计算机工程与应用2019，Vol.55Issue(21)：59-64,6.DOI:10.3778/j.issn.1002-8331.1808-0117
改进收敛因子和比例权重的灰狼优化算法
Improved Grey Wolf Optimizer with Convergence Factor and Proportional Weight
摘要
Abstract
On the basis of analyzing the insufficiency of grey wolf optimizer, an improved grey wolf optimization algo-rithm(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.关键词
灰狼优化算法/收敛因子/Richards模型/参数估计Key words
grey wolf optimizer/convergence factor/Richards model/parameter estimation分类
信息技术与安全科学引用本文复制引用
王秋萍,王梦娜,王晓峰..改进收敛因子和比例权重的灰狼优化算法[J].计算机工程与应用,2019,55(21):59-64,6.基金项目
国家自然科学基金(No.61772416). （No.61772416）
