Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems
Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. S...
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| Format: | Article |
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Science Publishing Corporation
2018
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| _version_ | 1825721456073375744 |
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| author | Yusof, Zulkifli Md. Ibrahim, Zuwairie Adam, Asrul Azmi, Kamil Zakwan Mohd Ab. Rahman, Tasiransurini Muhammad, Badaruddin Ab. Aziz, Nor Azlina Abd Aziz, Nor Hidayati Mokhtar, Norrima Shapiai, Mohd Ibrahim Muhammad, Mohd Saberi |
| author_facet | Yusof, Zulkifli Md. Ibrahim, Zuwairie Adam, Asrul Azmi, Kamil Zakwan Mohd Ab. Rahman, Tasiransurini Muhammad, Badaruddin Ab. Aziz, Nor Azlina Abd Aziz, Nor Hidayati Mokhtar, Norrima Shapiai, Mohd Ibrahim Muhammad, Mohd Saberi |
| author_sort | Yusof, Zulkifli Md. |
| collection | UM |
| description | Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance evaluated SKF (DESKF). These three combinatorial SKF algorithms use binary encoding to represent the solution to a combinatorial optimization problem. This paper introduces the latest version of distance evaluated SKF which uses state encoding, instead of binary encoding, to represent the solution to a combinatorial problem. The algorithm proposed in this paper is called state-encoded distance evaluated SKF (SEDESKF) algorithm. Since the original SKF algorithm tends to converge prematurely, the distance is handled differently in this study. To control and exploration and exploitation of the SEDESKF algorithm, the distance is normalized. The performance of the SEDESKF algorithm is compared against the existing combinatorial SKF algorithm based on a set of Traveling Salesman Problem (TSP). |
| first_indexed | 2024-03-06T05:50:36Z |
| format | Article |
| id | um.eprints-20230 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:50:36Z |
| publishDate | 2018 |
| publisher | Science Publishing Corporation |
| record_format | dspace |
| spelling | um.eprints-202302019-06-27T09:06:34Z http://eprints.um.edu.my/20230/ Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems Yusof, Zulkifli Md. Ibrahim, Zuwairie Adam, Asrul Azmi, Kamil Zakwan Mohd Ab. Rahman, Tasiransurini Muhammad, Badaruddin Ab. Aziz, Nor Azlina Abd Aziz, Nor Hidayati Mokhtar, Norrima Shapiai, Mohd Ibrahim Muhammad, Mohd Saberi TK Electrical engineering. Electronics Nuclear engineering Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance evaluated SKF (DESKF). These three combinatorial SKF algorithms use binary encoding to represent the solution to a combinatorial optimization problem. This paper introduces the latest version of distance evaluated SKF which uses state encoding, instead of binary encoding, to represent the solution to a combinatorial problem. The algorithm proposed in this paper is called state-encoded distance evaluated SKF (SEDESKF) algorithm. Since the original SKF algorithm tends to converge prematurely, the distance is handled differently in this study. To control and exploration and exploitation of the SEDESKF algorithm, the distance is normalized. The performance of the SEDESKF algorithm is compared against the existing combinatorial SKF algorithm based on a set of Traveling Salesman Problem (TSP). Science Publishing Corporation 2018 Article PeerReviewed Yusof, Zulkifli Md. and Ibrahim, Zuwairie and Adam, Asrul and Azmi, Kamil Zakwan Mohd and Ab. Rahman, Tasiransurini and Muhammad, Badaruddin and Ab. Aziz, Nor Azlina and Abd Aziz, Nor Hidayati and Mokhtar, Norrima and Shapiai, Mohd Ibrahim and Muhammad, Mohd Saberi (2018) Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems. International Journal of Engineering & Technology, 7 (4). pp. 22-29. ISSN 2227-524X, https://www.sciencepubco.com/index.php/ijet/article/view/22431 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Yusof, Zulkifli Md. Ibrahim, Zuwairie Adam, Asrul Azmi, Kamil Zakwan Mohd Ab. Rahman, Tasiransurini Muhammad, Badaruddin Ab. Aziz, Nor Azlina Abd Aziz, Nor Hidayati Mokhtar, Norrima Shapiai, Mohd Ibrahim Muhammad, Mohd Saberi Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems |
| title | Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems |
| title_full | Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems |
| title_fullStr | Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems |
| title_full_unstemmed | Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems |
| title_short | Distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems |
| title_sort | distance evaluated simulated kalman filter with state encoding for combinatorial optimization problems |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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