【摘要】 协同优化(CO,Collaborative Optimization)算法在应用中存在计算困难的问题.分析了引起CO算法计算困难的原因;使用L1范数改进了CO算法的学科一致性约束,避免了学科级目标函数导数的不连续性,并从数学理论上证明了改进的学科一致性约束的收敛性;增加系统级罚函数,使系统级优化问题转化为无约束优化问题;使用快速启动方法,在子系统级优化过程中充分合理利用系统级优化求得的最优解;给出了使用改进的学科一致性约束、系统级罚函数和快速启动方法的ICO(Improved Collaborative Optimization)多学科设计优化方法,较好地克服了CO算法存在计算困难的缺点.标准算例实验结果表明,ICO多学科设计优化方法有效提高了算法的稳定性、可靠性和计算效率.更多还原

【Abstract】 Some serious computational difficulties are found in application of collaborative optimization(CO).Reasons that cause computational difficulties in CO were analyzed.The L1 norm was used to improve subsystem consistency constraint and to avoid discontinuities in subsystem object function derivatives.Mathematical theory proved the convergency of the improve subsystem consistency constraint.Penalty function was added to system-level object function to convert unconstrained optimization into constrained optimization.A quick-start strategy was used to make the best use of optimal solution of system-level optimization in subsystem-level optimization.A new improved collaborative optimization(ICO) method was presented.Improved subsystem consistency constraint,system-level penalty function,and the quick-start strategy were used in ICO.These measures overcome the computational difficulties of CO better.Experiment results show that the robustness,reliability and computing efficiency of ICO are higher than those of CO.更多还原