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Academic Journal of Computing & Information Science, 2022, 5(6); doi: 10.25236/AJCIS.2022.050605.

Fuel Consumption Prediction Based on Whale Algorithm Optimized Support Vector Machine


Andi Liu

Corresponding Author:
​Andi Liu

School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, Shandong, 255000, China


In order to improve the prediction accuracy of vehicle fuel consumption, this paper proposes a support vector machine model based on whale optimization algorithm for prediction. First, SPSS software is used to analyze the relationship between the influencing factors of fuel consumption. On this basis, the correlation between various influencing factors and fuel consumption is analyzed. Then, the whale optimization algorithm is used to optimize the penalty parameter C and the kernel parameter g in the support vector machine kernel function, and it is brought into the model for simulation. Taking the determination coefficient and mean square error as the evaluation indexes, the prediction results show that the model has high accuracy. 


correlation analysis, WOA, SVM, fuel consumption

Cite This Paper

Andi Liu. Fuel Consumption Prediction Based on Whale Algorithm Optimized Support Vector Machine. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 6: 25-30. https://doi.org/10.25236/AJCIS.2022.050605.


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