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In this paper, we describe the application of the well known Non-dominated Sorting Genetic Algorithm II (NSGA-II) in order to obtain high-quality Pareto-optimal solutions for three optimization scenarios.
Preliminary results for such optimization scenarios were presented in [37].
Several optimization scenarios are studied to emphasize different performance variables.
Two multi-objective, Pareto-based robust optimization scenarios have been investigated to encounter the permeability uncertainties.
This motivates us to extend this approach to nonlinear optimization scenarios.
Two different multi-objective optimization scenarios are carried out to determine the best design parameters.
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In the first optimization scenario, U is optimized without the given the maximum and minimum limits.
In "Robust optimization", scenario-based robust approach based on Mulvey robust optimization has been expressed briefly.
To prove this, a case study characterized by a complex optimization scenario has been selected.
In Figure 6, a result of a substrate feed optimization scenario with respect to the objective function is visualized.
While in the second optimization scenario, U has limitation at the minimum value, which is the value of U on the initial design data.
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