Adaptive-somersault MRFO for global optimization with an application to optimize PD control
This paper presents an Adaptive-Somersault Manta Ray Foraging Algorithm (AS-MRFO). Manta Ray Foraging Algorithm (MRFO) is a recently introduced algorithm inspired from Manta Ray Foraging strategy. MRFO is proven as a good performance optimization algorithm in finding a theoretical optima solution of...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Springer, Singapore
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34294/1/Adaptive-somersault%20mrfo%20for%20global%20optimization.pdf |
| _version_ | 1825824005685248000 |
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| author | Mohd Falfazli, Mat Jusof Ahmad Nor Kasruddin, Nasir Ahmad Azwan, Abdul Razak Nurul Amira, Mhd Rizal Mohd Ashraf, Ahmad Ikhwan Hafiz, Muhamad |
| author_facet | Mohd Falfazli, Mat Jusof Ahmad Nor Kasruddin, Nasir Ahmad Azwan, Abdul Razak Nurul Amira, Mhd Rizal Mohd Ashraf, Ahmad Ikhwan Hafiz, Muhamad |
| author_sort | Mohd Falfazli, Mat Jusof |
| collection | UMP |
| description | This paper presents an Adaptive-Somersault Manta Ray Foraging Algorithm (AS-MRFO). Manta Ray Foraging Algorithm (MRFO) is a recently introduced algorithm inspired from Manta Ray Foraging strategy. MRFO is proven as a good performance optimization algorithm in finding a theoretical optima solution of various optimization benchmark functions. It has a considerable high accuracy performance as compared with other state-of-the-art algorithms. In this work, an adaptive position update sine-based formula is adopted into the original MRFO as a strategy to improve its exploration and exploitation strategies. The proposed algorithm is tested on Evolutionary benchmark functions (CEC) to show its accuracy performance. It is also applied to optimize Proportional-Derivative (PD) control for a flexible manipulator system. Result of the performance test shows that the proposed adaptive algorithm has significantly outperformed the accuracy of the original MRFO. The application of the algorithm to optimize the PD control shows that the control scheme optimized by the proposed adaptive-somersault algorithm has attained a better control performance. |
| first_indexed | 2024-03-06T12:57:40Z |
| format | Conference or Workshop Item |
| id | UMPir34294 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:57:40Z |
| publishDate | 2022 |
| publisher | Springer, Singapore |
| record_format | dspace |
| spelling | UMPir342942022-11-11T07:28:34Z http://umpir.ump.edu.my/id/eprint/34294/ Adaptive-somersault MRFO for global optimization with an application to optimize PD control Mohd Falfazli, Mat Jusof Ahmad Nor Kasruddin, Nasir Ahmad Azwan, Abdul Razak Nurul Amira, Mhd Rizal Mohd Ashraf, Ahmad Ikhwan Hafiz, Muhamad T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This paper presents an Adaptive-Somersault Manta Ray Foraging Algorithm (AS-MRFO). Manta Ray Foraging Algorithm (MRFO) is a recently introduced algorithm inspired from Manta Ray Foraging strategy. MRFO is proven as a good performance optimization algorithm in finding a theoretical optima solution of various optimization benchmark functions. It has a considerable high accuracy performance as compared with other state-of-the-art algorithms. In this work, an adaptive position update sine-based formula is adopted into the original MRFO as a strategy to improve its exploration and exploitation strategies. The proposed algorithm is tested on Evolutionary benchmark functions (CEC) to show its accuracy performance. It is also applied to optimize Proportional-Derivative (PD) control for a flexible manipulator system. Result of the performance test shows that the proposed adaptive algorithm has significantly outperformed the accuracy of the original MRFO. The application of the algorithm to optimize the PD control shows that the control scheme optimized by the proposed adaptive-somersault algorithm has attained a better control performance. Springer, Singapore 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34294/1/Adaptive-somersault%20mrfo%20for%20global%20optimization.pdf Mohd Falfazli, Mat Jusof and Ahmad Nor Kasruddin, Nasir and Ahmad Azwan, Abdul Razak and Nurul Amira, Mhd Rizal and Mohd Ashraf, Ahmad and Ikhwan Hafiz, Muhamad (2022) Adaptive-somersault MRFO for global optimization with an application to optimize PD control. In: Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020: NUSYS’20 , 27-28 October 2020 , Virtually via the IEEE OES Malaysia Virtual/Online Conference Platform. pp. 1027-1039., 770. ISBN 978-981162405-6 (Published) https://doi.org/10.1007/978-981-16-2406-3_75 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Mohd Falfazli, Mat Jusof Ahmad Nor Kasruddin, Nasir Ahmad Azwan, Abdul Razak Nurul Amira, Mhd Rizal Mohd Ashraf, Ahmad Ikhwan Hafiz, Muhamad Adaptive-somersault MRFO for global optimization with an application to optimize PD control |
| title | Adaptive-somersault MRFO for global optimization with an application to optimize PD control |
| title_full | Adaptive-somersault MRFO for global optimization with an application to optimize PD control |
| title_fullStr | Adaptive-somersault MRFO for global optimization with an application to optimize PD control |
| title_full_unstemmed | Adaptive-somersault MRFO for global optimization with an application to optimize PD control |
| title_short | Adaptive-somersault MRFO for global optimization with an application to optimize PD control |
| title_sort | adaptive somersault mrfo for global optimization with an application to optimize pd control |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/34294/1/Adaptive-somersault%20mrfo%20for%20global%20optimization.pdf |
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