Oppositional learning prediction operator with jumping rate for simulated kalman filter
Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this 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 lea...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/25147/1/43.%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf http://umpir.ump.edu.my/id/eprint/25147/2/43.1%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf |
| _version_ | 1796993413989531648 |
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| author | Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof |
| author_facet | Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof |
| author_sort | Badaruddin, Muhammad |
| collection | UMP |
| description | Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this 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 with jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases. |
| first_indexed | 2024-03-06T12:33:37Z |
| format | Conference or Workshop Item |
| id | UMPir25147 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:33:37Z |
| publishDate | 2019 |
| publisher | IEEE |
| record_format | dspace |
| spelling | UMPir251472022-06-15T04:05:40Z http://umpir.ump.edu.my/id/eprint/25147/ Oppositional learning prediction operator with jumping rate for simulated kalman filter Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Simulated Kalman filter (SKF) is among the new generation of metaheuristic optimization algorithm established in 2015. In this 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 with jumping rate. The results show that using CEC2014 as benchmark problems, the SKF algorithm with oppositional learning prediction operator with jumping rate outperforms the original SKF algorithm in most cases. IEEE 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25147/1/43.%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf pdf en http://umpir.ump.edu.my/id/eprint/25147/2/43.1%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf Badaruddin, Muhammad and Mohd Saberi, Mohamad and Zuwairie, Ibrahim and Kamil Zakwan, Mohd Azmi and Mohd Ibrahim, Shapiai and Mohd Falfazli, Mat Jusof (2019) Oppositional learning prediction operator with jumping rate for simulated kalman filter. In: International Conference on Computer and Information Sciences, ICCIS 2019 , 3 - 4 April 2019 , Jouf University, Aljouf, Kingdom of Saudi Arabia. pp. 1-7.. ISBN 978-153868125-1 https://doi.org/10.1109/ICCISci.2019.8716382 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Badaruddin, Muhammad Mohd Saberi, Mohamad Zuwairie, Ibrahim Kamil Zakwan, Mohd Azmi Mohd Ibrahim, Shapiai Mohd Falfazli, Mat Jusof Oppositional learning prediction operator with jumping rate for simulated kalman filter |
| title | Oppositional learning prediction operator with jumping rate for simulated kalman filter |
| title_full | Oppositional learning prediction operator with jumping rate for simulated kalman filter |
| title_fullStr | Oppositional learning prediction operator with jumping rate for simulated kalman filter |
| title_full_unstemmed | Oppositional learning prediction operator with jumping rate for simulated kalman filter |
| title_short | Oppositional learning prediction operator with jumping rate for simulated kalman filter |
| title_sort | oppositional learning prediction operator with jumping rate for simulated kalman filter |
| topic | TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/25147/1/43.%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf http://umpir.ump.edu.my/id/eprint/25147/2/43.1%20Oppositional%20learning%20prediction%20operator%20with%20jumping%20rate.pdf |
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