Estimation of Middle-East oil consumption using hybrid meta-heuristic algorithms

• Main Authors: Haruna, Chiroma, Khan, Abdullah, Abubakar, Adamu, Saadi, Younes, Abdullahi Muaz, Sanah, Ya’u Gital, Abdulsalam, Shuib, Liyana
• Format: Book Chapter
• Language: English; English
• Published: Springer Nature Singapore 2019
• Subjects: Q350 Information theory
• Online Access: http://irep.iium.edu.my/74274/7/74294_Hybrid%20of%20Swarm%20Intelligent%20Algorithms%20in%20Medical%20Applications_Scopus.pdf; http://irep.iium.edu.my/74274/13/74274_Estimation%20of%20Middle-East%20oil%20consumption.pdf; http://irep.iium.edu.my/74274/; https://link.springer.com/chapter/10.1007%2F978-981-13-1799-6_16; https://doi.org/10.1007/978-981-13-1799-6

The consumption of energy has significantly increased in theworld during the preceding decade. Two-third of energy requirements are produced by oil and gas. Estimation of oil consumption can give clues on the future energy consumption. In this study, the effectiveness of three hybrid metaheuristic algorithms, namely, Cuckoo Search Neural Network (CSNN), Artificial Bee Colony Neural Network (ABCNN), and Genetic Algorithm Neural Network (GANN) were investigated for the estimation of oil consumption. The simulation results showed that the CSNN improved the estimation accuracy of oil consumption over ABCNN and GANN whereas GANN improved convergence speed over CSNN and ABCNN. The study has shown that in terms of accuracy, the CSNN is appropriate for the estimation of oil consumption. In terms of convergence speed, GANN is the most suitable algorithms