Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic
In the past few years nature-inspired algorithms are experiencing rapid growth where most optimisation problems in different domains are addressed using it. As a result of this development come the issue of handling a complex optimisation problem within a short period remains very difficult. Symbiot...
| Main Authors: | , , , |
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| Format: | Article |
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The Association of Professional Researchers and Academicians
2022
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| _version_ | 1796984295507623936 |
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| author | Sa'ad, Suleiman Abdullah, Muhammed Abdullah, Azizol Ayob, Fahrul Hakim |
| author_facet | Sa'ad, Suleiman Abdullah, Muhammed Abdullah, Azizol Ayob, Fahrul Hakim |
| author_sort | Sa'ad, Suleiman |
| collection | UPM |
| description | In the past few years nature-inspired algorithms are experiencing rapid growth where most optimisation problems in different domains are addressed using it. As a result of this development come the issue of handling a complex optimisation problem within a short period remains very difficult. Symbiotic organisms search (SOS) algorithm is one of the nature-inspired metaheuristics that mimics the symbiotic association of organisms in an ecosystem. This paper proposes to investigate symbiotic organisms search algorithms used in handling various optimisation problems in different fields to bring out strengths and weaknesses of the existing algorithms as well as to point out future directions for the upcoming studies in the domain. To achieve that, studies done in optimisation problems using symbiotic organisms search from 2014 – 2020 that are obtained from some databases (Scopus, ScienceDirect, IEEE Xplore, ACM) were surveyed; where the review of various issues related to SOS such as diversity of solution search space, variants, scalability, and applications of the SOS. Finally, future research directions in the area were recommended. |
| first_indexed | 2024-03-06T11:18:20Z |
| format | Article |
| id | upm.eprints-103387 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:18:20Z |
| publishDate | 2022 |
| publisher | The Association of Professional Researchers and Academicians |
| record_format | dspace |
| spelling | upm.eprints-1033872023-06-08T07:32:03Z http://psasir.upm.edu.my/id/eprint/103387/ Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic Sa'ad, Suleiman Abdullah, Muhammed Abdullah, Azizol Ayob, Fahrul Hakim In the past few years nature-inspired algorithms are experiencing rapid growth where most optimisation problems in different domains are addressed using it. As a result of this development come the issue of handling a complex optimisation problem within a short period remains very difficult. Symbiotic organisms search (SOS) algorithm is one of the nature-inspired metaheuristics that mimics the symbiotic association of organisms in an ecosystem. This paper proposes to investigate symbiotic organisms search algorithms used in handling various optimisation problems in different fields to bring out strengths and weaknesses of the existing algorithms as well as to point out future directions for the upcoming studies in the domain. To achieve that, studies done in optimisation problems using symbiotic organisms search from 2014 – 2020 that are obtained from some databases (Scopus, ScienceDirect, IEEE Xplore, ACM) were surveyed; where the review of various issues related to SOS such as diversity of solution search space, variants, scalability, and applications of the SOS. Finally, future research directions in the area were recommended. The Association of Professional Researchers and Academicians 2022 Article PeerReviewed Sa'ad, Suleiman and Abdullah, Muhammed and Abdullah, Azizol and Ayob, Fahrul Hakim (2022) Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic. The Systematic Literature Review and Meta-Analysis Journal, 3 (1). pp. 1-8. https://slr-m.com/index.php/home/article/view/29 10.54480/slrm.v3i1.29 |
| spellingShingle | Sa'ad, Suleiman Abdullah, Muhammed Abdullah, Azizol Ayob, Fahrul Hakim Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic |
| title | Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic |
| title_full | Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic |
| title_fullStr | Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic |
| title_full_unstemmed | Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic |
| title_short | Symbiotic organisms search optimization algorithm in cloud computing: a nature-inspired meta-heuristic |
| title_sort | symbiotic organisms search optimization algorithm in cloud computing a nature inspired meta heuristic |
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