Adaptive Cuckoo Search Algorithm for Parameter Estimation of Heavy Oil Thermal Cracking Model

被引：0
作者：
Chen, Yiping [1 ]
Wang, Ning [1 ]
机构：
[1] Zhejiang Univ, Inst Cyber Syst & Control, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
来源：
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC) | 2019年
关键词：
Adaptive cuckoo search algorithm (ACS); heavy oil thermal cracking; parameter estimation;
D O I：
10.23919/chicc.2019.8865897
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
To overcome the shortcomings of cuckoo search, an adaptive cuckoo search (ACS) algorithm is proposed. In the ACS algorithm, the logarithmic adaptive step size is adopted and the random traction component is added to the Levy flight formula to update the population. The effectiveness of the ACS algorithm is verified with four benchmark functions. The ACS algorithm is applied to estimate the parameters of the heavy oil thermal cracking model. The experimental results show that the established model has higher accuracy than the models obtained by the other optimization algorithms.
引用
收藏
页码：2679 / 2684
页数：6
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