Opposition-based learning simulated kalman filter for Numerical optimization problems
Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. The SKF is however subjected to premature convergence problem. In this research, opposition-based learning is employed to solve the premature convergence problem in SKF. Th...
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| Format: | Research Book Profile |
| Language: | en |
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2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/36251/1/Opposition-based%20learning%20simulated%20kalman%20filter%20for%20Numerical%20optimization%20problems.pdf http://umpir.ump.edu.my/id/eprint/36251/ |
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| _version_ | 1831529052538667008 |
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| author | Mohd Falfazli, Mat Jusof |
| author_facet | Mohd Falfazli, Mat Jusof |
| author_sort | Mohd Falfazli, Mat Jusof |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. The SKF is however subjected to premature convergence problem. In this research, opposition-based learning is employed to solve the premature convergence problem in SKF. The opposition-based learning can be applied either after the solution is updated or as the prediction step in SKF. Using CEC2014 benchmark suite, it is found that the SKF with opposition-based learning outperforms the original SKF algorithm in most cases. The SKF with opposition-based learning is also applied as adaptive beamforming algorithm for adaptive array antenna. In this application, the objective is to maximize the signal to interference plus noise ratio (SINR) and results show that the SKF with opposition-based learning outperforms the existing adaptive mutated Boolean particle swarm optimization (AMBPSO) |
| format | Research Book Profile |
| id | my.ump.umpir.36251 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2016 |
| record_format | eprints |
| spelling | my.ump.umpir.362512023-01-04T02:12:31Z http://umpir.ump.edu.my/id/eprint/36251/ Opposition-based learning simulated kalman filter for Numerical optimization problems Mohd Falfazli, Mat Jusof TK Electrical engineering. Electronics Nuclear engineering Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. The SKF is however subjected to premature convergence problem. In this research, opposition-based learning is employed to solve the premature convergence problem in SKF. The opposition-based learning can be applied either after the solution is updated or as the prediction step in SKF. Using CEC2014 benchmark suite, it is found that the SKF with opposition-based learning outperforms the original SKF algorithm in most cases. The SKF with opposition-based learning is also applied as adaptive beamforming algorithm for adaptive array antenna. In this application, the objective is to maximize the signal to interference plus noise ratio (SINR) and results show that the SKF with opposition-based learning outperforms the existing adaptive mutated Boolean particle swarm optimization (AMBPSO) 2016 Research Book Profile NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36251/1/Opposition-based%20learning%20simulated%20kalman%20filter%20for%20Numerical%20optimization%20problems.pdf Mohd Falfazli, Mat Jusof (2016) Opposition-based learning simulated kalman filter for Numerical optimization problems. , [Research Book Profile: Research Report] (Unpublished) |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Falfazli, Mat Jusof Opposition-based learning simulated kalman filter for Numerical optimization problems |
| title | Opposition-based learning simulated kalman filter for Numerical optimization problems |
| title_full | Opposition-based learning simulated kalman filter for Numerical optimization problems |
| title_fullStr | Opposition-based learning simulated kalman filter for Numerical optimization problems |
| title_full_unstemmed | Opposition-based learning simulated kalman filter for Numerical optimization problems |
| title_short | Opposition-based learning simulated kalman filter for Numerical optimization problems |
| title_sort | opposition-based learning simulated kalman filter for numerical optimization problems |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/36251/1/Opposition-based%20learning%20simulated%20kalman%20filter%20for%20Numerical%20optimization%20problems.pdf http://umpir.ump.edu.my/id/eprint/36251/ |
| url_provider | http://umpir.ump.edu.my/ |