Wavelet network meta-models for the analysis of slender offshore structures

Pina, Aloísio Carlos de; Albrecht, Carl Horst; Lima, Beatriz Souza Leite Pires de; Jacob, Breno Pinheiro

doi:10.1016/j.engstruct.2014.02.039

Cited by 27 publications

(5 citation statements)

References 37 publications

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“…The IEEE Congress on Evolutionary Computation (CEC) competitions have presented many articles that solve computationally expensive functions by employing metamodels with EAs. Regarding particularly the application to offshore oil & gas production systems mentioned above, surrogate models based on neural networks were used in [28,29] to avoid expensive nonlinear dynamic Finite Element analyses.…”

Section: Introductionmentioning

confidence: 99%

An enhanced surrogate-assisted differential evolution for constrained optimization problems

et al. 2023

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The application of Evolutionary Algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations of the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed to replace those expensive simulations. In this work, a surrogate-assisted evolutionary optimization procedure is proposed. The procedure combines the Differential Evolution method with a 𝑘nearest neighbors (𝑘-NN) similarity-based surrogate model. In this approach, the database that stores the solutions evaluated by the exact model, which are used to approximate new solutions, is managed according to a merit scheme. Constraints are handled by a rank-based technique that builds multiple separate queues based on the values of the objective function and the violation of each constraint. Also, to avoid premature convergence of the method, a strategy that triggers a random reinitialization of the population is considered. The performance of the proposed method is assessed by numerical experiments using 24 constrained benchmark functions and 5 mechanical engineering problems. The results show that the method achieves optimal solutions with a remarkably reduction in the number of function evaluations compared to the literature.

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Section: Introductionmentioning

confidence: 99%

An enhanced surrogate-assisted differential evolution for constrained optimization problems

et al. 2023

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Section: Introductionmentioning

confidence: 99%

An enhanced surrogate-assisted differential evolution for constrained optimization problems

Garcia

Lima

Lemonge

et al. 2021

Preprint

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The application of Evolutionary Algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations of the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed to replace those expensive simulations. In this work, a surrogate-assisted evolutionary optimization procedure is proposed. The procedure combines the Differential Evolution method with a Anchor -nearest neighbors ( –NN) similarity-based surrogate model. In this approach, the database that stores the solutions evaluated by the exact model, which are used to approximate new solutions, is managed according to a merit scheme. Constraints are handled by a rank-based technique that builds multiple separate queues based on the values of the objective function and the violation of each constraint. Also, to avoid premature convergence of the method, a strategy that triggers a random reinitialization of the population is considered. The performance of the proposed method is assessed by numerical experiments using 24 constrained benchmark functions and 5 mechanical engineering problems. The results show that the method achieves optimal solutions with a remarkably reduction in the number of function evaluations compared to the literature.

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“…ANN was used to predict the extreme values of a turret-moored FPSO under different metocean condition with specific configuration for the mooring system. The use of meta-models has been presented in [44][45][46] to obtain full timeseries of mooring line or riser tensions. Pina [44] use Neural Network with an "exogenous" model, he presented estimation of line tension of moorings in time series using NARX, and then compare it results with FE simulation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dynamic responses of floating structures using NARX with mirroring technique

et al. 2018

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Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.

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Wavelet network meta-models for the analysis of slender offshore structures

Cited by 27 publications

References 37 publications

An enhanced surrogate-assisted differential evolution for constrained optimization problems

An enhanced surrogate-assisted differential evolution for constrained optimization problems

An enhanced surrogate-assisted differential evolution for constrained optimization problems

Prediction of dynamic responses of floating structures using NARX with mirroring technique

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