Abstract: To solve problem of high computational cost and low convergence speed of initial points away from the optimal solution in collaborative optimization, a new dynamic relaxation cooperative optimization method with fast convergence is presented. Two-phase optimization is adopted in this method. In the accelerating convergence phase, the calculation method of relaxation factor is improved, and the inconsistent information between the optimal value of disciplines and its mean value is used to construct the relaxation factor. The optimization solution of the first phase is adopted as the initial points in the optimization solution phase. The relaxation factor satisfying the consistent precision requirement is selected for cooperative optimization, and the global optimal solution is obtained. A typical numerical example and the reducer MDO problem are adopted to test this optimization method. Experimental results show that the proposed method can greatly reduce the computational cost and accelerate the convergence speed of the initial points away from the optimal solution.

Key words: multidisciplinary optimization, collaborative optimization, relaxation factor