Construction of response surfaces for design optimization applications

Venter, Gerhard; Haftka, Raphael T.; Starnes, James H.

doi:10.2514/6.1996-4040

Cited by 71 publications

(34 citation statements)

References 7 publications

(6 reference statements)

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“…Further, as it is well-known to the computational research community, the applications of optimization are wide and the performance of proposed technique may be studied on multiple real world scenarios related to networking [21], electric power systems [22], design applications [23] and the like.…”

Section: Discussionmentioning

confidence: 99%

Extending the Nelder-Mead algorithm for feature selection from brain networks

Kar

Konar

Chakraborty³

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-Centrifugation is often applied in laboratories and industries to increase the effective gravity on a particle and hence make it sediment faster. Based on this principle, one may extend the existing optimization techniques, which are driven by only gravitational force (objective values of discovered best solutions), and do not consider application of centrifugal force for faster convergence. We extended the Nelder-Mead's simplex algorithm, by applying an exponentially decaying centrifugal force on each of the computed vertices of the simplex. The proposed centrifugation technique was also applied on other optimization algorithms including differential evolution and gravitational search algorithms. It was seen that application of centrifugal force indeed enhanced the objective values obtained by of all the tested evolutionary algorithms. The comparative performance of the extended Nelder-Mead Algorithm was found to be better among all the tested algorithms. The algorithms were compared on the basis of the best obtained objective value after a fixed number of objective function evaluations (here 20 times the problem dimension). Testing was performed in the real world problem of EEG feature selection (from brain networks), for the classification of memory encoding versus recall using SVM. The average classification accuracy was found to be high (89.97%).

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Section: Discussionmentioning

confidence: 99%

Extending the Nelder-Mead algorithm for feature selection from brain networks

Kar

Konar

Chakraborty³

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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“…Additionally, approximation models and sensitivity analysis are implemented and applied to predict missing data in the database. [5][6][7] Analysis methods are developed for aerodynamics, propulsion and performance. [8][9][10] These analysis codes are implemented in a spreadsheet using the well-known Visual Basic for applications (VBA) programming language for simple and efficient calculation.…”

Section: Process and Methodologymentioning

confidence: 99%

“…7,19) Generally the second-order polynomial function is used for the design variables to represent the response surface. 20)…”

Section: )mentioning

confidence: 99%

Aircraft Design Optimization Process using a Decision Model by Database

Naing

Nguyen

Lee

et al. 2015

Trans. Japan Soc. Aero. S Sci.

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In this study, an efficient methodology capable of systematically constructing an aircraft design database is developed and its application is discussed. The database focuses on fighter and attack aircraft because their design is a particular challenge compared with the design of other types of aircraft. For small conventional aircraft, historical information accumulated over many years on countless previous designs is freely available to designers. Such data for fighter aircraft design are, however, difficult to obtain or are insufficient. The database developed can contribute to the design of fighter and attack aircraft on a conceptual level. In addition, decision-making models are developed by means of data mining as well as approximation models based on the developed database to facilitate initial aircraft configuration decisions. The efficiency and simplicity of the proposed method are described in a case study, which also describes a sample process of conceptual design optimization using initial design information extracted from the developed database.

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“…The VCRSM method employs DOE and RS modeling techniques and has been successfully applied to the multidisciplinary wing design of a high speed civil transport, 7 to the analysis and design of composite curved channel frames, 8 and to reduce numerical noise in structural analyses. 9,10 A recent review of several applications of response surface models in engineering design is given in Ref. 11; for applications of approximations in structural design, see Ref.…”

Section: Frame Of Referencementioning

confidence: 99%

Comparison of response surface and kriging models for multidisciplinary design optimization

Simpson

Korte

Mauery

et al. 1998

7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

372

178

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In this paper, we compare and contrast the use of second-order response surface models and kriging models for approximating non-random, deterministic computer analyses. After reviewing the response surface method for constructing polynomial approximations, kriging is presented as an alternative approximation method for the design and analysis of computer experiments. Both methods are applied to the multidisciplinary design of an aerospike nozzle which consists of a computational fluid dynamics model and a finite-element model. Error analysis of the response surface and kriging models is performed along with a graphical comparison of the approximations, and four optimization problems are formulated and solved using both sets of approximation models. The second-order response surface models and kriging models-using a constant underlying global model and a Gaussian correlation function-yield comparable results.

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Construction of response surfaces for design optimization applications

Cited by 71 publications

References 7 publications

Extending the Nelder-Mead algorithm for feature selection from brain networks

Extending the Nelder-Mead algorithm for feature selection from brain networks

Aircraft Design Optimization Process using a Decision Model by Database

Comparison of response surface and kriging models for multidisciplinary design optimization

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