A Parameter Estimation of Fractional Order Grey Model Based on Adaptive Dynamic Cat Swarm Algorithm

TL;DR

This paper introduces an adaptive dynamic cat swarm optimization algorithm to improve parameter estimation in fractional order grey models, enhancing prediction accuracy and convergence speed over traditional methods.

Contribution

The paper proposes a novel ADCSO algorithm tailored for fractional order grey model parameter estimation, outperforming PSO and LSM in accuracy and convergence.

Findings

ADCSO reduces prediction error compared to PSO and LSM.

ADCSO achieves faster convergence and avoids local optima.

Parameter estimation with ADCSO improves model prediction accuracy.

Abstract

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.