Multi-objective discrete optimization of laminated structures

Spallino, R.; Rizzo, Santi Agatino

doi:10.1016/s0093-6413(02)00227-6

Cited by 35 publications

(12 citation statements)

References 11 publications

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“…First, the binary digit substring should be decoded to an unsigned decimal integer to treat continuous design variable, then the physical value of x is converted from equation (2). The required precision is determined by the length of the substring log 2 …”

Section: Continuous Design Variablementioning

confidence: 99%

An optimization technique using the characteristics of genetic algorithm

Kim

Lee²

2008

Materialwissenschaft Werkst

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Optimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient-based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.

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Section: Continuous Design Variablementioning

confidence: 99%

An optimization technique using the characteristics of genetic algorithm

Kim

Lee²

2008

Materialwissenschaft Werkst

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“…Multi-objective optimization of laminated composite materials have been studied by several authors in literature. The problem of the multi-objective optimal design of laminated composite structures has been studied by [1], incorporating the applicable criteria that follow. A methodology for the multi-objective optimization of laminated composite materials that is based on an integercoded genetic algorithm has presented by [2].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Genetic Algorithm optimization of the composite laminates as a satellite structure material for coefficient of thermal expansion and elastic modulus

Aydın

Artem

2009

2009 4th International Conference on Recent Advances in Space Technologies

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Abstract-This study considers multi-objective optimal design of the fiber reinforced symmetric-balanced laminated composites using genetic algorithms. MATLAB Genetic Algorithm and Direct Search Toolbox is used to obtain Pareto-optimal design for three different model problems. The objectives of the problems are to maximize the Young's moduli and minimize the coefficient of thermal expansion (CTE) simultaneously for 8 and 16 layered carbon/epoxy composites. Simplified micromechanics equations and classical lamination theory are used in order to obtain the fitness functions of the problems. Stress and strain distributions are presented through the thickness of the laminates for the optimized problems under mechanical and thermal loadings.

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“…The objective of arch dam optimization is to get the optimum design scheme in certain condition by means of mathematical method. It is a new technology developed in recent 20 years, and many people domestic and overseas have done a lot of studies in this aspect and gained great achievements [1]. The available achievements and practical engineering applications show that optimization design can reduce the volume of dam body by 5-20% and has gained obvious economic and social benefit.…”

Section: Introductionmentioning

confidence: 99%