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A number of variables are applied to assess this.
The new technique and the classical 1 0 binary variables are applied to the various evolutionary methods and they are implemented on a number of structural topology optimisation problems.
Specific discrete variables are applied to realize the standardization design in the optimization process, in which a Surrogate Genetic Coding Algorithm (SGCA) is introduced, and a special check module is used to get rid of repeated fitness evaluation of the samples.
In this study, the exogenous variables are applied to the forecast model as described above.
At the start of the simulation several static variables are applied to the model.
The membership functions defined on the input variables are applied to their actual values to determine the degree of truth for each rule premise.
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Then, the adjoint variable method combined with complex variables is applied to obtain the shape and size sensitivity for structural optimization.
These variables were applied to determine the type of Sustainable Flood Retention Basin (predominantly used for hydraulic purposes) with an error of <8% in comparison to the application of all 34 variables.
A nonlocal physically based crystal plasticity (CP) FEM using the densities of statistically stored dislocations and geometrically necessary dislocations as the internal state variables is applied to simulate the micro-tensile operations.
Fast and elitist non-dominated sorting genetic algorithm (NSGA-II) with continuous and discrete variables were applied to obtain the maximum effectiveness (heat recovery) and the minimum total cost as two objective functions.
These scaled variables were applied to the input neurons to carry out the ANN modeling.
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