Crow Algorithm Based on Variation and Parameter Recurrence

In order to improve the global search ability and easy to fall into local extrema, we propose a modified crow search algorithm (ISA) based on differential variation perturbation factor and dynamic recurrence rules of model parameters. Introduce the differential variation perturbation factor to accelerate the information exchange and improve the convergence rate, and then introduce the model parameter dynamic recurrence rule to improve the perception probability and flight length to balance the global search and local search. The experimental results of the ablation experiments on two engineering optimization design problems show that the optimization performance of the improved crow search algorithm improves significantly, which proves the effectiveness and correctness of the improved method.