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

摘要： 在分析灰狼优化算法不足的基础上，提出一种改进的灰狼优化算法（CGWO），该算法采用基于余弦规律变化的收敛因子，平衡算法的全局搜索和局部搜索能力，同时引入基于步长欧氏距离的比例权重更新灰狼位置，从而加快算法的收敛速度。对8个经典测试函数进行仿真实验，结果表明CGWO算法的求解精度更高，稳定性更好。最后以预测谷氨酸菌体生长浓度为例，利用CGWO算法估计Richards模型的参数，以均方根误差和平均绝对误差作为评价指标，与PSO算法、GA算法和VS-FOA算法的结果进行比较，CGWO算法可以有效地估计Richards模型中的参数。

关键词: 灰狼优化算法, 收敛因子, Richards模型, 参数估计