中文摘要：

针对协同优化算法迭代次数多、易收敛于局部极值点问题，提出一种全局快速寻优的协同优化算法。在系统级一致性等式约束中采用改进后松弛因子，改进动态松弛因子使优化设计点快速收敛于极值点，静态松弛因子使优化设计点跳出局部极值点，确保系统目标函数得到全局最优解；子学科目标函数由一致性目标函数和子学科最优目标函数两个部分以不同权重相加组成，考虑一致性的同时，又兼顾子学科独立性。采用减速器优化案例对改进协同优化算法进行验证。仿真结果表明，改进后算法在保证最大约束值较小的前提下，可快速得到全局最优解且鲁棒性好。

英文摘要：

We propose a new collaborative optimization （CO） method with global fast optimization to solve the problems of too many times of iterations and local optimal solutions of CO. A new slack factor is introduced into system optimization, and optimal design points can be fast converged to extreme points by the improved dynamic slack factor. Static slack factor enables optimal design points to jump out of local extreme points, guaranteeing that the results of the system objective function are global optimal solutions. The objective function of subsystem is divided into two parts： consistent objective function and subsystem optimal objective function, which are added up with different weights as the subsystem objective function. Thus both the consistence and the independence of subsystems are taken into account. The improved CO （ICO） is validated via the examples of reducer. Simulation results show that on the premise of ensuring a smaller constrained maximum value, the ICO can quickly get the global optimal solution and has good robustness.