Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control
Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray For...
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| Format: | Article |
| Language: | English |
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SAGE Publications Inc.
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41331/1/Opposition-based%20manta%20ray%20foraging%20algorithm%20for%20global%20optimization.pdf |
| _version_ | 1825815664137338880 |
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| author | Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir Nor Maniha, Abdul Ghani Mohammad Osman, Tokhi |
| author_facet | Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir Nor Maniha, Abdul Ghani Mohammad Osman, Tokhi |
| author_sort | Ahmad Azwan, Abdul Razak |
| collection | UMP |
| description | Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray Foraging Optimization (MRFO) is a promising algorithm that can be applied to optimize the control design. However, MRFO still suffers the local optima problem due to unbalance exploration-exploitation of the MRFO agents and hence limiting the performance of the desired control. In this paper, Standard, Quasi, Super, and Quasi-Reflected opposition strategies are integrated into the MRFO structure. Each strategy enhances the exploration-exploitation capability and offers different approaches of varying agent’s step size relative to the algorithm’s iteration. The proposed opposition-based MRFO (OMRFO) algorithms are applied to optimize the IT2FLC control design for a laboratory-scaled inverted pendulum system. Moreover, as the algorithms are also promising strategies to other problems, they are applied to solve 50D of 30 IEEE CEC14 benchmark functions representing problems with different features. Performance analysis of the algorithms is statistically conducted using Wilcoxon sign rank and Friedman tests. The result shows that the performance of MRFO and Quasi-Reflected-OMRFO are equal, while all other OMRFO variants show a significant improvement and better rank over the MRFO. The Super and Quasi OMRFO-IT2FLC schemes acquired the best responses for the cart and pendulum, respectively. |
| first_indexed | 2024-09-25T03:49:38Z |
| format | Article |
| id | UMPir41331 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:49:38Z |
| publishDate | 2024 |
| publisher | SAGE Publications Inc. |
| record_format | dspace |
| spelling | UMPir413312024-07-01T01:08:51Z http://umpir.ump.edu.my/id/eprint/41331/ Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir Nor Maniha, Abdul Ghani Mohammad Osman, Tokhi T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray Foraging Optimization (MRFO) is a promising algorithm that can be applied to optimize the control design. However, MRFO still suffers the local optima problem due to unbalance exploration-exploitation of the MRFO agents and hence limiting the performance of the desired control. In this paper, Standard, Quasi, Super, and Quasi-Reflected opposition strategies are integrated into the MRFO structure. Each strategy enhances the exploration-exploitation capability and offers different approaches of varying agent’s step size relative to the algorithm’s iteration. The proposed opposition-based MRFO (OMRFO) algorithms are applied to optimize the IT2FLC control design for a laboratory-scaled inverted pendulum system. Moreover, as the algorithms are also promising strategies to other problems, they are applied to solve 50D of 30 IEEE CEC14 benchmark functions representing problems with different features. Performance analysis of the algorithms is statistically conducted using Wilcoxon sign rank and Friedman tests. The result shows that the performance of MRFO and Quasi-Reflected-OMRFO are equal, while all other OMRFO variants show a significant improvement and better rank over the MRFO. The Super and Quasi OMRFO-IT2FLC schemes acquired the best responses for the cart and pendulum, respectively. SAGE Publications Inc. 2024-04-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41331/1/Opposition-based%20manta%20ray%20foraging%20algorithm%20for%20global%20optimization.pdf Ahmad Azwan, Abdul Razak and Ahmad Nor Kasruddin, Nasir and Nor Maniha, Abdul Ghani and Mohammad Osman, Tokhi (2024) Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control. Journal of Low Frequency Noise Vibration and Active Control. pp. 1-24. ISSN 1461-3484. (Published) https://doi.org/10.1177/14613484241242737 https://doi.org/10.1177/14613484241242737 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir Nor Maniha, Abdul Ghani Mohammad Osman, Tokhi Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control |
| title | Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control |
| title_full | Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control |
| title_fullStr | Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control |
| title_full_unstemmed | Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control |
| title_short | Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control |
| title_sort | opposition based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type 2 fuzzy logic control |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/41331/1/Opposition-based%20manta%20ray%20foraging%20algorithm%20for%20global%20optimization.pdf |
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