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WOA with adaptive mutation operator to estimate parameters of heavy oil thermal cracking model
Author(s): Shuyue Zhang; Ning Wang
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Paper Abstract

This paper proposes an enhanced whale optimization algorithm with adaptive mutation operator (amWOA). In amWOA, the adaptive mutation operator is designed to balance the global search and local search abilities. The population sequencing strategy is added to the mutation operator to help the algorithm jump out of the local optimum. The numerical results of three test functions show that the amWOA has better performance. The amWOA is adopted for parameter estimation of the heavy oil thermal cracking model. The simulation results show that the amWOA has the smallest modeling error.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212L (27 November 2019); doi: 10.1117/12.2539248
Show Author Affiliations
Shuyue Zhang, Zhejiang Univ. (China)
Ning Wang, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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