Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems
This study introduces a new single-agent metaheuristic algorithm, named cubature Kalman optimizer (CKO). The CKO is inspired by the estimation ability of the cubature Kalman filter (CKF). In control system, the CKF algorithm is used to estimate the true value of a hidden quantity from an observation...
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| Format: | Article |
| Language: | English |
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Penerbit Akademia Baru
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40651/1/Cubature%20kalman%20optimizer_A%20novel%20metaheuristic.pdf |
| _version_ | 1811138323955056640 |
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| author | Zulkifli, Musa Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Tsuboi, Yusei |
| author_facet | Zulkifli, Musa Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Tsuboi, Yusei |
| author_sort | Zulkifli, Musa |
| collection | UMP |
| description | This study introduces a new single-agent metaheuristic algorithm, named cubature Kalman optimizer (CKO). The CKO is inspired by the estimation ability of the cubature Kalman filter (CKF). In control system, the CKF algorithm is used to estimate the true value of a hidden quantity from an observation signal that contain an uncertainty. As an optimizer, the CKO agent works as individual CKF to estimate an optimal or a near-optimal solution. The agent performs four main tasks: solution prediction, measurement prediction, and solution update phases, which are adopted from the CKF. The proposed CKO is validated on CEC 2014 test suite on 30 benchmark functions. To further validate the performance, the proposed CKO is compared with well-known algorithms, including single-agent finite impulse response optimizer (SAFIRO), single-solution simulated Kalman filter (ssSKF), simulated Kalman filter (SKF), asynchronous simulated Kalman filter (ASKF), particle swarm optimization algorithm (PSO), genetic algorithm (GA), grey wolf optimization algorithm (GWO), and black hole algorithm (BH). Friedman's test for multiple algorithm comparison with 5% of significant level shows that the CKO offers better performance than the benchmark algorithms. |
| first_indexed | 2024-09-25T03:48:22Z |
| format | Article |
| id | UMPir40651 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:48:22Z |
| publishDate | 2023 |
| publisher | Penerbit Akademia Baru |
| record_format | dspace |
| spelling | UMPir406512024-04-30T06:42:31Z http://umpir.ump.edu.my/id/eprint/40651/ Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems Zulkifli, Musa Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Tsuboi, Yusei T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures This study introduces a new single-agent metaheuristic algorithm, named cubature Kalman optimizer (CKO). The CKO is inspired by the estimation ability of the cubature Kalman filter (CKF). In control system, the CKF algorithm is used to estimate the true value of a hidden quantity from an observation signal that contain an uncertainty. As an optimizer, the CKO agent works as individual CKF to estimate an optimal or a near-optimal solution. The agent performs four main tasks: solution prediction, measurement prediction, and solution update phases, which are adopted from the CKF. The proposed CKO is validated on CEC 2014 test suite on 30 benchmark functions. To further validate the performance, the proposed CKO is compared with well-known algorithms, including single-agent finite impulse response optimizer (SAFIRO), single-solution simulated Kalman filter (ssSKF), simulated Kalman filter (SKF), asynchronous simulated Kalman filter (ASKF), particle swarm optimization algorithm (PSO), genetic algorithm (GA), grey wolf optimization algorithm (GWO), and black hole algorithm (BH). Friedman's test for multiple algorithm comparison with 5% of significant level shows that the CKO offers better performance than the benchmark algorithms. Penerbit Akademia Baru 2023 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/40651/1/Cubature%20kalman%20optimizer_A%20novel%20metaheuristic.pdf Zulkifli, Musa and Zuwairie, Ibrahim and Mohd Ibrahim, Shapiai and Tsuboi, Yusei (2023) Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems. Journal of Advanced Research in Applied Sciences and Engineering Technology, 33 (1). pp. 333-355. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.33.1.333355 https://doi.org/10.37934/araset.33.1.333355 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Zulkifli, Musa Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Tsuboi, Yusei Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems |
| title | Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems |
| title_full | Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems |
| title_fullStr | Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems |
| title_full_unstemmed | Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems |
| title_short | Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems |
| title_sort | cubature kalman optimizer a novel metaheuristic algorithm for solving numerical optimization problems |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/40651/1/Cubature%20kalman%20optimizer_A%20novel%20metaheuristic.pdf |
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