Research on crow swarm intelligent search optimization algorithm based on surrogate model

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.