Many problems in real-world engineering are formulated as nonlinear constrained optimization problems.In most cases they consist not only of a nonlinear objective function that has to be optimized, but of a number of linear and/or nonlinear constraints as well that must be satisfied by the solution [1].Since the complexity of these problems, conventional optimization algorithms such as gradient optimization techniques are often unable to provide even a feasible solution.Therefore.developing effective search methods to handle complex nonlinear optimization is an important and valuable work.In the last decade, much attention has been attracted to solve nonlinear constrained optimization problems via evolutionary algorithms [1-3].It is known that the balance of searching between the objective and constraints greatly affects the effectiveness of the algorithm.Wang and et al.