![]() ![]() Population-based bio-inspired algorithms have recently been demonstrated to perform well in solving a wide range of optimization problems. 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 8, 9, 10. Furthermore, traditional methods cannot resolve all difficult non-linear problems in an acceptable time 6, 7. Obviously, some traditional methods can be used to solve optimization problems, but they may not yield optimal results. Classical gradient-based optimization algorithms have a limited ability to solve complex optimization problems using conventional mathematical methods 3, 4, 5. The complexity of engineering optimization problems is increasing. Optimization is a process that is used to find the best inputs to maximize/minimize outputs at affordable computational cost 1, 2. 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. 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 success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. JSO can also be used in conjunction with other artificial intelligence-related techniques. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. ![]() 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. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. ![]() 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. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. ![]()
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