Abstract
Previous studies proposed several bio-inspired algorithms for the optimization of Neural Network (NN) to avoid local minima and to improve accuracy and convergence speed. To advance the performance of NN, a new bio-inspired algorithm called Flower Pollination Algorithm (FPA) is used to optimize the weights and bias of NN due to its ability to explore very large search space and frequent chosen of similar solution. The FPA optimized NN (FPNN) was applied to build a model for the prediction of Dubai crude oil price unlike previous studies that mainly focus on the West Texas Intermediate and Brent crude oil price benchmarks. Results suggested that the FPNN was found to improve the convergence speed and accuracy of the cuckoo search algorithm and artificial bee colony optimized NN in the prediction of Dubai crude oil price. The Middle East region that produces a significant amount of crude oil relies on the Dubai crude oil price to benchmark prices for exporting crude oil to Asian countries. Our model could be of help to the Middle East region for monitoring possible fluctuations in the Dubai crude oil market price so as to take better decision related to international crude oil price.
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Acknowledgements
This work is supported by University of Malaya High Impact Research Grant no vote UM.C/625/HIR/MOHE/SC/13/2 from Ministry of Higher Education Malaysia.
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Chiroma, H. et al. (2019). Bio-inspired Algorithm Optimization of Neural Network for the Prediction of Dubai Crude Oil Price. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_17
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