Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum
This paper presents a hybrid Manta ray foraging—particle swarm optimization algorithm. Manta Ray Foraging Optimization (MRFO) algorithm is a recent algorithm that has a promising performance as compared to other popular algorithms. On the other hand, Particle Swarm Optimization (PSO) algorithm is a...
| Main Authors: | , , , , , |
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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/34300/1/Hybrid%20manta%20ray%20foraging%E2%80%94particle%20swarm%20algorithm.pdf |
| _version_ | 1825824006946684928 |
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| author | M. F. M., Jusof S., Mohammad A. A. A., Razak N. A. M., Rizal A. N. K., Nasir M. A., Ahmad |
| author_facet | M. F. M., Jusof S., Mohammad A. A. A., Razak N. A. M., Rizal A. N. K., Nasir M. A., Ahmad |
| author_sort | M. F. M., Jusof |
| collection | UMP |
| description | This paper presents a hybrid Manta ray foraging—particle swarm optimization algorithm. Manta Ray Foraging Optimization (MRFO) algorithm is a recent algorithm that has a promising performance as compared to other popular algorithms. On the other hand, Particle Swarm Optimization (PSO) algorithm is a well-known and a good performance algorithm. The proposed hybrid algorithm in this work incorporates social interaction and elitism mechanisms from PSO into MRFO strategy. The mechanisms help search agents to determine their new search direction. The proposed algorithm is tested on various dimensions and fitness landscapes of CEC2014 benchmark functions. In solving a real world engineering problem, it is applied to optimize a PD controller for an inverted pendulum system. Result of the benchmark function test is statistically analyzed. The proposed algorithm has successfully improved the accuracy performance for most of the test functions. For optimization of the PD control, result shows that the proposed algorithm has attained a better control performance compared to MRFO |
| first_indexed | 2024-03-06T12:57:41Z |
| format | Conference or Workshop Item |
| id | UMPir34300 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:57:41Z |
| publishDate | 2022 |
| publisher | Springer, Singapore |
| record_format | dspace |
| spelling | UMPir343002022-11-11T04:08:56Z http://umpir.ump.edu.my/id/eprint/34300/ Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum M. F. M., Jusof S., Mohammad A. A. A., Razak N. A. M., Rizal A. N. K., Nasir M. A., Ahmad T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This paper presents a hybrid Manta ray foraging—particle swarm optimization algorithm. Manta Ray Foraging Optimization (MRFO) algorithm is a recent algorithm that has a promising performance as compared to other popular algorithms. On the other hand, Particle Swarm Optimization (PSO) algorithm is a well-known and a good performance algorithm. The proposed hybrid algorithm in this work incorporates social interaction and elitism mechanisms from PSO into MRFO strategy. The mechanisms help search agents to determine their new search direction. The proposed algorithm is tested on various dimensions and fitness landscapes of CEC2014 benchmark functions. In solving a real world engineering problem, it is applied to optimize a PD controller for an inverted pendulum system. Result of the benchmark function test is statistically analyzed. The proposed algorithm has successfully improved the accuracy performance for most of the test functions. For optimization of the PD control, result shows that the proposed algorithm has attained a better control performance compared to MRFO Springer, Singapore 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34300/1/Hybrid%20manta%20ray%20foraging%E2%80%94particle%20swarm%20algorithm.pdf M. F. M., Jusof and S., Mohammad and A. A. A., Razak and N. A. M., Rizal and A. N. K., Nasir and M. A., Ahmad (2022) Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum. In: Recent Trends in Mechatronics Towards Industry 4.0: Selected Articles from iM3F 2020, Malaysia , 6 August 2020 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. pp. 1-13., 730. ISBN 978-981334596-6 (Published) https://doi.org/10.1007/978-981-33-4597-3_1 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering M. F. M., Jusof S., Mohammad A. A. A., Razak N. A. M., Rizal A. N. K., Nasir M. A., Ahmad Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum |
| title | Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum |
| title_full | Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum |
| title_fullStr | Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum |
| title_full_unstemmed | Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum |
| title_short | Hybrid manta ray foraging—particle swarm algorithm for PD control optimization of an inverted pendulum |
| title_sort | hybrid manta ray foraging particle swarm algorithm for pd control optimization of an inverted pendulum |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/34300/1/Hybrid%20manta%20ray%20foraging%E2%80%94particle%20swarm%20algorithm.pdf |
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