Estimation of Middle-East oil consumption using hybrid meta-heuristic algorithms
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 metaheuristi...
| Main Authors: | , , , , , , |
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| Format: | Book Chapter |
| Language: | English English |
| Published: |
Springer Nature Singapore
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/74274/ http://irep.iium.edu.my/74274/ http://irep.iium.edu.my/74274/ 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 |
| Summary: | 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 |
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