particle swarm optimization algorithm provides better solutions in terms of performance and computational time than the genetic algorithm based approaches.

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Section: Introductionmentioning

confidence: 99%

Particle swarm optimization approach for multi-objective composite box-beam design

Sundaram

Sujit

Rao

2007

Composite Structures

114

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“…GA is, in this respect, more flexible, but it frequently takes more computational time [56,61,66]. It is not easy to generalize the previous conclusion, since there are other researches, such as Rama Mohan Rao and Shyju [58], which shows that SA had better computational efficiency and was better at finding a solution to other combinatorial problems.…”

Section: Simulated Annealingmentioning

confidence: 84%

“…Sargent et al [56] compared GA with some raging algorithms (i.e., random search, raging search, and simulated annealing) and observed that GA obtained better solutions than raging search, but in some cases they were unable to find a solution. Sivakumar et al [57] compared David, Fletcher, and Powell (DFP)'s Quasi-Newton Method and GA, applied to reduce weight of a laminated composite limited by its basic frequencies.…”

Section: State Of the Art Of Composite Materials Designmentioning

confidence: 99%

Memetic Computing Applied to the Design of Composite Materials and Structures

Peláez

Gómez-Ruiz

Veintimilla

et al. 2017

Mathematical Problems in Engineering

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Presently, there exists an important need for lighter and more resistant structures, with reduced manufacturing costs. Laminated polymers are materials which respond to these new demands. Main difficulties of the design process of a composite laminate include the necessity to design both the geometry of the element and the material configuration itself and, therefore, the possibilities of creating composite materials are almost unlimited. Many techniques, ranging from linear programming or finite elements to computational intelligence, have been used to solve this type of problems. The aim of this work is to show that more effective and dynamic methods to solve this type of problems are obtained by using certain techniques based on systematic exploitation of knowledge of the problem, together with the combination of metaheuristics based on population as well as on local search. With this objective, a memetic algorithm has been designed and compared with the main heuristics used in the design of laminated polymers in different scenarios. All solutions obtained have been validated by the ANSYS5 software package.

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“…Recently, genetic algorithms (GAs) have been introduced as a methodology that is particularly suited to the design of composite structures because they do not appear to suffer from the same shortcomings as gradient-based optimization techniques. 5,[8][9][10]12,14…”

Section: Introductionmentioning

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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Design of laminate composite layups using genetic algorithms

Cited by 19 publications

References 3 publications

Particle swarm optimization approach for multi-objective composite box-beam design

Particle swarm optimization approach for multi-objective composite box-beam design

Memetic Computing Applied to the Design of Composite Materials and Structures

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

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