A large amount of calculation exists in a complex engineering optimization problem. The swarm intelligence algorithm can improve calculation efficiency and accuracy of complex engineering optimization. In the existing research, the surrogate model and the swarm intelligence algorithm are only two independent tools to solve the optimization problem. In this paper, we propose the surrogate-assisted crow swarm intelligent search optimization algorithm (SACSA) by combining the characteristics of swarm intelligence algorithm and surrogate model. The proposed algorithm utilizes the initial samples to construct the surrogate model, and then the improved crow search algorithm (CSA) is applied to obtain optimal solution. Finally, the proposed algorithm is compared with EGO, MSSR, ARSM-ISES, AMGO and SEUMRE, MPS, HAM algorithms. The comparison results show that the proposed algorithm can find a global optimal solution with fewer samples and is beneficial to improving the efficiency and accuracy of calculation.

复杂的工程优化问题中存在大量计算。群智能算法可以提高复杂工程优化的计算效率和准确性。在现有的研究中，替代模型和群体智能算法只是解决优化问题的两个独立工具。本文结合群体智能算法的特点和代理模型，提出了一种代理辅助的乌鸦群智能搜索优化算法（SACSA）。该算法利用初始样本构建代理模型，然后使用改进的Crow Search算法（CSA）获得最优解。最后，将该算法与EGO，MSSR，ARSM-ISES，AMGO和SEUMRE，MPS，HAM算法进行了比较。