A Kalman Filter Approach for Solving Unimodal Optimization Problems
In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each age...
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| Format: | Article |
| Language: | English |
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ICIC International
2015
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19724/1/fkee-2015-zuwairie-a%20kalman%20filter%20approach%20for%20solving.pdf |
| _version_ | 1825811844072210432 |
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| author | Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad |
| author_facet | Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad |
| author_sort | Zuwairie, Ibrahim |
| collection | UMP |
| description | In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each agent in SKF acts as a Kalman Filter. Every agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. To evaluate the performance of the SKF algorithm in solving unimodal optimization problems, it is applied unimodal benchmark functions of CEC 2014 for real-parameter single objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach in solving unimodal optimization problems and has a comparable performance to some well-known metaheuristic algorithms. |
| first_indexed | 2024-03-06T12:20:21Z |
| format | Article |
| id | UMPir19724 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:20:21Z |
| publishDate | 2015 |
| publisher | ICIC International |
| record_format | dspace |
| spelling | UMPir197242018-02-02T02:32:58Z http://umpir.ump.edu.my/id/eprint/19724/ A Kalman Filter Approach for Solving Unimodal Optimization Problems Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad QA75 Electronic computers. Computer science In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, state estimation problem is regarded as an optimization problem, and each agent in SKF acts as a Kalman Filter. Every agent in the population finds solution to optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. To evaluate the performance of the SKF algorithm in solving unimodal optimization problems, it is applied unimodal benchmark functions of CEC 2014 for real-parameter single objective optimization problems. Statistical analysis is then carried out to rank SKF results to those obtained by other metaheuristic algorithms. The experimental results show that the proposed SKF algorithm is a promising approach in solving unimodal optimization problems and has a comparable performance to some well-known metaheuristic algorithms. ICIC International 2015-12 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19724/1/fkee-2015-zuwairie-a%20kalman%20filter%20approach%20for%20solving.pdf Zuwairie, Ibrahim and Nor Hidayati, Abdul Aziz and Nor Azlina, Ab. Aziz and Saifudin, Razali and Mohd Ibrahim, Shapiai and Sophan Wahyudi, Nawawi and Mohd Saberi, Mohamad (2015) A Kalman Filter Approach for Solving Unimodal Optimization Problems. ICIC Express Letters, 9 (12). pp. 3415-3422. ISSN 1881-803X. (Published) http://www.ijicic.org/el-9(12).htm |
| spellingShingle | QA75 Electronic computers. Computer science Zuwairie, Ibrahim Nor Hidayati, Abdul Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Ibrahim, Shapiai Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad A Kalman Filter Approach for Solving Unimodal Optimization Problems |
| title | A Kalman Filter Approach for Solving Unimodal Optimization Problems |
| title_full | A Kalman Filter Approach for Solving Unimodal Optimization Problems |
| title_fullStr | A Kalman Filter Approach for Solving Unimodal Optimization Problems |
| title_full_unstemmed | A Kalman Filter Approach for Solving Unimodal Optimization Problems |
| title_short | A Kalman Filter Approach for Solving Unimodal Optimization Problems |
| title_sort | kalman filter approach for solving unimodal optimization problems |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/19724/1/fkee-2015-zuwairie-a%20kalman%20filter%20approach%20for%20solving.pdf |
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