Manta Ray Foraging Optimization Algorithm: Modifications and Applications
The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficien...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10124217/ |
| _version_ | 1797808497479385088 |
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| author | Mohammed Abdullahi Ibrahim Hayatu Hassan Muhammad Dalhat Abdullahi Ibrahim Aliyu Jinsul Kim |
| author_facet | Mohammed Abdullahi Ibrahim Hayatu Hassan Muhammad Dalhat Abdullahi Ibrahim Aliyu Jinsul Kim |
| author_sort | Mohammed Abdullahi |
| collection | DOAJ |
| description | The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields. |
| first_indexed | 2024-03-13T06:39:24Z |
| format | Article |
| id | doaj.art-b5259aeae25c4ef49cfd0976632cd8bd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:39:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b5259aeae25c4ef49cfd0976632cd8bd2023-06-08T23:00:33ZengIEEEIEEE Access2169-35362023-01-0111533155334310.1109/ACCESS.2023.327626410124217Manta Ray Foraging Optimization Algorithm: Modifications and ApplicationsMohammed Abdullahi0https://orcid.org/0000-0001-7844-9527Ibrahim Hayatu Hassan1https://orcid.org/0000-0001-5522-8465Muhammad Dalhat Abdullahi2Ibrahim Aliyu3https://orcid.org/0000-0002-5340-6675Jinsul Kim4https://orcid.org/0000-0002-2433-4473Department of Computer Science, Ahmadu Bello University, Zaria, NigeriaDepartment of Computer Science, Ahmadu Bello University, Zaria, NigeriaDepartment of Computer Science, Ahmadu Bello University, Zaria, NigeriaDepartment of ICT Convergence System Engineering, Chonnam National University, Gwangju, South KoreaDepartment of ICT Convergence System Engineering, Chonnam National University, Gwangju, South KoreaThe novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.https://ieeexplore.ieee.org/document/10124217/Global searchlocal searchmanta ray foraging optimization (MRFO)metaheuristicoptimization |
| spellingShingle | Mohammed Abdullahi Ibrahim Hayatu Hassan Muhammad Dalhat Abdullahi Ibrahim Aliyu Jinsul Kim Manta Ray Foraging Optimization Algorithm: Modifications and Applications IEEE Access Global search local search manta ray foraging optimization (MRFO) metaheuristic optimization |
| title | Manta Ray Foraging Optimization Algorithm: Modifications and Applications |
| title_full | Manta Ray Foraging Optimization Algorithm: Modifications and Applications |
| title_fullStr | Manta Ray Foraging Optimization Algorithm: Modifications and Applications |
| title_full_unstemmed | Manta Ray Foraging Optimization Algorithm: Modifications and Applications |
| title_short | Manta Ray Foraging Optimization Algorithm: Modifications and Applications |
| title_sort | manta ray foraging optimization algorithm modifications and applications |
| topic | Global search local search manta ray foraging optimization (MRFO) metaheuristic optimization |
| url | https://ieeexplore.ieee.org/document/10124217/ |
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