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As the optimisation problem contains a number of continuous (ply angles and thicknesses) and discrete (material combinations) design variables, a sequential solution procedure is devised in which the optimal variables are computed in different stages.
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The multi-objective optimization algorithm gives a set of possible combinations of design variables, which is referred as the optimal Pareto-front, out of which a unique combination is selected based upon a decision making criterion.
The 2D and 3D design spaces showing the combinations of design variables selected as sample inputs are demonstrated in Figs. 14 and 15, respectively.
In case of aircraft fuel consumption, the objective function was found to be highly nonlinear and discontinuous, with several combinations of design variables not having a feasible solution.
It is also hoped that with the developed equations, a designer can subsequently select the best combination of design variables in order to achieve good uniformity in a naturally ventilated calf building.
If more than one combination of design variables satisfies all these conditions, we would like to determine that combination of design variables which optimizes some measure of merit (e.g. weight, cost, or yield).
The combination of design variables for the sequencing is presented in Fig. 13.
In order to achieve the appropriate combination of design variables that satisfy the design constraints, a GA-based program was used in a reverse analysis process.
Dimensionless charts for determining the required combination of design variables are presented with a view to informing first-order design guidance for naturally ventilated buildings.
Depending on DMS a unique combination of design variables is then selected for each type of fill that maximizes the tower's performance.
Then, an optimization procedure incorporating the Kriging modeling technique and a modified PSO algorithm is proposed to find the optimal combination of design variables.
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