Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems
Optimization problems arise in diverse fields such as engineering, economics, and industry. Metaheuristic algorithms, including the Simulated Kalman Filter (SKF), have been developed to solve these problems. SKF, inspired by the Kalman Filter (KF) in control engineering, requires three parameters (i...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307424000111 |
| _version_ | 1826985731999924224 |
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| author | Zulkifli Musa Zuwairie Ibrahim Mohd Ibrahim Shapiai |
| author_facet | Zulkifli Musa Zuwairie Ibrahim Mohd Ibrahim Shapiai |
| author_sort | Zulkifli Musa |
| collection | DOAJ |
| description | Optimization problems arise in diverse fields such as engineering, economics, and industry. Metaheuristic algorithms, including the Simulated Kalman Filter (SKF), have been developed to solve these problems. SKF, inspired by the Kalman Filter (KF) in control engineering, requires three parameters (initial error covariance P(0), measurement noise Q, and process noise R). However, studies have yet to focus on tuning these parameters. Furthermore, no significant improvement is shown by the parameter-less SKF (with randomized P(0), Q, and R). Randomly choosing values between 0 and 1 may lead to too small values. As an estimator, KF raises concerns with excessively small Q and R values, which can introduce numerical stability issues and result in unreliable outcomes. Tuning parameters for SKF is a challenging and time-consuming task. The Multi-Agent Cubature Kalman Filter (MACKO), inspired by the Cubature Kalman filter (CKF), was introduced in this work. The nature of the Cubature Kalman filter (CKF) allows the use of small values for parameters P(0), Q, and R. In the MACKO algorithm, Cubature Transformation Techniques (CTT) are employed. CTT can use small values for parameters P(0), Q, and R, so CKF was developed to overcome KF and other estimation algorithms. Moreover, in CTT, the term local neighborhoods is used to propagate the cubature point in local search, where the radius, δ, of local search is updated in every iteration to balance between the exploration and exploitation processes. MACKO is evaluated on the CEC 2014 benchmark suite with 30 optimization problems, and its performance is compared with nine existing metaheuristic algorithms. Simulation results demonstrate that MACKO is superior, outperforming the benchmark algorithms, as indicated by Friedman's test with a 5 % significance level. |
| first_indexed | 2024-04-24T21:41:36Z |
| format | Article |
| id | doaj.art-748b165db8004c04ac324ca9d14ef815 |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2025-02-18T07:13:36Z |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-748b165db8004c04ac324ca9d14ef8152024-11-08T04:42:01ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742024-01-015140152Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problemsZulkifli Musa0Zuwairie Ibrahim1Mohd Ibrahim Shapiai2Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Malaysia; Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, MalaysiaFaculty of Manufacturing, Universiti Malaysia Pahang, Pekan 26600, Malaysia; Corresponding author.Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, MalaysiaOptimization problems arise in diverse fields such as engineering, economics, and industry. Metaheuristic algorithms, including the Simulated Kalman Filter (SKF), have been developed to solve these problems. SKF, inspired by the Kalman Filter (KF) in control engineering, requires three parameters (initial error covariance P(0), measurement noise Q, and process noise R). However, studies have yet to focus on tuning these parameters. Furthermore, no significant improvement is shown by the parameter-less SKF (with randomized P(0), Q, and R). Randomly choosing values between 0 and 1 may lead to too small values. As an estimator, KF raises concerns with excessively small Q and R values, which can introduce numerical stability issues and result in unreliable outcomes. Tuning parameters for SKF is a challenging and time-consuming task. The Multi-Agent Cubature Kalman Filter (MACKO), inspired by the Cubature Kalman filter (CKF), was introduced in this work. The nature of the Cubature Kalman filter (CKF) allows the use of small values for parameters P(0), Q, and R. In the MACKO algorithm, Cubature Transformation Techniques (CTT) are employed. CTT can use small values for parameters P(0), Q, and R, so CKF was developed to overcome KF and other estimation algorithms. Moreover, in CTT, the term local neighborhoods is used to propagate the cubature point in local search, where the radius, δ, of local search is updated in every iteration to balance between the exploration and exploitation processes. MACKO is evaluated on the CEC 2014 benchmark suite with 30 optimization problems, and its performance is compared with nine existing metaheuristic algorithms. Simulation results demonstrate that MACKO is superior, outperforming the benchmark algorithms, as indicated by Friedman's test with a 5 % significance level.http://www.sciencedirect.com/science/article/pii/S2666307424000111OptimizationMetaheuristicCKFLocal search neighborhood |
| spellingShingle | Zulkifli Musa Zuwairie Ibrahim Mohd Ibrahim Shapiai Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems International Journal of Cognitive Computing in Engineering Optimization Metaheuristic CKF Local search neighborhood |
| title | Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems |
| title_full | Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems |
| title_fullStr | Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems |
| title_full_unstemmed | Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems |
| title_short | Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems |
| title_sort | multi agent cubature kalman optimizer a novel metaheuristic algorithm for solving numerical optimization problems |
| topic | Optimization Metaheuristic CKF Local search neighborhood |
| url | http://www.sciencedirect.com/science/article/pii/S2666307424000111 |
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