Control constraints are handled through non-quadratic functions.
Constraints are handled through a fuzzy penalty function method.
Input and output constraints are handled in the optimization task using a nonlinear programming method.
Motion constraints are handled by the new form of Kane's equation.
The constraints are handled by a nonlinear iterative cone-complementary linearization method in obtaining gain matrices.
Both the equality and inequality constraints are handled by penalty functions.
Meanwhile, various constraints are handled effectively by using heuristic strategies designed by their characteristics.
Problem-specific constraints are handled by a feasibility-based fitness function that reflects their degree of violation.
The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints.
Such constraint is directly enforced when an ant builds a candidate solution, while the other mechanical constraints are handled by means of an adaptive penalty method (APM).
