WOA with adaptive mutation operator to estimate parameters of heavy oil thermal cracking model

Shuyue Zhang; Ning Wang

doi:10.1117/12.2539248

27 November 2019 WOA with adaptive mutation operator to estimate parameters of heavy oil thermal cracking model

Shuyue Zhang, Ning Wang

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113212L (2019) https://doi.org/10.1117/12.2539248
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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.

Citation Download Citation

Shuyue Zhang and Ning Wang "WOA with adaptive mutation operator to estimate parameters of heavy oil thermal cracking model", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212L (27 November 2019); https://doi.org/10.1117/12.2539248

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