To solve the problems existing in the current constraint handling techniques, where it's easy to fall into the local optimal solutions, difficult to satisfy the equality constraints and there are also multiple control parameters, the parameter omega is designed, based on the gradient descent method and multi-objective crowding-distance theory. In this paper, omega is a reflection of population constraint violation degree distribution information, which can adaptively adjust the relaxation of the constraint violation threshold mu to solve the constrained problem accurately. In addition, the mu threshold comparison criteria have been improved to increase population diversity. After the solutions of the standard constrained optimization problems (COP) of CEC2017, we compare the results with other advanced algorithms. And the comparison shows that the improved mu-constraint handling techniques can deal with complex equation constraints efficiently.