Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/24720/8/Enhancing%20simulated%20Kalman%20filter%20algorithm%20using%20current.pdf |
| _version_ | 1796993334471819264 |
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| author | Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz |
| author_facet | Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz |
| author_sort | Kamil Zakwan, Mohd Azmi |
| collection | UMP |
| description | Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL. The COOBL is employed after the estimation process of SKF. Experimental results over the IEEE congress on evolutionary computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all. |
| first_indexed | 2024-03-06T12:32:28Z |
| format | Article |
| id | UMPir24720 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:32:28Z |
| publishDate | 2019 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir247202019-11-21T04:38:50Z http://umpir.ump.edu.my/id/eprint/24720/ Enhancing simulated kalman filter algorithm using current optimum opposition-based learning Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL. The COOBL is employed after the estimation process of SKF. Experimental results over the IEEE congress on evolutionary computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all. Penerbit UMP 2019 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/24720/8/Enhancing%20simulated%20Kalman%20filter%20algorithm%20using%20current.pdf Kamil Zakwan, Mohd Azmi and Zuwairie, Ibrahim and Pebrianti, Dwi and Mohd Falfazli, Mat Jusof and Nor Hidayati, Abdul Aziz and Nor Azlina, Ab. Aziz (2019) Enhancing simulated kalman filter algorithm using current optimum opposition-based learning. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 1 (1). pp. 1-13. ISSN 2637-0883. (Published) http://journal.ump.edu.my/mekatronika/article/view/157 https://doi.org/10.15282/mekatronika.v1i1.157 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Kamil Zakwan, Mohd Azmi Zuwairie, Ibrahim Pebrianti, Dwi Mohd Falfazli, Mat Jusof Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Enhancing simulated kalman filter algorithm using current optimum opposition-based learning |
| title | Enhancing simulated kalman filter algorithm using current optimum opposition-based learning |
| title_full | Enhancing simulated kalman filter algorithm using current optimum opposition-based learning |
| title_fullStr | Enhancing simulated kalman filter algorithm using current optimum opposition-based learning |
| title_full_unstemmed | Enhancing simulated kalman filter algorithm using current optimum opposition-based learning |
| title_short | Enhancing simulated kalman filter algorithm using current optimum opposition-based learning |
| title_sort | enhancing simulated kalman filter algorithm using current optimum opposition based learning |
| topic | TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/24720/8/Enhancing%20simulated%20Kalman%20filter%20algorithm%20using%20current.pdf |
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