Multiple- and single-objective approaches to laminate optimization with genetic algorithms

Costa, Lino; Fernandes, Luís Alberto D’Ávila; Figueiredo, Isabel N.; Júdice, Joaquim J.; Leal, Rogério; Oliveira, Pedro

doi:10.1007/s00158-003-0355-y

Cited by 24 publications

(18 citation statements)

References 13 publications

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“…The beam is optimized for minimum deflection and minimum weight under constraints on maximum stress and maximum deflection [7]. …”

Section: Multi-objective Optimization Of Cantilever Beam Problemmentioning

confidence: 99%

Weight and deflection optimization of Cantilever Beam using a modified Non-Dominated sorting Genetic Algorithm

Gurugubelli¹,

Kallepalli²

2014

IOSRJEN

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-Many problems having single or multiple objectives are routinely solved using GA methodologies. This paper is devoted to the simultaneous weight and deflection optimization of cantilever beam by using a modified Non-Dominated sorting Genetic Algorithm, subjected to maximum stress and maximum deflection constraints. The multi-objective optimization algorithm used in this paper has better sorting, incorporates elitism and no sharing parameter needs to be chosen a priori.

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“…The beam is optimized for minimum deflection and minimum weight under constraints on maximum stress and maximum deflection [7]. …”

Section: Multi-objective Optimization Of Cantilever Beam Problemmentioning

confidence: 99%

Weight and deflection optimization of Cantilever Beam using a modified Non-Dominated sorting Genetic Algorithm

Gurugubelli¹,

Kallepalli²

2014

IOSRJEN

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“…Deb [7]). The Elitist GA, described in Costa and Oliveira [8] and Costa et al [9], was applied to problems (47) and (48), with standard values for the parameters. We briefly describe next some technical features and the parameters of this GA.…”

Section: Discrete Modelmentioning

confidence: 99%

“…Table 7. Optimal location of the projections of 3 electrodes, for the meshes 3x3, 4x4 and 5x5, and BC=2 Figure 1 displays the undeformed (solid line) and deformed (dashed line) meshes, for 4x4 finite elements, for BC=1, pe= [1,9]. Figure 1.…”

mentioning

confidence: 99%

“…Figure 1. Undeformed and deformed meshes, BC=1, pe= [1,9] In this figure, the element numbers are indicated at the center of the element, the nodemarks are circles, and the node N=21, that is the left vertex on the top side of ω, in finite element 13, is the node with maximum displacement. The deformed mesh corresponds to the tangential displacement u tg of the middle plane ω.…”

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confidence: 99%

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Actuator Effect of a Piezoelectric Anisotropic Plate Model

Costa

Oliveira

Figueiredo

et al. 2006

Mechanics of Advanced Materials and Structures

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This paper addresses the actuator effect of a piezoelectric anisotropic plate model, depending on the location of the applied electric potentials, and for different clamped boundary conditions. It corresponds to integer optimization problems, whose objective functions involve the displacement of the plate. We adopt the two-dimensional piezoelectric anisotropic nonhomogeneous plate model derived in Figueiredo and Leal [1]. This model is first discretised by the finite element method. Then, we describe the associated integer optimization problems, which aim to find the maximum displacement of the plate, as a function of the location of the applied electric potentials. In this sense, we also introduce a related multi-objective optimization problem, that is solved through genetic algorithms. Several numerical examples are reported. For all the tests, the stiffness matrices and force vectors have been evaluated with the subroutines planre and platre, of the CALFEM toolbox of MATLAB [2], and, the genetic algorithms have been implemented in C ++ .

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“…13 , -21 However, Costa et al 22 have considered both of multi-objective and single-objective approaches to laminate optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Comparison of stochastic search optimization algorithms for the laminated composites under mechanical and hygrothermal loadings

Aydın

Artem

2011

Journal of Reinforced Plastics and Composites

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The aim of the present study is to design the stacking sequence of the laminated composites that have low coefficient of thermal expansion and high elastic moduli. In design process, multi-objective genetic algorithm optimization of the carbon fiber laminated composite plates is verified by single objective optimization approach using three different stochastic optimization methods: genetic algorithm, generalized pattern search, and simulated annealing. However, both the multi-and single-objective approaches to laminate optimization have been used by considerably few authors. Simplified micromechanics equations, classical lamination theory, and MATLAB Symbolic Math toolbox are used to obtain the fitness functions of the optimization problems. Stress distributions of the optimized composites are presented through the thickness of the laminates subjected to mechanical, thermal, and hygral loadings.

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Multiple- and single-objective approaches to laminate optimization with genetic algorithms

Cited by 24 publications

References 13 publications

Weight and deflection optimization of Cantilever Beam using a modified Non-Dominated sorting Genetic Algorithm

Weight and deflection optimization of Cantilever Beam using a modified Non-Dominated sorting Genetic Algorithm

Actuator Effect of a Piezoelectric Anisotropic Plate Model

Comparison of stochastic search optimization algorithms for the laminated composites under mechanical and hygrothermal loadings

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