关键词: 灰狼优化算法, 收敛因子, Richards模型, 参数估计

Abstract: On the basis of analyzing the insufficiency of grey wolf optimizer, an improved grey wolf optimization algorithm（CGWO） is proposed. The proposed algorithm adopts the convergence factor based on the variation of cosine law to maintain a better balance between global search and local search, and the weight based on the Euclidean distance of the step length is introduced to accelerate the convergence rate of the algorithm. The simulation experiments are carried out on eight benchmark functions, the experimental results show that the CGWO algorithm is more accurate and more stable. Finally, the prediction of the growth concentration of glutamic acid bacteria is taken as an example, and the parameters of the Richards model are estimated by CGWO algorithm. The root-mean-square error and the mean absolute error are used as evaluation indexes. Compared with the results of PSO algorithm, GA algorithm and VS-FOA algorithm, the CGWO algorithm can effectively estimate the parameters of the Richards model.

Key words: grey wolf optimizer, convergence factor, Richards model, parameter estimation