  In this paper, we utilize ADCSO (Adaptive Dynamic Cat Swarm Optimization) to
estimate the parameters of Fractional Order Grey Model. The parameters of
Fractional Order Grey Model affect the prediction accuracy of the model. In
order to solve the problem that general swarm intelligence algorithms easily
fall into the local optimum and optimize the accuracy of the model, ADCSO is
utilized to reduce the error of the model. Experimental results for the data of
container throughput of Wuhan Port and marine capture productions of Zhejiang
Province show that the different parameter values affect the prediction
results. The parameters estimated by ADCSO make the prediction error of the
model smaller and the convergence speed higher, and it is not easy to fall into
the local convergence compared with PSO (Particle Swarm Optimization) and LSM
(Least Square Method). The feasibility and advantage of ADCSO for the parameter
estimation of Fractional Order Grey Model are verified.
