HS4425: MultiObjective Shape Optimization Study
Perform a multiobjective Optimization study, and search for the Pareto front that minimizes both volume and maximum displacement.
Run MultiObjective Shape Optimization

Add an Optimization.
 In the Explorer, rightclick and select Add from the context menu.
 In the Add dialog, select Optimization.
 For Definition from, select Setup and click OK.

Modify input variables.
 Go to the step.
 In the work area, Active column, clear the radius_1, radius_2 and radius_3 checkboxes.
 Go to the step.
 Click the Objectives/Constraints  Goals tab.

Apply an objective on the Volume and Max_Disp output responses.

Click the Define Output Responses step, and change the
Evaluate From column to Fit  RBF (fit_4) for Volume,
Max_Stress, and Max_Disp.
 Go to the step.
 In the work area, set the Mode to Multi_Objective Genetic Algorithm (MOGA).
 Click Apply.
 Go to the step.

Click Evaluate Tasks.
HyperStudy stops MOGA after 50 iterations, and performs a total of 13317 analyses. The Pareto front of the last iteration contains 408 points.
 Go to the step.

Click the Optima tab.
The Pareto front of Objective 2 versus Objective 1 is displayed in the plot.
The goal of this study was to minimize both Volume (Objective 1) and Max_Disp (Objective 2). The Pareto plot shows all of the nondominated solutions. A nondominated solution is a solution which can no longer improve one objective without deteriorating another. You can see that minimizing Objective 1 will increase Objective 2, and minimizing Objective 2 will increase Objective 1. According to these results, you must decide what would be the optimal solution. For instance, the Pareto plot may allow a compromise solution to be selected somewhere in the middle.