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Therefore, a suitable method to handle the constraints must also be executed.
First, a special penalty function is designed to handle the constraints.
To handle the constraints, we first propose a novel dynamic S-type soft-threshold penalty method.
To handle the constraints in the optimization problem, a self-adaptive penalty function scheme is used.
Some modifications have been introduced to the original NSGA-II code to handle the constraints of the optimization problem.
To handle the constraints systematically, model predictive control (MPC) is used in some parts of the power cycle.
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In addition, we propose some extensions, such as the introduction of a mixed variational setting and its discretization, to handle the constraint on the divergence of the field.
The prominent way to handle the constraint is the penalty function method.
Since the envelope theorem is formulated for free maximization problems, we must first handle the constraint.
A constraint handling mechanism based on repair methods is used for handling the constraints.
Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints.
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