Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm
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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Advanced Science Letters
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/12020/ http://umpir.ump.edu.my/id/eprint/12020/1/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm.pdf http://umpir.ump.edu.my/id/eprint/12020/7/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm-%20abstract.pdf |
| _version_ | 1848819130253180928 |
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| author | Zuwairie, Ibrahim Nor Hidayati, Abd Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Saberi, Mohamad |
| author_facet | Zuwairie, Ibrahim Nor Hidayati, Abd Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Saberi, Mohamad |
| author_sort | Zuwairie, Ibrahim |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. An 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 Simulated Kalman Filter algorithm, it is applied to 30 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, and has a comparable performance to some well-known metaheuristic algorithms. |
| first_indexed | 2025-11-15T01:48:40Z |
| format | Conference or Workshop Item |
| id | ump-12020 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T01:48:40Z |
| publishDate | 2016 |
| publisher | Advanced Science Letters |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-120202018-02-08T03:03:58Z http://umpir.ump.edu.my/id/eprint/12020/ Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm Zuwairie, Ibrahim Nor Hidayati, Abd Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Saberi, Mohamad TK Electrical engineering. Electronics Nuclear engineering 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. An 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 Simulated Kalman Filter algorithm, it is applied to 30 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, and has a comparable performance to some well-known metaheuristic algorithms. Advanced Science Letters 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/12020/1/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/12020/7/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm-%20abstract.pdf Zuwairie, Ibrahim and Nor Hidayati, Abd Aziz and Nor Azlina, Ab. Aziz and Saifudin, Razali and Mohd Saberi, Mohamad (2016) Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm. In: International Symposium of Information and Internet Technology , 26-28 January 2016 , Melaka. . (In Press / Online First) (In Press / Online First) |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Zuwairie, Ibrahim Nor Hidayati, Abd Aziz Nor Azlina, Ab. Aziz Saifudin, Razali Mohd Saberi, Mohamad Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm |
| title | Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm |
| title_full | Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm |
| title_fullStr | Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm |
| title_full_unstemmed | Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm |
| title_short | Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm |
| title_sort | simulated kalman filter: a novel estimation-based metaheuristic optimization algorithm |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/12020/ http://umpir.ump.edu.my/id/eprint/12020/1/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm.pdf http://umpir.ump.edu.my/id/eprint/12020/7/Simulated%20Kalman%20Filter-%20A%20Novel%20Estimation-based%20Metaheuristic%20Optimization%20Algorithm-%20abstract.pdf |