A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator
The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. Each agent in SKF is treated as a Kalman filter. To find the global optimum, the SKF employs a Kalman filter mechanism that includes prediction, measurement, and estimate. Howeve...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/33311/ http://umpir.ump.edu.my/id/eprint/33311/1/A%20Modified%20Simulated%20Kalman%20Filter%20Optimizer%20with%20State.pdf http://umpir.ump.edu.my/id/eprint/33311/2/A%20Modified%20Simulated%20Kalman%20Filter%20Optimizer%20with%20State%20Measurement.pdf |
| _version_ | 1848824223267553280 |
|---|---|
| author | Suhazri Amrin, Rahmad Zuwairie, Ibrahim Zulkifli, Md. Yusof |
| author_facet | Suhazri Amrin, Rahmad Zuwairie, Ibrahim Zulkifli, Md. Yusof |
| author_sort | Suhazri Amrin, Rahmad |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. Each agent in SKF is treated as a Kalman filter. To find the global optimum, the SKF employs a Kalman filter mechanism that includes prediction, measurement, and estimate. However, the SKF is limited to operating in the numerical search space only. Numerous techniques and modifications have been made to numerical meta-heuristic algorithms in the literature in order to enable them to operate in a discrete search space. This paper presents modifications to measurement and estimation in SKF to accommodate the discrete search space. The modified algorithm is called Discrete Simulated Kalman Filter Optimizer (DSKFO). Additionally, the DSKFO algorithm incorporates the 2-opt operator to improve the solution in solving the travelling salesman problem (TSP). The DSKFO algorithm was compared against four other combinatorial SKF algorithms and outperformed them all. |
| first_indexed | 2025-11-15T03:09:37Z |
| format | Conference or Workshop Item |
| id | ump-33311 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:09:37Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-333112022-02-07T02:46:22Z http://umpir.ump.edu.my/id/eprint/33311/ A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator Suhazri Amrin, Rahmad Zuwairie, Ibrahim Zulkifli, Md. Yusof TS Manufactures The simulated Kalman filter (SKF) is an algorithm for population-based optimization based on the Kalman filter framework. Each agent in SKF is treated as a Kalman filter. To find the global optimum, the SKF employs a Kalman filter mechanism that includes prediction, measurement, and estimate. However, the SKF is limited to operating in the numerical search space only. Numerous techniques and modifications have been made to numerical meta-heuristic algorithms in the literature in order to enable them to operate in a discrete search space. This paper presents modifications to measurement and estimation in SKF to accommodate the discrete search space. The modified algorithm is called Discrete Simulated Kalman Filter Optimizer (DSKFO). Additionally, the DSKFO algorithm incorporates the 2-opt operator to improve the solution in solving the travelling salesman problem (TSP). The DSKFO algorithm was compared against four other combinatorial SKF algorithms and outperformed them all. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33311/1/A%20Modified%20Simulated%20Kalman%20Filter%20Optimizer%20with%20State.pdf pdf en http://umpir.ump.edu.my/id/eprint/33311/2/A%20Modified%20Simulated%20Kalman%20Filter%20Optimizer%20with%20State%20Measurement.pdf Suhazri Amrin, Rahmad and Zuwairie, Ibrahim and Zulkifli, Md. Yusof (2021) A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator. In: IEEE 19th Student Conference on Research and Development (SCOReD 2021) , 23-25 November 2021 , Kota Kinabalu, Malaysia. pp. 91-95.. ISBN 978-1-6654-0193-7 (Published) https://doi.org/10.1109/SCOReD53546.2021.9652702 |
| spellingShingle | TS Manufactures Suhazri Amrin, Rahmad Zuwairie, Ibrahim Zulkifli, Md. Yusof A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator |
| title | A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator |
| title_full | A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator |
| title_fullStr | A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator |
| title_full_unstemmed | A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator |
| title_short | A Modified Simulated Kalman Filter Optimizer with State Measurement, Substitution Mutation, Hamming Distance Calculation, and 2-Opt Operator |
| title_sort | modified simulated kalman filter optimizer with state measurement, substitution mutation, hamming distance calculation, and 2-opt operator |
| topic | TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/33311/ http://umpir.ump.edu.my/id/eprint/33311/ http://umpir.ump.edu.my/id/eprint/33311/1/A%20Modified%20Simulated%20Kalman%20Filter%20Optimizer%20with%20State.pdf http://umpir.ump.edu.my/id/eprint/33311/2/A%20Modified%20Simulated%20Kalman%20Filter%20Optimizer%20with%20State%20Measurement.pdf |