Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems bec...

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Bibliographic Details
Published in:Scientific reports 2022-11, Vol.12 (1), p.19157-19157, Article 19157
Main Authors: Chou, Jui-Sheng, Molla, Asmare
Format: Article
Language:English
Subjects:
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Algorithms
Animals
Artificial Intelligence
Cnidaria
Heuristics
Humanities and Social Sciences
Hybridization
multidisciplinary
Optimization algorithms
Problem Solving
Science
Science (multidisciplinary)
Scyphozoa
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Summary:The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-23121-z