Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods
The collective behaviour of fish schools, shoals and other swarms in nature has long inspired researchers to develop solutions for optimization problems. Instinct influences the behaviour of fish to group into schools to increase safety, enhance foraging success, and promote breeding. According to t...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Netherlands
2022
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| Online Access: | https://hdl.handle.net/1721.1/141374.2 |
| _version_ | 1811081699023388672 |
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| author | Alhaqbani, Amjaad Kurdi, Heba A. Hosny, Manar |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Alhaqbani, Amjaad Kurdi, Heba A. Hosny, Manar |
| author_sort | Alhaqbani, Amjaad |
| collection | MIT |
| description | The collective behaviour of fish schools, shoals and other swarms in nature has long inspired researchers to develop solutions for optimization problems. Instinct influences the behaviour of fish to group into schools to increase safety, enhance foraging success, and promote breeding. According to these instinctive behaviours, several fish-inspired algorithms have been introduced to solve hard problems. This paper presents a comprehensive survey of fish-inspired heuristics, exploring their evolution within the context of general optimization problems. To our knowledge, this survey is the first to cover both main fish-inspired heuristics in the literature, namely, the artificial fish swarm algorithm (AFSA) and Fish school search (FSS), in addition to other algorithms inspired by specific fish species. The review covers more than 50 papers published in the Web of Science and IEEE databases since 2000. We first review the basic fish heuristics, highlighting their advantages and drawbacks, and then detail attempts in the literature to improve their behaviour to solve complex, multi-objective and high-dimensional problems in several domains. Our work is intended to provide guidance for researchers and practitioners for the purpose of further advancing research in the area of fish-inspired heuristics. We aspire to encourage their utilization in various fields for global optimization and in real-life applications. The survey findings indicate that fish-inspired heuristics are very alive in recent literature and still have great potential. Several challenges and future research directions are also identified among the findings of this survey, which can help to enhance this vibrant line of research. |
| first_indexed | 2024-09-23T11:51:04Z |
| format | Article |
| id | mit-1721.1/141374.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:51:04Z |
| publishDate | 2022 |
| publisher | Springer Netherlands |
| record_format | dspace |
| spelling | mit-1721.1/141374.22024-06-14T18:07:34Z Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods Alhaqbani, Amjaad Kurdi, Heba A. Hosny, Manar Massachusetts Institute of Technology. Department of Mechanical Engineering The collective behaviour of fish schools, shoals and other swarms in nature has long inspired researchers to develop solutions for optimization problems. Instinct influences the behaviour of fish to group into schools to increase safety, enhance foraging success, and promote breeding. According to these instinctive behaviours, several fish-inspired algorithms have been introduced to solve hard problems. This paper presents a comprehensive survey of fish-inspired heuristics, exploring their evolution within the context of general optimization problems. To our knowledge, this survey is the first to cover both main fish-inspired heuristics in the literature, namely, the artificial fish swarm algorithm (AFSA) and Fish school search (FSS), in addition to other algorithms inspired by specific fish species. The review covers more than 50 papers published in the Web of Science and IEEE databases since 2000. We first review the basic fish heuristics, highlighting their advantages and drawbacks, and then detail attempts in the literature to improve their behaviour to solve complex, multi-objective and high-dimensional problems in several domains. Our work is intended to provide guidance for researchers and practitioners for the purpose of further advancing research in the area of fish-inspired heuristics. We aspire to encourage their utilization in various fields for global optimization and in real-life applications. The survey findings indicate that fish-inspired heuristics are very alive in recent literature and still have great potential. Several challenges and future research directions are also identified among the findings of this survey, which can help to enhance this vibrant line of research. 2022-03-29T13:51:09Z 2022-03-28T12:05:58Z 2022-03-29T13:51:09Z 2022-03 2021-07 2022-03-27T03:12:10Z Article http://purl.org/eprint/type/JournalArticle 1886-1784 1134-3060 https://hdl.handle.net/1721.1/141374.2 Alhaqbani, Amjaad, Kurdi, Heba A. and Hosny, Manar. 2022. "Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods." en https://doi.org/10.1007/s11831-022-09711-0 Archives of Computational Methods in Engineering Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/octet-stream Springer Netherlands Springer Netherlands |
| spellingShingle | Alhaqbani, Amjaad Kurdi, Heba A. Hosny, Manar Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods |
| title | Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods |
| title_full | Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods |
| title_fullStr | Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods |
| title_full_unstemmed | Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods |
| title_short | Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods |
| title_sort | fish inspired heuristics a survey of the state of the art methods |
| url | https://hdl.handle.net/1721.1/141374.2 |
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