一种改进的协同优化算法及其应用

针对协同优化方法收敛困难、优化效率低的问题,提出了一种改进的协同优化算法—ICO算法。通过引入自适应松弛因子将一致性等式约束转化为不等式约束,同时建立混合惩罚函数,将系统级约束优化问题转化为无约束优化问题,ICO算法较好地克服了传统协同优化算法难于收敛的缺点。标准算例实验结果表明,ICO算法能够有效提高优化的稳定性、可靠性和计算效率。优化结果显示了协同优化算法解决海洋供应船的设计优化问题的有效性,为解决更为复杂工程系统的设计优化问题奠定了基础。

An improved collaborative optimization algorithm and its application

Aiming at the convergence difficulties and low optimization efficiency of Collaborative Optimization (CO), an improved collaborative optimization algorithm (ICO) is proposed in this paper. By introducing the adaptive relaxation factor to let the restriction of consistency equality be transformed to the restriction of non equality as well as by constructing the mixed penalty function to convert the system level constraint optimization problem into unconstraint optimization problem, the improved algorithm can overcome the disadvantages of convergence difficulties of traditional collaborative optimization. Experimental results show that the stability, reliability and computing efficiency of ICO are better than CO, and lay a foundation for solving more complex engineering system.