A metamodel using neural networks and genetic algorithms for an integrated optimal design of mechanisms

Marcelin, Jean-Luc

doi:10.1007/s00170-003-1750-7

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…Kramer () illustrated a workflow to embed a supervised learning model into a GA where the real fitness evaluations are performed only to the solutions that fulfil criterion in the predictive model. This way of integration is easily recognizable in different engineering applications (Marcelin ; Sreekanth and Datta ; Ibaraki, Tomita and Sugimoto ; Sato and Fujita ; Sakaguchi et al . ).…”

Section: Survey‐parameter Updatementioning

confidence: 91%

“…Kramer (2017) illustrated a workflow to embed a supervised learning model into a GA where the real fitness evaluations are performed only to the solutions that fulfil criterion in the predictive model. This way of integration is easily recognizable in different engineering applications (Marcelin 2004;Sreekanth and Datta 2011;Ibaraki, Tomita and Sugimoto 2015;Sato and Fujita 2016;Sakaguchi et al 2018). Exploiting a strategy from these engineering problems is certainly valuable to our survey-design problem because a large computation effort to calculate the objective function inherently restricts the number of fitness evaluations in our case.…”

Section: S U R V E Y -P a R A M E T E R U P D A T Ementioning

confidence: 97%

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Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Nakayama

Blacquière

Ishiyama

2019

Geophysical Prospecting

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Blended acquisition along with efficient spatial sampling is capable of providing high‐quality seismic data in a cost‐effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio‐inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters.

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Section: Survey‐parameter Updatementioning

confidence: 91%

Section: S U R V E Y -P a R A M E T E R U P D A T Ementioning

confidence: 97%

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Nakayama

Blacquière

Ishiyama

2019

Geophysical Prospecting

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“…The expected result is a play of optimal design variables. This strategy has been developed in [15] for gear boxes, and in [16] for hotrolled complex beams.…”

Section: Conclusion: Towards An Optimal Integrated Design For Mechanmentioning

confidence: 99%

Integrated Optimization of Mechanisms with Genetic Algorithms

Marcelin¹

2010

ENG

Self Cite

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This paper offers an integrated optimization of mechanisms with genetic algorithm, the principle of which is to use a neural network as a global calculation program and to couple the network with stochastic methods of optimization. In other words, this paper deals with the integrated optimization of mechanisms with genetic algorithms, and, in conclusion, the possible use of neural networks for complex mechanisms or processes

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“…At this point, surrogate models are often employed to replace the original FE simulations so that the design optimization problem can be solved efficiently using mathematical algorithms. Marcelin (2004) proposed a numerical optimization approach for car gear box mechanism using neural networks surrogate model. Kim et al (2016) optimized a piezoelectric cantilever beam energy harvester by introducing Kriging surrogate model to approximate the relationship between the design variables and the responses predicted by the FE model, and Halder and Samad (2017) optimized the geometric parameters to improve the performance of wave energy harvesting turbines by employing also Kriging surrogate model.…”

Section: Introductionmentioning

confidence: 99%

Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

Luo

Liu

Zhu

et al. 2018

Journal of Intelligent Material Systems and Structures

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Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique.

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A metamodel using neural networks and genetic algorithms for an integrated optimal design of mechanisms

Cited by 13 publications

References 18 publications

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Integrated Optimization of Mechanisms with Genetic Algorithms

Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

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