Zuwairie, Ibrahim and Kamil Zakwan, Mohd Azmi and Badaruddin, Muhammad and Mohd Falfazli, Mat Jusof and Nor Azlina, Alias and Nor Hidayati, Abdul Aziz and Mohd Ibrahim, Shapiai (2018) An oppositional learning prediction operator for simulated kalman filter. In: 3th International Conference on Computational Intelligence and Applications 2018 , 28 - 30 July 2018 , Hong Kong. pp. 1-5.. (Unpublished)
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Abstract
Simulated Kalman filter (SKF) is a recent metaheuristic optimization algorithm established in 2015. In the present study, we introduce a prediction operator in SKF to prolong its exploration and to avoid premature convergence. The proposed prediction operator is based on oppositional learning. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator outperforms the original SKF algorithm in most cases.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | SKF; Prediction; Oppositional learning |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering Faculty of Manufacturing Engineering |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 07 Dec 2018 06:54 |
| Last Modified: | 07 Dec 2018 06:54 |
| URI: | http://umpir.ump.edu.my/id/eprint/22171 |
| Download Statistic: | View Download Statistics |
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