Efficient sampling techniques for surrogate-based optimization with thermoelastic application

In many real-life models, the objective function may be very expensive to calculate or sometimes a black-box function. Therefore, a surrogate model is required to reduce the cost of the calculations. To build a robust surrogate model, a good sampling technique is needed. This paper suggests two new...

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Vydané v:International journal of mechanical sciences Ročník 266; s. 108896
Hlavní autori: AbdEl-latief, A., Haridy, A., Abouelseoud, Y., EL-Alem, M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 15.03.2024
Predmet:
Derivative-free method
Fractional calculus
Global optimization
Half space
Sampling techniques
Thermoelasticity
Thermoelasticity
Global optimization
Derivative-free method
Half space
Fractional calculus
Sampling techniques
ISSN:0020-7403, 1879-2162
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Shrnutí:In many real-life models, the objective function may be very expensive to calculate or sometimes a black-box function. Therefore, a surrogate model is required to reduce the cost of the calculations. To build a robust surrogate model, a good sampling technique is needed. This paper suggests two new sampling techniques for surrogate-based optimization, depending on four random walk steps, which in turn give a good chance for building an accurate approximation for the original function. In addition to that, one of these new sampling techniques has a new feature that was not used before. The main advantage of our two suggested sampling techniques is that there is no optimization subproblem will be solved for the next sample point, which in turn saves CPU time. Furthermore, we applied our two sampling techniques to a 1D thermal shock problem for a half-space in the context of the fractional order theory of thermoelasticity to reduce the number of function evaluations and consequently CPU time. Laplace transform techniques are used to obtain the solution of the system in the frequency domain. Applying our algorithms to the few points obtained by using Laplace’s inverse, we can reach the optimal solution for all functions filed. The predictions of our techniques are discussed and compared with the classical method. 2D and 3D graphs are plotted for our numerical results. The temperature, displacement, and stress distributions are computed and represented graphically for different times and several fractional parameters. [Display omitted] •Application of optimization techniques in thermoelasticity is presented.•Our techniques require fewer function evaluations achieving competitive accuracy.•Comparison between two different numerical methods in evaluation all function fields.•Our results demonstrate the superiority over the classical method on the CPU time.
ISSN:0020-7403
1879-2162
DOI:10.1016/j.ijmecsci.2023.108896